Journal of Mechanical Engineering 2013 10 by Darko Svetak

http://www.sv-jme.eu

59 (2013) 10

Since 1955

Papers

575

Elvira Džebo, Dušan Žagar, Matjaž Četina, Gregor Petkovšek: Reducing the Computational Time of the Smoothed Particle Hydrodynamics Method with a Coupled 2-D/3-D Approach

Mitja Mori, Tilen Mržljak, Boštjan Drobnič, Mihael Sekavčnik: Integral Characteristics of Hydrogen Production in Alkaline Electrolysers

595

Hongming Lv, Shaona Liu: Closed-Loop Handling Stability of 4WS Vehicle with Yaw Rate Control

604

José Salgueiro, Gabrijel Peršin, Jože Vižintin, Matic Ivanovič, Boštjan Dolenc: On-line Oil Monitoring and Diagnosis

613

Jong Boon Ooi, Xin Wang, Ying Pio Lim, ChingSeong Tan, Jee-Hou Ho, Kok-Cheong Wong: Parametric Optimization of the Output Shaft of a Portal Axle using Finite Element Analysis

620

Prabu Krishnasamy, Jancirani Jayaraj, Dennie John: Experimental Investigation on Road Vehicle Active Suspension

626

Kursad Gov, Omer Eyercioglu, Mehmed Veysel Cakir: Hardness Effects on Abrasive Flow Machining

585

Journal of Mechanical Engineering - Strojniški vestnik

Contents

10 year 2013 volume 59 no.

Strojniški vestnik Journal of Mechanical Engineering

Strojniški vestnik – Journal of Mechanical Engineering (SV-JME) Aim and Scope The international journal publishes original and (mini)review articles covering the concepts of materials science, mechanics, kinematics, thermodynamics, energy and environment, mechatronics and robotics, fluid mechanics, tribology, cybernetics, industrial engineering and structural analysis. The journal follows new trends and progress proven practice in the mechanical engineering and also in the closely related sciences as are electrical, civil and process engineering, medicine, microbiology, ecology, agriculture, transport systems, aviation, and others, thus creating a unique forum for interdisciplinary or multidisciplinary dialogue. The international conferences selected papers are welcome for publishing as a special issue of SV-JME with invited co-editor(s). Editor in Chief Vincenc Butala University of Ljubljana Faculty of Mechanical Engineering, Slovenia Technical Editor Pika Škraba University of Ljubljana Faculty of Mechanical Engineering, Slovenia Editorial Office University of Ljubljana (UL) Faculty of Mechanical Engineering SV-JME, Aškerčeva 6, SI-1000 Ljubljana, Slovenia Phone: 386-(0)1-4771 137 Fax: 386-(0)1-2518 567 E-mail: info@sv-jme.eu, http://www.sv-jme.eu Print DZS, printed in 440 copies

Founders and Publishers University of Ljubljana (UL), Faculty of Mechanical Engineering, Slovenia University of Maribor (UM), Faculty of Mechanical Engineering, Slovenia Association of Mechanical Engineers of Slovenia Chamber of Commerce and Industry of Slovenia, Metal Processing Industry Association President of Publishing Council Branko Širok, UL, Faculty of Mech. Engineering, Slovenia Vice-President of Publishing Council Jože Balič, UM, Faculty of Mech. Engineering, Slovenia

Cover: Flow into contracted reach. In channels with contraction, a typical phenomenon occurs: the reflection of a surge-wave from the constricted crosssection. The reflected wave propagates in the upstream direction. For simulating flow in complex channels, such as a channel with a contraction, mathematical models are usually needed. However, the accuracy of the used model has often been questioned. Image Courtesy: Rajar, R. (1972). Recherche théorique et expérimentale sur la propagation des ondes de rupture de barrage dans une vallée naturelle. PhD. thesis, University of Toulouse, Toulouse. (In French).

International Editorial Board Koshi Adachi, Graduate School of Engineering,Tohoku University, Japan Bikramjit Basu, Indian Institute of Technology, Kanpur, India Anton Bergant, Litostroj Power, Slovenia Franci Čuš, UM, Faculty of Mech. Engineering, Slovenia Narendra B. Dahotre, University of Tennessee, Knoxville, USA Matija Fajdiga, UL, Faculty of Mech. Engineering, Slovenia Imre Felde, Obuda University, Faculty of Informatics, Hungary Jože Flašker, UM, Faculty of Mech. Engineering, Slovenia Bernard Franković, Faculty of Engineering Rijeka, Croatia Janez Grum, UL, Faculty of Mech. Engineering, Slovenia Imre Horvath, Delft University of Technology, Netherlands Julius Kaplunov, Brunel University, West London, UK Milan Kljajin, J.J. Strossmayer University of Osijek, Croatia Janez Kopač, UL, Faculty of Mech. Engineering, Slovenia Franc Kosel, UL, Faculty of Mech. Engineering, Slovenia Thomas Lübben, University of Bremen, Germany Janez Možina, UL, Faculty of Mech. Engineering, Slovenia Miroslav Plančak, University of Novi Sad, Serbia Brian Prasad, California Institute of Technology, Pasadena, USA Bernd Sauer, University of Kaiserlautern, Germany Brane Širok, UL, Faculty of Mech. Engineering, Slovenia Leopold Škerget, UM, Faculty of Mech. Engineering, Slovenia George E. Totten, Portland State University, USA Nikos C. Tsourveloudis, Technical University of Crete, Greece Toma Udiljak, University of Zagreb, Croatia Arkady Voloshin, Lehigh University, Bethlehem, USA General information Strojniški vestnik – Journal of Mechanical Engineering is published in 11 issues per year (July and August is a double issue). Institutional prices include print & online access: institutional subscription price and foreign subscription €100,00 (the price of a single issue is €10,00); general public subscription and student subscription €50,00 (the price of a single issue is €5,00). Prices are exclusive of tax. Delivery is included in the price. The recipient is responsible for paying any import duties or taxes. Legal title passes to the customer on dispatch by our distributor. Single issues from current and recent volumes are available at the current single-issue price. To order the journal, please complete the form on our website. For submissions, subscriptions and all other information please visit: http://en.sv-jme.eu/. You can advertise on the inner and outer side of the back cover of the magazine. The authors of the published papers are invited to send photos or pictures with short explanation for cover content. We would like to thank the reviewers who have taken part in the peerreview process.

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Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10 Contents

Contents Strojniški vestnik - Journal of Mechanical Engineering volume 59, (2013), number 10 Ljubljana, October 2013 ISSN 0039-2480 Published monthly

Papers Elvira Džebo, Dušan Žagar, Matjaž Četina, Gregor Petkovšek: Reducing the Computational Time of the Smoothed Particle Hydrodynamics Method with a Coupled 2-D/3-D Approach Mitja Mori, Tilen Mržljak, Boštjan Drobnič, Mihael Sekavčnik: Integral Characteristics of Hydrogen Production in Alkaline Electrolysers Hongming Lv, Shaona Liu: Closed-Loop Handling Stability of 4WS Vehicle with Yaw Rate Control José Salgueiro, Gabrijel Peršin, Jože Vižintin, Matic Ivanovič, Boštjan Dolenc: On-line Oil Monitoring and Diagnosis Jong Boon Ooi, Xin Wang, Ying Pio Lim, ChingSeong Tan, Jee-Hou Ho, Kok-Cheong Wong: Parametric Optimization of the Output Shaft of a Portal Axle using Finite Element Analysis Prabu Krishnasamy, Jancirani Jayaraj, Dennie John: Experimental Investigation on Road Vehicle Active Suspension Kursad Gov, Omer Eyercioglu, Mehmed Veysel Cakir: Hardness Effects on Abrasive Flow Machining

575 585 595 604 613 620 626

Received for review: 2013-01-02 Received revised form: 2013-04-24 Accepted for publication: 2013-06-17

Reducing the Computational Time of the Smoothed Particle Hydrodynamics Method with a Coupled 2-D/3-D Approach Džebo, E. – Žagar, D. – Četina, M. – Petkovšek, G. Elvira Džebo1,* – Dušan Žagar1 – Matjaž Četina1 – Gregor Petkovšek2 1 University

of Ljubljana, Faculty of Civil and Geodetic Engineering, Slovenia 2 HR Wallingford, United Kingdom

Smoothed particle hydrodynamics (SPH) is a particle-based Lagrangian method that can be adapted for simulating free surface flows. The method is particularly suitable for phenomena in which the flow changes rapidly. A weakness of the SPH method is the very long computational time. However, this can be significantly reduced using new techniques. In this research, the two-dimensional (2-D) and three-dimensional (3D) models Tis Isat, developed at the University of Ljubljana, were used with an appropriate coupling procedure. The new model was validated and calibrated against the results of laboratory experiments. The simulation results of the 2-D/3-D coupled model were compared to the measurements, to the results of the fully 3-D Tis Isat model and to the results of a one-dimensional (1-D) finite difference (FD) model. The performed SPH simulations showed good agreement with measurements and the 1-D model results in the symmetry axis of the channel. The two greatest advantages of the coupled model are a more realistic description of the water-level below the expansion and the significantly shorter computational time as a result of the adopted coupling procedure. Keywords: SPH method, 2-D/3-D coupling, free-surface flow

0 INTRODUCTION Despite the fact that the Eulerian grid-based approach is commonly used for fluid flow simulations, new Lagrangian particle-based techniques have recently been developed and applied. One of the most widely used particle-based approaches to solving fluid motion equations is smoothed particle hydrodynamics (SPH). The SPH method was developed by Gingold & Monaghan [1] and Lucy [2] for solving astrophysical problems. The method has been modified to simulate the dynamics of solids [3] and [4], liquids [5] and [6], explosions [7] etc. Although in SPH water can be considered incompressible [8], a weakly compressible approach is mostly used. In this study we employed the Tis Isat model, developed at the University of Ljubljana. Tis Isat is a typical weakly compressible SPH model that introduces a non-discrete boundary condition with friction into the SPH method [9]. It has been verified against two benchmark cases [10] and [11]. The results were in a good agreement with the measurements and the results of other available models, while behaviour at the boundaries was better [12], [13] and [9]. The Tis Isat model was also verified on a real case study [14] and the results of the Tis Isat model were compared to measurements and results of a mesh-based model [15]. Probably one of the biggest challenges in the development of the SPH method is reducing the computational time. SPH models are usually coupled with other, computationally less demanding models. So far, different coupling approaches have been

proposed: a) coupling the SPH model with mesh methods [16], b) coupling SPH models of different resolutions [17]to [21] and c) coupling SPH models of different dimensionalities [22]. In this work we propose a 2-D/3-D coupling approach in order to significantly reduce the computational time. The coupled 2-D/3-D model can be used in all cases where flow transits from a 2-D to a 3-D state and vice versa. As a good example of such transient flow we simulated two channels, one with an expansion (channel A) and the other with a contraction and an expansion (channel B). 1 METHOD 1.1 SPH Method Smoothed particle hydrodynamics is a meshless particle-based computational method for simulating fluid flows. The interpolating kernel function solves Eq. (1) for any quantity A in an arbitrary point ri:

A ( ri ) ≈ ∫ A ( r )W ( r − ri ) dr. (1) In discrete form, it can be described as:

A ( ri ) = ∑ j

ρj

(

)

AjW rj − ri , (2)

where mj is the mass of the particle j, ρj is its density and Aj the value of the quantity A of the particle j. Many forms of smoothing kernels W are known. In this research the following form was used:

*Corr. Author’s Address: University of Ljubljana, Faculty of Civil and Geodetic Engineering, Jamova 2, 1000 Ljubljana, Slovenia, elvira.dzebo@fgg.uni-lj.si

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 3 2 3 3 1 − 2 q + 4 q 0 ≤ q < 1  1 1 3 1 ≤ q < 2 . (3) W (q) =  (2 − q) w 4 0 2<q   

Eq. (3) was proposed by Monaghan and Lattanzio [23], where q = l/h (h is the smoothing length and l is the distance between the particles) and w is the normalization factor (w = 7πh2/10 for 2-D simulations and w = πh3 for 3-D simulations). The general equations of fluid motion, the dynamic equation (Eq. (4)) and the equation of state (Eq. (5)) were derived by integration by parts, where gradients are replaced by kernel derivatives:   p p dvi = ∑ j   −  2i + 2j     ρi ρ j dt 

 υ ′ vij rij  +  3 rij rij

 υ ′v   eij + ij  m jWij′ + g , (4)  rij   

d ρi ′ = ∑ j vij eij m jWij , (5) dt

where:

vij = vi − v j , (6)

rij = ri − rj , (7)

vi is the velocity of particle i, ri is the position of particle i, pi is the pressure of particle i, υ' is the quotient between kinematic viscosity υ and the average density of particles i and j, eij is a directional vector, Wij' is a derivative of W with respect to l (distance between particles i and j) and g is the gravity acceleration. The treatment of boundary conditions is one of the most demanding parts of the SPH method. In particle methods, wall boundaries are usually converted into particles [24]. The Tis Isat treats solid boundaries differently; the integration approaches are described in Petkovšek et al. [9]. Furthermore, two eddy viscosity coefficients were defined in the Tis Isat model, as described in [9] and [13]: one is used for the wall-particle (bvis) and another for the particleparticle interactions (avis). The turbulent viscosity between particles is then calculated as:

υa = avis ⋅ d 2 v la , (8)

and the turbulent viscosity between the wall and particles is calculated as: 576

υb = bvis ⋅ d 2 v l b , (9)

where d is the size of the particle, v is the velocity of the particle, la is the distance between particles and lb is the distance between the boundary and the particle. 1.2 Computation of Water Depth The SPH method is particle-based and therefore the accuracy of the computed water depth depends on the particle size d. The real water level can be located anywhere between the computed value ±d/2 and therefore the particle size should be as small as possible. The Tis Isat model calculates water depth hdep using the equation: hdep = d 2 ∑W , (10) for 2-D simulations and hdep = d 3 ∑W , (11) for 3-D simulations, as described in [13], where W is the smoothing kernel and d is the particle size. Eqs. (10) and (11) are used to calculate water depth at any chosen point. Particles within a 2h distance from the observed point in the horizontal plane influence the water depth at the observed point (red/darker particles, Figs. 1a and b). The influences of the smoothing kernel (Fig. 1c) for any particle within the 2h distance from the observed point are calculated using Eq. (3). As seen from Fig. 1c, particles closer to the observed point have a greater impact on water depth. The graph curve shows the value of the smoothing kernel for a chosen particle and its influence on water depth. 1.3 2-D/3-D coupling For simulating flow in complex channels, such as a channel with a contraction and/or an expansion (Fig. 5), 3-D models should be applied. While flow phenomena near contractions or expansions cannot be satisfactorily described with any width-averaged 2-D model, such channels usually consist of relatively long straight sections, where width-average modelling is sufficient. In such cases, using the extremely time consuming fully 3-D SPH modelling is not necessary over the entire channel. The key idea of this research was to combine the 2-D and 3-D Tis Isat models and use them simultaneously in order to save on computational time. Both versions of the model can be coupled in two ways: a) coupling a 2-D model upstream with a 3-D model downstream and b) coupling a 3-D model upstream with a 2-D model downstream. When water flows into an expansion, a 2-D model is used in the narrow part of the channel. Near

Džebo, E. – Žagar, D. – Četina, M. – Petkovšek, G.

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 575-584

Fig. 1. Calculation of water depth with the 2-D Tis Isat model: a) ground plane of the channel with the observation point, b) longitudinal profile of the channel with the observation point, c) smoothing kernel influence (Eq. (3)).

Fig. 2. a) 2-D/3-D and b) 3-D/2-D coupling

Fig. 3. A surge wave is formed by reflexion from the contracted section, travelling in the upstream direction; this phenomenon usually occurs in channels with a contraction, such as channel B; the arrow denotes flow direction [11]

Fig. 4. Flow area shortly downstream of the expansion with locally supercritical flow, which terminates with an oblique hydraulic jump; this phenomenon usually occurs in channels with an expansion, such as channel A; the arrow denotes flow direction [11].

Reducing the Computational Time of the Smoothed Particle Hydrodynamics Method with a Coupled 2-D/3-D Approach

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the expansion the coupling procedure begins. When the 2-D simulation transits into a 3-D simulation, the particles at the distance cs dt above the expansion are taken into account (cs being the speed of sound and dt the time step). This is the distance at which the influence of any flow disruption travelling with the speed of sound fades in time step dt and therefore the smallest coupling length recommended in the Tis Isat model. In this area particles from the 2-D model are multiplied in rows across the channel width to be ready for the flow simulation using the 3-D model (Fig. 2a). Initially the particle velocities and pressures in the 3-D model are the same as at the last crosssection of the 2-D model. In the following time step, all particle parameters are calculated using the general 3-D equations for fluid motion (Eqs. (4) and (5)). This approach was used in channel A (Fig. 5a). The same approach can be applied for coupling an upstream 3-D model with a downstream 2-D model. The particles from the 3-D model, located within |0.5 d| from the symmetry axis of the channel (d being the particle size) and within the distance cs dt above the expansion, are transformed into particles for the 2-D model, maintaining their parameters. Tis Isat moves the centres of these particles into the symmetry axis of the channel and the transformed particles are ready for the 2-D flow simulation using the 2-D Tis Isat model (Fig. 2b). In the 2-D SPH model, solid boundaries are located at a distance ±d/2 from the centre of the channel and in the 3-D SPH model they are placed at the real distance from the symmetry axis. Critical flow occurs at the site of sudden expansion of the channel in all the experiments on the physical model [11]. Thus, at the downstream end of the 2-D SPH model an open

boundary condition was prescribed where particles can freely leave the computational domain. However, this solution is not entirely general, as there can be cases where the flow at the expansion is not critical during the whole computational time. For example, if there is an obstacle downstream from the expansion, the flow will be critical in the first moments and later a surge wave can propagate upstream even into the narrow part of the channel. In such cases the methodology should be complemented. One of the possible solutions would be to make an iterative procedure between the first section upstream of the 2-D/3-D limit and the first section downstream, calculating each region alternatively with the 2-D and the 3-D model and iteratively approaching the solution. We plan to complement the model in this manner. We are aware, however, that with such a procedure the computational time will probably increase significantly. In the case of contraction/expansion (channel B, Fig. 5b), the 2-D model was used in the part of the channel above the contraction. However, in this particular case the coupling procedure began sooner (9 m downstream from the dam), as the hydraulic phenomenon occurring upstream from the contraction (described in detail in the following section) is much better simulated using a 3-D model. 2 CASE STUDY In channels with expansion and/or contraction, two typical phenomena occur (shown in Figs. 3 and 4). The first is the reflection of a surge-wave from the constricted cross-section, which propagates in the

Table 2. Parameters of the numerical experiments Parameter Particle size

Number of particles (t = 0 s)

Coefficient used to calculate turbulent viscosity between the wall and particles

578

Abbreviation d Coupled 2-D/3-D model Tis Isat np Fully 3-D model Tis Isat Coupled 2-D/3-D model Tis Isat bvis Fully 3-D model Tis Isat

Value Case 1 0.039 m or 0.02 m 8,350 2-D particles or 33,403 2-D particles 83,663 3-D particles or 668,4873-D particles 0.001 or 0.0011 0.004 or 0.0041

Case 2

Case 3

Case 4

0.044 m

0.029 m

0.031 m

9,252 2-D particles + 46,326 3-D particles

10,968 2-D particles

7,911 2-D particles + 37,773 3-D particles

129,710 3-D particles

187,806 3-D particles

116,969 3-D particles

0.001

0.0013

0.003

0.001

0.0015

Džebo, E. – Žagar, D. – Četina, M. – Petkovšek, G.

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 575-584

upstream direction (Fig. 3). This phenomenon occurs in channels with a contraction. The second phenomenon occurs in channels with an expansion. Water depth changes rapidly from a high to a low stage. In the low-stage area the flow is supercritical and velocities are highly non-uniform. At the end of this area an oblique hydraulic jump is formed (Fig. 4). Phenomena shown in Figs. 3 and 4 also occur in channels A and B. Simulations were performed in the two channels shown in Fig. 5. In both cases the width of the accumulation was 0.4 m and the channel slope was 0.087%. The physical model and the 1-D finite

Fig. 6. Wave front propagation for a) Case 1; b) Case 2; Case 3 and d) Case 4

difference (FD) model are described in more detail in [11] and [25].

Fig. 5. a) Channel A with an expansion; and b) Channel B with a contraction and an expansion [11]

In both channels, simulations over dry and wet channel bed were performed. The four cases and the initial water depths are described in Table 1. Water depths in the accumulation were measured just above the dam and water depths in the channel just below it.

Fig. 7. Water surface profile in channel axis at a) t = 15.5 s; b) t = 27 s and c) t = 45 s after the dam break for Case 1

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Table 1. Case studies and initial parameters Case

Channel

Case 1

Channel A dry channel bed Channel Awet channel bed Channel Bdry channel bed Channel Bwet channel bed

Case 2 Case 3 Case 4

Water depth in the Water depth in the accumulation just channel just below above the dam [cm] the dam [cm] 38.9

0.0

43.7

7.0

29.2

0.0

30.7

5.8

Parameters used in the Tis Isat model are given in Table 2. The particles were 10 times smaller than the water depth in the accumulation. Good results were obtained using this particle size and the computational

time was still acceptable. Case 1 was also simulated with smaller particles (d = 2 cm; 20 times smaller than the water depth in the accumulation). The number of particles in each simulation depends on the particle size and the model: with the 3-D model the number of particles is invariable, while in the coupled model, the number of particles varies throughout the simulation. The following parameters were calibrated as described in [22]. The proposed value of the smoothing length ratio is between 1.0 and 2.0 [26] and the Tis Isat model has given good results for the value 1.0 [22]. The value of the coefficient used in the calculation of turbulent viscosity between particles avis was set to 0.01, which corresponds to the value proposed by Gesteira et al. [27]. The coefficient for calculating wall-particle turbulent viscosity was

Fig. 8. Axonometric view of the part of the channel with an expansion and water flow simulated with a) a fully 3-D model with larger particles (d = 0.039 m); b) a fully 3-D model with smaller particles (d = 0.02 m); c) a coupled 2-D/3-D model with larger particles (d = 0.39 m); and d) a coupled 2-D/3-D model with smaller particles (d = 0.02 m); Tis Isat model (t = 45 s) for Case 1 (pink and blue particles show areas with higher and lower water depths, respectively)

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calibrated by comparing the simulated results with the chosen parameter bvis to experimental measurements. Some differences between the used values bvis in the coupled 2-D/3-D and the fully 3-D model are to be expected. In the 2-D Tis Isat model the channel sidewalls are located next to the particles and this gives rise to greater friction between water particles and the wall in comparison with the 3-D model. For this reason lower values of the parameter bvis are needed in the coupled model. The best simulation results are presented herein.

3 RESULTS AND DISCUSSION The results of both models (coupled 2-D/3-D model and the fully 3-D model) were compared to the measurements. Although the 1-D mesh-based models give poorer results, the available 1-D FD model results are also shown [11]. Fig. 6 shows the wavefront propagation for all cases. 3.1 Case 1 Fig. 7 shows a comparison of the measured and simulated data 15.5, 27 and 45 s after dam failure. Compared to measurements in the channel axis, neither of the SPH models gave significantly better results than the 1-D FD model. However, SPH models can better simulate the phenomenon that occurs shortly after the expansion (Fig. 4). In Case 1, all models are in good agreement with the measurements regarding the wave-front propagation (Fig. 6a). Fig. 8 shows part of Channel A in axonometric view, and the water flow simulated with the coupled 2-D/3-D and the fully 3-D model with larger and smaller particle size at t = 45 s. Pink/lighter particles show the areas with higher water depth. Fig. 9 shows cross-sections at x = 32.22 m, x = 33.11 m and x = 34.00 m with the thickness of the slice d obtained from the coupled 2-D/3-D (d = 0.039 m and d = 0.02 m) and the fully 3-D (d = 0.039 m and d = 0.02 m) model at t = 45 s. These two figures show the ability of the 3-D Tis Isat model to simulate an area of supercritical flow and the oblique hydraulic jump that occurs after the expansion (Fig. 4). Both Figs. 8 and 9 show much better results using smaller particles. 3.2 Case 2 In Figs. 10 and 6b, the results of the simulations of the expanding flow over an initially wet channel bed are presented. Fig. 10 shows the water surface profile in the channel axis at t = 16.5 s and 35 s after the damâ&#x20AC;&#x2122;s collapse. SPH models showed good agreement with the measurements during the expanding flow immediately after the expansion (Figs. 10 and 6b). 3.3 Case 3

Fig. 9. Cross-sections at a) x = 32.22 m, b) x = 33.11 m and c) x = 34.00 m at t = 45 s simulated with the 3-D and 2-D/3-D Tis Isat models with both larger and smaller particles for Case 1; the thickness of the slice is equal to the particle size, d

Figs. 11 and 6c show the significantly more complex flow in Channel B over an initially dry channel bed. Fig. 11 shows the water surface profile 20, 30 and 58 s after the dam failure. Good results were obtained only before the contraction with all models.

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Fig. 10. Water surface profile in the channel axis at a) t = 16.5 s and b) t = 35 s (bottom) after the dam break for Case 2

Fig. 12. Water surface profile in the channel axis at a) t = 19.5 s, b) t = 26.5 s and c) t = 40 s after the dam break for Case 4

front propagation is simulated well with all the models (Fig. 6c). 3.4 Case 4

Fig. 11. Water surface profile in the channel axis at a) t = 20 s, b) t = 30 s and c) t = 58 s (bottom) after the dam break for Case 3

The 1-D FD model shows good results after the expansion, while the water level calculated by the coupled 2-D/3-D SPH model is too low and the level calculated by the fully 3-D SPH is too high. The wave 582

The last simulation was dedicated to the flow in Channel B over an initially wet channel bed. Water surface 19.5 s after the dam failure is shown in Fig. 12a. All models show good results. Fig. 12b shows the water surface 26.5 s after the dam’s collapse. In comparison to measurements, satisfactory results were achieved using SPH models. A comparison of the measured data and simulated water depth 40 s after the dam break is shown in Fig. 12c. Both SPH models show the phenomena above the contraction and below the expansion very well. The wave front propagation is simulated well with all the models (Fig. 6d). 4 DISCUSSION The results obtained by the SPH models can be divided into two groups: in the first three cases the

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results of the SPH simulations are aproximately as good as the results of the 1-D FD model. SPH models are able to accurately reproduce the hydraulic phenomena which occur before the contraction and after the expansion. Case 4, on the other hand, shows very good results. The agreement of the water levels with the measurements was much better than in other cases. Therefore, all four cases show that SPH simulations are suitable for this type of simulations. We also demonstrated that complex local hydrodynamic phenomena, such as the reflected wave propagation upstream from the contraction (Fig. 3) and the area of supercritical flow and non-uniform velocities ending in an oblique hydraulic jump after the expansion (Figs. 4 and 8) can easily be simulated with the SPH method. Furthermore, as the SPH method is not based on a computational grid, it can be used to effectively describe complex geometries and can therefore be used in highly complex computational domains. The main disadvantage of the SPH method is its high cost with regard to computational time. SPH models give more accurate results using a larger number of particles, which strongly increases the computational cost of simulations, as a search for all neighboring particles must be performed at each time step. Therefore, the development of new techniques for reducing computational time is very important in SPH. All simulations were performed on a quad-core processor. Details regarding the processor used and the required computational times are shown in Table 3. Case 1 was simulated with larger and smaller particles. By using an aproximately 2-fold smaller Table 3. Used processor, required computational time and reduction in computational time using the coupled 2-D/3-D SPH model in comparison to the fully 3-D SPH model Quad–core processor: Intel® CoreTM i7 3.33 GHz Installed memory (RAM): 6.00 GB (2.99 GB usable) Computational time [h] Case

Reduction [%]

Coupled 2-D/3-D SPH model

3-D SPH model

Case 1 (d = 0.039 m)

0.50

5.60

Case 1 (d = 0.02 m)

11.5

106

Case 2

2.25

4.00

Case 3

7.00

11.00

Case 4

8.25

12.00

particle size, computational time was increased 20fold. By using the coupled SPH model in comparison to the fully 3-D SPH model, computational time was significantly reduced in all cases (Table 3). A further decrease in computational time could be achieved in Channels A and B since it is possible to use the 2-D SPH model again in the downstream expansion part of both channels, when the flow again becomes regular and width-averaged. 5 CONCLUSIONS The simulations presented in this work were performed with a coupled 2-D/3-D and a fully 3-D SPH model. It was shown that the coupled 2-D/3-D SPH model is as good as the fully 3-D SPH model. Both SPH models can simulate the hydraulic phenomena occurring before the contraction and after the expansion. The most important advantage of using the 2-D/3-D coupling technique is the significant decrease in the computational time with no decrease in the accuracy of the results. 6 ACKNOWLEDGEMENTS The research was funded by the Ministry of Education, Science, Culture and Sport of the Republic of Slovenia, DEM Maribor, d.o.o., SENG Nova Gorica, d.o.o. and CGS plus d.o.o. Special thanks to Professor Rudi Rajar for his help with the manuscript. 7 REFERENCES [1] Gingold, R.A., Monaghan, J.J. (1977). Smoothed particle hydrodynamics. Theory and application to non-spherical stars. Monthly Notices of the Royal Astronomical Society, vol. 181, p. 375-389. [2] Lucy, L.B. (1977). A numerical approach to the testing of the fission hypothesis. Astronomical Journal, vol. 82, no. 12, p. 1013-1024, DOI:10.1086/112164. [3] Libersky, L.D., Petsheck, A.G., Carney,T.C., Hipp, J.R., Allahdadi, F.A. (1993). High strain Lagrangian hydrodynamics – a three-dimensional SPH code for dynamic material response. Journal of Computational Physics, vol. 109, no. 1, p. 67-75, DOI:10.1006/ jcph.1993.1199. [4] Liu, M.B., Liu, G.R., Lam, K.Y. (2006). Adaptive smoothed particle hydrodynamics for high strain hydrodynamics with material strength. Shock Waves, vol. 15, no. 1, p. 21-29, DOI:10.1007/s00193-0050002-1. [5] Monaghan, J.J. (1992). Smooth particle hydrodynamics. Annual Review of Astronomy and Astrophysics, vol. 30, p. 573-574, DOI:10.1146/ annurev.aa.30.090192.002551.

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[6] Vesenjak, M., Ren, Z., Müllerschön, H., Matthaei, S. (2006). Computational modelling of fuel motion and its interaction with the reservoir structure. Strojniški vestnik – Journal of Mechanical Engineering, vol. 52, no. 2, p. 85-100. [7] Liu, M.B., Liu, G.R., Zong, Z., Lam, K.Y. (2003). Computer simulation of the high explosive explosion using smoothed particle hydrodynamics methodology. Computers & Fluids, vol. 32, no. 3, p. 305-322, DOI:10.1016/S0045-7930(01)00105-0. [8] Cummins, J.S., Rudman, M. (1999). An SPH projection method. Journal of Computational Physics, vol. 152, no. 2, p. 587-607, DOI:10.1006/jcph.1999.6246. [9] Petkovšek, G., Džebo, E., Četina, M., Žagar, D. (2010). Application of non-discrete boundaries with friction to smoothed particle hydrodynamics. Strojniški vestnik Journal of Mechanical Engineering, vol. 56, no. 5, p. 307-315. [10] Martin, J.C., Moyce, W.J. (1952). An experimental study of the collapse of liquid columns on a rigid horizontal plane. Philosophical Transactions of the Royal Society of London, no. 244, p. 312-324. [11] Rajar, R. (1972). Recherche théorique et expérimentale sur la propagation des ondes de rupture de barrage dans une vallée naturelle – Theoretical and experimental research of dam-break waves in natural river valleys. PhD. thesis, University of Toulouse, Toulouse. (In French) [12] Žagar, D., Džebo, E., Četina, M., Petkovšek, G. (2008). Effects of boundary friction in SPH flow simulations. SPHERIC newsletter, no. 7, from http:// wiki.manchester.ac.uk/spheric/index.php/Newsletters, accessed on 2013-07-08. [13] Petkovšek, G., Rajar, R., Četina, M., Žagar, D. (2010). Numerical simulation of flow expansion with SPH. 1st European Division Congress IAHR. [14] Žagar, D., Džebo, E., Četina, M., Petkovšek, G. (2009). Simulations of dam-break and flow through a steep valley using SPH. 33rd IAHR Congress, p. 171-178. [15] Krzyk, M., Klasinc, R., Četina, M. (2012). Twodimensional mathematical modelling of a dam-break wave in a narrow step stream. Strojniški vestnik – Journal of Mechanical Engineering, vol. 58, no. 4, p. 255-262, DOI:10.5545/sv-jme.2010.216. [16] Narayanaswamy, M., Crespo, A.J.C., Gómez-Gesteira, M., Dalrymple, R.A. (2010). SPHysics - FUNWAVE hybrid model for coastal wave propagation. Journal of

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Hydraulic Research, vol. 48, no. 1, p. 85-93, DOI:10.10 80/00221686.2010.9641249. [17] Børve, S., Omang, M., Trulsen, J. (2001). Regularized smoothed particle hydrodynamics: a new approach to simulating magnetohydrodynamic shocks. The Astrophysical Journal, vol. 561, no. 1, p. 82-93, DOI:10.1086/323228. [18] Børve, S., Omang, M., Trulsen, J. (2005) Regularized smoothed particle hydrodynamics with improved multi-resolution handling. Journal of Computational Physics, vol. 208, no. 1. p. 345-367, DOI:10.1016/j. jcp.2005.02.018. [19] Lastiwka, M., Quinland, N., Basa, M. (2005). Adaptive particle distribution for smoothed particle hydrodynamics. International Journal for Numerical Methods in Fluids, vol. 47, no. 10-11, p. 1403-1409, DOI:10.1002/fld.891. [20] Feldman, J., Bonet, J. (2007). Dynamic refinement and boundary contact forces in SPH with applications in fluid flow problems. International Journal for Numerical Methods in Engineering, vol. 72, no. 3, p. 295-324, DOI:10.1002/nme.2010. [21] Vacondio, R., Rogers, B.D., Stansby, P.K., Mignosa, P., Feldman, J. (2013). Variable resolution for SPH: A dynamic particle coalescing and splitting scheme. Computer Methods in Applied Mechanics and Engineering, vol. 256, p. 132-148, DOI:10.1016/j. cma.2012.12.014. [22] Džebo, E., Žagar, D., Četina, M., Petkovšek, G. (2012). Simulation of dam-break flow in channel expansion with coupled 2-D/3-D SPH model. 7th International SPHERIC Workshop Proceedings, p. 403-408. [23] Monaghan, J.J., Lattanzio, J.C. (1985). A refined particle method for astrophysical problems. Astronomy and Astrophyspcs, vol. 149, no. 1, p. 135-143. [24] Monaghan, J.J. (1994). Simulating free surface flows with SPH. Journal of Computational Physics, vol. 110, no. 2, p. 399-406, DOI:10.1006/jcph.1994.1034. [25] Rajar, R. (1978). Mathematical simulation of dambreak flow. Journal of the Hydraulics Division, vol. 104, no. 7, p. 1011-1026. [26] Violeau, D. (2012). Fluid Mechanics and the SPH Method – Theory and Applications. Oxford University, Oxford, DOI:10.1093/acprof:o so/9780199655526.001.0001. [27] Gesteira, M.G., Rogers, B.D., Dalrymple, R.A., Crespo, A.J.C., Narayanaswamy, M. (2009). User’s guide for the SPHysics code. User’s Guide.

Džebo, E. – Žagar, D. – Četina, M. – Petkovšek, G.

Received for review: 2012-11-09 Received revised form: 2013-06-20 Accepted for publication: 2013-07-08

Integral Characteristics of Hydrogen Production in Alkaline Electrolysers Mori, M. – Mržljak, T. – Drobnič, B. – Sekavčnik, M. Mitja Mori* – Tilen Mržljak – Boštjan Drobnič – Mihael Sekavčnik University of Ljubljana, Faculty of Mechanical Engineering, Slovenia This paper summarises the results of experimental investigations of a commercial alkaline water electrolyser used for hydrogen production to balance decentralised electricity production from renewables. Experiments have been conducted on an alkaline water electrolyser operating at a pressure range up to 25 bar g and having a maximum production capacity of 15 Nm³ hydrogen per hour. In stationary conditions, the energy efficiency of an electrolytic cell has been calculated, and the characteristics of the electrolyser stack has been described with an empirical equation. The energy efficiency of the entire system and hydrogen losses within the boundaries of the system have been determined. Experimental results show that the energy efficiency of an electrolytic cell at typical operating conditions ranges between 73 and 83%, and the energy efficiency of the entire system is between 50 and 60%. Hydrogen losses within the boundaries of the system, compared to the total produced amount of hydrogen, are between 10 and 25%, depending of operating conditions. Keywords: advanced energy systems, renewable energy sources, alkaline water electrolysis, hydrogen production, energy characteristics

0 INTRODUCTION Renewable energy sources (RES) will play a crucial role in the world’s future energy supply. However, prior to their large-scale integration into the existing infrastructure, certain challenges have to be addressed. One of the main challenges is associated with the somewhat unpredictable and fluctuating nature of wind and solar energy sources, which can cause imbalances between the production and consumption of electrical energy in the grid, [1] and [2]. These inconsistencies in supply (due to the stochastic nature of renewable energy sources) and forecasting difficulties can be reduced to a certain extent by different (electrical) energy storage systems, [3] and [4]. Energy storage systems enable power-supply reliability and quality as well as system stability. Pumped-storage hydroelectric power plants maintain the balance between the supply and demand of electricity in the transmission network, while renewable energy sources (except large hydroelectric power plants) are usually connected to the distribution network, [4]. Energy storage based upon converting electrical energy to chemical (internal) energy of hydrogen and back is foreseen as one possible solution to this problem, [3] and [5]. Hydrogen is proposed as an energy-efficient pathway. Therefore, it is recognised as one of the energy carriers of the future, [5]. An electrolyser using electricity to generate hydrogen from water, a hydrogen storage tank and a fuel cell that recombines hydrogen with oxygen to generate electricity would be the main components of the so-called hydrogen infrastructure, which would balance the production

and consumption of electrical energy in the distribution network (Fig. 1). A demonstration laboratory for the simulation of advanced energy systems has been constructed at the hydrogen production site on the location of Šoštanj Thermal Power Plant (TEŠ). The primary aim of this project is to use technologically-advanced hydrogen production and logistics solutions in the design and demonstration of an advanced energy supply system that uses renewable energy sources and enables the optimisation of the performance of existing energy sources, [6]. This paper summarises the first stage of this project, whose main objective was to experimentally investigate the operational and energy characteristics of a commercial electrolyser and evaluate its role in advanced energy supply systems. 1 THEORETICAL PRINCIPLES OF WATER ELECTROLYSIS The splitting of water into gaseous hydrogen and oxygen by the action of electricity can be expressed as, [7]:

H 2 O ( l ) + 2 F → H 2 ( g ) + 1 / 2 O 2 ( g ) , (1)

where F is the Faraday constant representing the magnitude of electric charge per mole of electrons (96487 As/mol, [8]). Eq. (1) shows that water electrolysis is an extremely clean process, since no polluting by-products are formed. However, it should not be forgotten that a technology cannot be cleaner than the energy source used to power it, [7]. A definite advantage of electrochemical technology is its reversibility. The reverse of the above reaction (Eq. (1)) occurs in an H2-O2 fuel cell, [7]:

*Corr. Author’s Address: University of Ljubljana, Faculty of Mechanical Engineering, Aškerčeva 6, 1000 Ljubljana, Slovenia, mitja.mori@fs.uni-lj.si

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Fig. 1. The idea of hydrogen infrastructure integration into conventional power system

H 2 ( g ) + 1 / 2 O 2 ( g ) → H 2 O ( l ) + 2 F . (2)

The quantity of a substance altered at an electrode during electrolysis can be calculated according to Faraday’s (first) law of electrolysis, [9]:

m p =

dmp dt

Mp ⋅ I . (3) νe ⋅ F

The mass flow of the product (ṁp) is directly proportional to the electric current (I) running through the electrolytic cell. Other terms in the expression correspond to the Faraday constant (F), stoichiometric coefficient (required number of moles of electrons for the formation of one mole of product; oxygen: νe = 4; hydrogen: νe = 2) and the molar mass of the product (oxygen: Mp = 32.0 g mol-1; hydrogen: Mp = 2.02 g mol-1, [8]). An electrochemical system is in equilibrium when the following condition is met, [10]: 586

∆G = z ⋅ F ⋅ U rev , (4)

where ΔG represents the change in Gibbs energy, z is the number of moles of electrons transferred in the reaction (for water electrolysis z = 2) and Urev is the reversible (cell) voltage, which is the minimum voltage needed to drive the water-splitting reaction (and also the maximum amount of useful work that can be derived from the system when driving the reaction in the opposite direction; Eq. (2)), [11]. At standard conditions (T = 25 °C and p = 101.3 kPa, [8]), the reversible voltage is equal to 1.23 V (ΔG0 = 237.0 kJ mol-1, [8]). However, at this voltage (and conditions), the water-splitting reaction is endothermic; hence, for isothermal operation heat must be absorbed from the surrounding environment. The total amount of energy needed in water electrolysis is equivalent to the change in enthalpy (ΔH), which differs from ΔG by the entropic term T·ΔS, [9]:

Mori, M. – Mržljak, T. – Drobnič, B. – Sekavčnik, M.

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 585-594

∆G = ∆H − T ⋅ ∆S . (5)

The entropic term T·ΔS represents thermal irreversibility, which for a reversible process is equal to the heat demand. The voltage corresponding to the total energy demand (ΔH), the thermo-neutral (cell) voltage (Utn) is given by the equation, [10]:

∆H = z ⋅ F ⋅ U tn . (6)

At standard conditions, the thermoneutral voltage is equal to 1.48 V (ΔH0 = 285.5 kJ mol-1, [8]). When the electrolytic cell is operated above Utn, the reaction becomes exothermic, and heat must be removed from the cell for isothermal operation (Fig. 2).

is above the thermoneutral voltage. The operating or actual voltage (Uop) of an electrolytic cell can be expressed as, [7], [11]:

The overvoltage (the voltage in excess of the Urev) can be divided into three categories. The term ΣΔUel represents the sum of the anodic and cathodic overpotentials. It arises as a result of several polarisation effects, including low activity of the electrodes in the electrolyte (known as ‘activation overpotential’). The electrode overpotential increases logarithmically (Fig. 3) with current density (j) as given by the Tafel relation, [11]:

Fig. 2. Reversible and thermo-neutral voltage for water electrolysis as a function of temperature at a pressure of 1 bar

U op = U rev + ∑ ∆U el + ∆U ohm + ∆U t . (7)

∆U = a + b ⋅ ln ( j ) , (8)

where a and b are characteristic constants for the electrode system. The electrode overpotential can be minimised by selecting electrode materials with high electro-catalytic activity, and maximum realto-apparent surface area as well as by operating the process at elevated temperatures (enhances the reaction rate) and pressures. In contrast, managing the process at higher current densities, which are associated with higher production rates (Eq. (3)), increases overvoltages (Eq. (8)). The term ΔUohm represents the energy dissipation related to ohmic drops in the electrolytic cell that occur mainly at the electrodes, electrical lead wires, metalmetal joints, and inside the electrolyte. Optimisation of the cell design, i.e. minimising the distance between the electrodes and reducing the electrolyteresistance, lowers the ohmic overvoltage. An increase in operating temperature also helps to reduce ΔUohm as it decreases the electrolyte resistance, [7] and [11]. This category of overvoltage changes according to Ohm’s law (Fig. 3):

Fig. 3. Contribution of individual (over)voltage to the operating (actual) voltage of the electrolytic cell, [7]

Due to the energy losses associated with reaction kinetics as well as charge transport through electrical leads and the electrolyte, all practical devices for water electrolysis operate in a voltage regime that

∆U ohm = R ⋅ I . (9)

The last term in Eq. (7) is ΔUt, which expresses a phenomenological observation that the operating voltage applied to an electrolytic cell (at constant operating conditions; T, p and I) tends to increase with time as a consequence of performance degradation. The latter can be due to a loss of activity of electrode materials (surface wear) as well as an increase in ohmic drops (decrease in electrolyte concentration, loosening of electrical connections), [7]. Overvoltage minimisation is essential for the high efficiency operation of the electrolytic cells.

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The contribution of mass concentration overvoltage is not shown in Fig. 3, since it would become apparent only at some point outside the voltages tested here. Three process versions have been developed for water electrolysis: alkaline water electrolysis, membrane water electrolysis (also SPE water electrolysis or PEM water electrolysis) and hightemperature (steam) electrolysis, [9] and [14]. 2 EXPERIMENTS Experiments have been conducted on a commercial alkaline water electrolysis system, which was initially acquired (and designed) for the needs of the Šoštanj thermal power plant for hydrogen (cooling of electric generators) and oxygen (welding) production. The alkaline electrolyser went into operation in 2006. A programmable logic controller (PLC), a computer and a fuel cell were added to the existing water electrolysis system and hydrogen storage tank. The primary aim of the first stage of the project was to investigate operational and energy characteristics, as

well as the limitations of the water electrolyser before connecting all the elements into a system of advanced energy supply. The analysed commercial alkaline electrolyser presented in Fig. 4 operates at a pressure range up to 25 bar g and has a maximum production capacity of 15 Nm³ hydrogen per hour. The cell stack consists of a series of 90 interconnected, circular electrolysis cells (each cell has an electrode area of 0.2463 m²) arranged in a bipolar configuration. The basic parts are assembled and compressed in a unique and patented way, following the filter press system (zerogap geometry). A 30% aqueous solution of potassium hydroxide (30% KOH) is used as the electrolyte. The produced hydrogen has a nominal purity of 99.998%, and the hydrogen storage tank has a volume of 20 m³, [12]. In comparison to a typical alkaline water electrolysis system, it has two distinctive features, [12]: • demineralised water is fed to the system by gravity via an intermediate buffer (called ‘Fill Up Volume’ – Fig. 4), which is cyclically pressurised by the pressure of the system (valves are switched

Fig. 4. Simplified schematic of the analyzed commercial alkaline electrolyzer, [12]

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in a certain sequence), so there is no need for a demineralised water feed pump, and • the special design of the cell stack enables the circulation of the electrolyte without the use of an electrolyte (circulation) pump. An advantage of this configuration is that the system has lower electrical load, thereby contributing to a lower consumption of electricity. However, the operation of the Fill Up Volume buffer involves hydrogen losses, which have a negative impact on the overall energy efficiency of the system.

According to the analysis of the BPRi experiments, the duration of a single measurement point was shortened to 5 or 10 minutes. The entire experiment lasted 4 hours and 40 minutes. The data from 20 sensors were automatically recorded every 30 seconds. In the case of electricity consumption, ambient temperature and pressure and temperature in hydrogen storage tank measurement data were collected manually at the beginning and at the end of individual sets of measurements to obtain the integral value of each observed parameter.

2.1 Experimental Procedure Signals from twenty-one (21) sensors installed in the system provide data regarding temperature (cell stack, deoxo drier, cooling system, process room, electrical room), gauge pressure (hydrogen storage tank line, cooling system, gas separator), electrolyte level (oxygen and hydrogen gas separator), electrical voltage (cell stack, UPS), electric current (cell stack), electrical conductivity (feed water), volume percentage of hydrogen in oxygen (coalescing filter for oxygen) and electricity consumption (hydrogen production site). During the experiments, the ambient temperature and pressure, the temperature in hydrogen storage tank and the experimental time were also monitored. During electrolyser operation, two parameters can be varied: electric current (in our case between 180 and 400 A, where 400 A corresponds to the nominal production of hydrogen; 15 Nm³ per hour), which is proportional to the production of hydrogen (Eq. (3)) and the position of the back pressure regulator (BPR in Fig. 4), which regulates gauge pressure in the system and was between 12 and 21 bar g in our case. The operating temperature of the process (electrolyte temperature) is predefined and locked by the manufacturer. During the experiments, it varied in a range from 59 and 65 °C. Two main sets of measurements were concluded, and are presented in experimental matrix in Fig. 5. In the first set (experiments BPRi), the position of the BPR was fixed (BPR1, BPR2, BPR3 in Fig. 5) and the electric current was varied. At a single position of the BPR, four measurements at different electric currents were made. The measurement at an experimental point lasted 20 minutes and the entire experiment 5 hours. The second set of measurements (experiments pCONST) were made with a variation of both the position of the BPR and the electric current. The position of the BPR was varied to achieve constant pressure in the system (p16, p18, p20 in Fig. 5).

Fig. 5. Experimental matrix of operating conditions during experiments

3 RESULTS On the basis of experimental data and the alkaline water electrolysis system specifications and limitations, the following calculations have been performed: • energy efficiency of an electrolytic cell, ηc, • constants of the empirical I – U model, • energy efficiency of the entire system, η, and • hydrogen losses within the boundaries of the system, ξ. 3.1 Energy Efficiency of an Electrolytic Cell The energy efficiency of an electrolytic cell can be calculated as: H H2 ,c η F ⋅ m H2 ,c ⋅ H S,H2 , (10) ηc = = Pele,c U op ⋅ I where ηF is the faradaic efficiency (also known as current efficiency) defined as the ratio between the actual and theoretical maximum amount of hydrogen produced in an electrolytic cell, ṁH2,c is the theoretical mass flow of hydrogen produced in an electrolytic cell (Eq. (3)), HS,H2 is the higher heating value (HHV)

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of hydrogen, and I is the electric current. Further derivation of Eq. (10) shows that ηc can also be written as, [12]:

ηc = η F ⋅

U tn . (11) U op

Since there is no sensor for hydrogen mass flow measurement installed in the system, it was assumed that the hydrogen mass flow from an electrolytic cell is equal to ṁH2,c calculated from Eq. (3). This means that the Faraday efficiency, ηF, in our calculations is equal to 1. Experimental data from [10] show that the Faraday efficiency is close to 1 at higher current densities. These conditions were also confirmed during experiments.

where r1, r2 and r3 are parameters, in general depended on operating temperature and pressure. The ratio I / A represents current density (j): j=

I , (13) A

where A is an electrode area. The results presented in Fig. 7 and Table 1 show that the operating pressure between 16 and 20 bar g has no significant influence on the electrolyser cell stack performance. Experimental data for this analysis were collected during the pCONST experiment.

Fig. 7. I – U curves for different operating pressures Table 1. Operating conditions and values of parameters in the empirical I – U model Fig. 6. Energy efficiency of an electrolytic cell at typical operating conditions

The results from both experiments (BPRi and pCONST) show that the energy efficiency of an electrolytic cell at typical operating conditions is between 73 and 83%, where higher electric current corresponds to lower energy efficiency (Fig. 6). Because of the system and experimental setup limitations, the energy efficiency of an electrolytic cell is, in our case, equal to the energy efficiency of the cell stack, [12]. 3.2 Empirical I – U Model For the evaluation of different operating pressures on the performance of the electrolyser cell stack, the following empirical current–voltage (I – U) relationship was used, [10]:

590

U op = U rev ⋅ r1

I  I  + r2 ⋅ ln  r3 ⋅ + 1 , (12) A  A 

Operating condition p [bar g] T [°C] 16.0 61.4 18.0 62.1 20.1 62.5

r1·10-3 [Vm2/A] 0.17893 0.16484 0.17795

Parameter r2 [V] 0.10175 0.10485 0.10216

r3 [m2/A] 0.10164 0.10431 0.10235

Fig. 8 also presents results from the BPRi experiments, including experimental data in the pressure range between 15 and 21 bar g, which further confirm that the performance of the electrolyser cell stack is not influenced by the operating pressure in this (extended) pressure range. According to experimental data from [10], in addition to electric current, operating (electrolyte) temperature has a significant influence on the alkaline water electrolysis process. However, in our case, electrolyte temperature could not be changed, since it is locked by the manufacturer. Nevertheless, its influence can be evaluated indirectly from experimental data obtained at a constant electric current before the beginning of the BPRi experiment (Fig. 9).

Mori, M. – Mržljak, T. – Drobnič, B. – Sekavčnik, M.

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 585-594

Fig. 8. Energy efficiency of the cell stack at constant electric current and different operating pressures

Fig. 10. Energy efficiency and electric current as a function of time (at constant electric current)

The electrolyte temperature and consequent energy efficiency of the cell could be more constant if the difference between regulation temperatures that turn on/shut off the cooling system of the electrolyte would be smaller. Because the manufacturer locked those settings, we could not test them. Another possibility to obtain more constant electrolyte temperature is to implement a different coolant system with evaporation cooling in which temperature of the coolant is constant. Fig. 9. Operating (cell) voltage, electrolyte temperature and coolant temperature at constant electric current as a function of time

In Fig. 9, it can be observed that by operating the electrolyser at constant electric current for a longer period of time the electrolyte temperature tends to follow the sinusoidal behaviour of the coolant temperature as a result of the operating characteristics of the cooling system. Furthermore, it can be seen that operating (cell) voltage varies with the same frequency as the electrolyte temperature, but with some phase shift. The phase shift is approximately equal to π, which means that the operating voltage reaches its minimum value when the electrolyte temperature is approximately at the maximum value. This observation corresponds to the experimental data from [10] that indicates that higher operating temperatures have a favourable effect on the process of water electrolysis, since a lower operating voltage results in higher energy efficiency (Eq. (11)). The results presented in Fig. 10 confirm that the variation (sinusoidal oscillation) in voltage and consequently in the energy efficiency of an electrolytic cell is a result of the variation of electrolyte temperature and not of the electric current.

3.3 Energy Efficiency of the Entire System The energy efficiency of the entire system can be defined as:

η=

H H2 Pele

δ ⋅ m H2 ( tm ) ⋅ H S,H2 Pele

, (14)

where δ is the purity of hydrogen leaving the system, ṁH2 (tm) represents the average mass flow of hydrogen leaving the system and Pele is the electric power input of the entire system. Pele is determined as the amount of electric energy consumed during the time of the experiment:

Pele =

Wele ( tm ) − Wele ( 0 ) , (15) tm

and the average mass flow of hydrogen leaving the system is calculated according to this gas law:

m H ( tm ) = 2

 pH ( t m )

Vtank tm ⋅ RH

⋅ 2

 Ttank ( tm )

−

pH ( 0 )  2

 . (16)

Ttank ( 0 ) 

The energy efficiency of the system is calculated on the basis of the gas law for ideal gases, since there

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is no direct measurement of hydrogen mass flow in the system. On the basis of the measured data and project documentation (volume of the hydrogen storage tank), the calculation was done for a longer period of hydrogen production to minimise the error of the calculated mass flow of hydrogen leaving the system. The results of the measurements show that the (average) energy efficiency of the entire system during the BPRi measurement set was 56.5±4.2%; during the pCONST measurement set, it was 54.6±3.9% (Table 2). Differences in efficiency are in principle due to higher losses of hydrogen in the second set of measurements, which corresponds to the higher operating pressure in the system, although during this experiment the average electric current was lower (Table 2), resulting in the higher energy efficiency of an electrolytic cell. The relatively high measuring uncertainty (±4%) is due to the mass flow calculation on the basis of gas law that was (in this case) the only possibility of indirectly measuring the mass flow of hydrogen leaving the system. 3.4 Hydrogen Losses within the Boundaries of the System Because of the specific design of the analysed alkaline water electrolysis system, also presented in Section 2, a portion of the produced hydrogen is used for system conditioning, resulting in hydrogen losses in the atmosphere. The hydrogen losses (ξ) are calculated on the basis of the following equation:

ξ = 1−

δ ⋅ m H2 ( tm ) n, (17) ηF ⋅ N ⋅ m H2 ,c ( tm )

where N is the number of electrolytic cells in the cell stack. Since the average mass flow of hydrogen leaving the system is calculated according to gas law, calculations are performed for a longer period of hydrogen production.

Fig. 11. Energy flows within the system boundaries

Results show that (average) hydrogen losses within the boundaries of the system were equal to 16.1±6.5% in the BPRi experiment setup and 18.1±6.2% in the pCONST experiment setup (Table 2). Higher hydrogen losses in the pCONST experiment (up to 2%) are due to higher operating pressure in the system. At higher operating pressures, more hydrogen is needed to fill the ‘Fill Up Volume’ (Fig. 4) for conditioning the system; consequently, losses of hydrogen are higher. On the basis of performed calculations, the average energy flows within the system boundaries for both major sets of measurements (experiment BPRi and pCONST) are presented in Fig. 11. It can be seen that hydrogen losses converted into energy flow represent a relatively high value; therefore, a question arises of whether a design of the system with a demineralised water feed pump would have a more favourable impact on the energy efficiency of the entire system. 4 CONCLUSIONS The integration of systems based on renewable energy sources (mainly photovoltaics, wind and small hydropower plants) into the existing electricity infrastructure is necessarily associated with the

Table 2. Average values for operating conditions, energy efficiencies and hydrogen losses for both experiments (U* is expanded uncertainty with expansion factor k = 2)

BPRi pCONST

592

I [A] 308.3 286.0

Operating conditions p [bar g] T [°C] 16.9 63.1 17.6 61.6

η [%] 56.5 54.6

Energy efficiency U* ηc [%] ±4.2 77.24 ±3.9 78.11

Mori, M. – Mržljak, T. – Drobnič, B. – Sekavčnik, M.

H2 losses U* ±0.29 ±0.27

ξ [%] 16.1 18.1

U* ±6.5 ±6.2

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development of energy storage systems. One possible solution is to store electrical energy in the internal energy of hydrogen through the electrolysis of water. A commercial alkaline electrolyser was analysed in detail regarding the energy efficiency of an electrolytic cell, the energy efficiency of the entire system and hydrogen losses within the boundaries of the system. In addition, the parameters of an empirical model that presents electrolyser characteristics, i.e. voltage vs. electric current (current density) dependency, were determined on the basis of experimental data. The results show that: • The energy efficiency of an electrolytic cell at typical operating conditions is between 73 and 83%, where higher electric current corresponds to lower energy efficiency. • The operating pressure between 16 and 20 bar g has no significant influence on the electrolyser cell stack performance. The electrolyser characteristics also show that higher hydrogen production is always linked with higher energy losses. • In addition to electric current, operating (electrolyte) temperature has a significant influence on the alkaline water electrolysis process. The variation of temperature in the range between 59 and 65 °C at constant electric current (266 A) leads to a variation of the energy efficiency of an electrolytic cell in the range between 77 and 79%, where higher temperature corresponds to higher energy efficiency. • The calculated overall (average) energy efficiency of the system on the basis of experimental data is in the range between 50 and 60%. The results suggest that the energy efficiency of the entire system depends on the operating pressure, where higher operating pressure corresponds to higher hydrogen losses within the boundaries of the system and consequently lower energy efficiency. • During the experiments, (average) hydrogen losses within the boundaries of the system ranged between 10 and 25%. Hydrogen losses converted into energy flows represent a relatively high value (~7 kW); therefore, a question arises of whether the design of the system with a demineralised water feed pump would have a more favourable impact on the overall energy efficiency of the system. The analysed commercial alkaline electrolyser is able to adapt relatively quickly to new operating conditions even if the electric current or/and system pressure changes significantly. However, operating the electrolyser at variable operating conditions has a

negative impact on the purity of the produced gases and the energy efficiency of the entire system. 5 ACKNOWLEDGEMENTS Part of this work was carried out within the Centre of Excellence for Low-Carbon Technologies (CO NOT), Hajdrihova 19, 1000 Ljubljana, Slovenia. 6 NOMENCLATURE A electrode surface [m²] a characteristic constant b characteristic constant F Faraday constant [As/mol] G Gibbs energy [J] H enthalpy [J] HS higher heating value [kJ/kg], [kJ/kmol] Ḣ enthalpy flow [W] I electric current [A] j current density [A/m²] k expansion factor M molar mass [g/mol], [kg/kmol] ṁ mass flow [kg/s] N number of electrolytic cells P electric power [W] p pressure [Pa], [bar] R electric resistance [Ω] R gas constant [J/(kg K)] r constant in I - U empirical model S entropy [J/K] T temperature [K], [°C] t time [s], [min] U electric voltage [V] U expanded uncertainty V volume [m³] W energy [J] x̅ arithmetic mean z number of moles of electrons GREEK SYMBOLS Δ difference δ hydrogen purity η energy efficiency νe stoichiometric coefficient ξ hydrogen losses c electrolytic cell SUBSCRIPTS el electrode ele electric F Faraday H2 hydrogen

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m measurement ohm ohmic op operating p product rev reversible t time tank tank tn thermo-neutral 7 REFERENCES [1] Asmus, P. (2010). Microgrids, virtual power plants and our distributed energy future. The Electricity Journal, vol. 23, no. 10, p. 72-82, DOI:10.1016/j. tej.2010.11.001. [2] Hammons, T.J. (2008). Integrating renewable energy sources into European grids. International Journal of Electrical Power and Energy Systems, vol. 30, no. 8, p. 462–475, DOI:10.1016/j.ijepes.2008.04.010. [3] Pirc, A., Sekavčnik, M., Drobnič, B., Mori, M. (2011). Use of hydrogen technologies for saving electric energy in combination with renewable energy systems. 6th International Workshop on Deregulated Electricity Market Issues in South-Eastern Europe, Demsee. [4] Tuma, M., Sekavčnik, M. (2004): Energy Systems: Distribution of Electrical and Heat Energy. University of Ljubljana, Faculty of Mechanical Engineering, Ljubljana. [5] Sekavčnik, M., Mori, M., Grilc, V., Gajzer, M., Mešl, M., Koplan, F. (2008). SPEV - Slovenia and Transition to Hydrogen Economy - SPEV: Final Report of Research Project: October 2008: CRP Competitive Position of Slovenia 2006-2013, University of Ljubljana, Faculty of Mechanical Engineering, Ljubljana.

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[6] Center of excellence for low carbon technologies – CO NOT, from: http://www.conot.si/index.php/ razvojni-programi/102-rrp-9-demonstracijski-projekt2-vodikove-tehnologije-v-napredni-energetski-oskrbi. html, accessed at 2012-12-12. [7] Barbaro, P., Bianchini, C. (2009). Catalysis for Sustainable Energy Production. Wiley-VCH Verlag KGaA, Weinheim. [8] Lazarini, F., Brenčič, J. (2004). General and Inorganic Chemistry: University Textbook. DZS, Ljubljana. [9] Wendt, H., Kreysa, G. (1999). Electrochemical Engineering: Science and Technology in Chemical and Other Industries. Springer-Verlag, Berlin, Heidelberg, New York. [10] Ulleberg, Ø. (2003). Modeling of advanced alkaline electrolyzers: a system simulation approach. International Journal of Hydrogen Energy, vol. 28, no. 1, p. 21-33, DOI:10.1016/S0360-3199(02)00033-2. [11] Grimes, C.A., Varghese, O.K., Ranjan, S. (2008). Light, Water, Hydrogen: The Solar Generation of Hydrogen by Water Photoelectrolysis. Springer Science+ Business Media, New York. [12] Mržljak, T. (2011). Electrolyzer Within Distributed Power Generation System. Master thesis, University of Ljubljana, Faculty of Mechanical Engineering, Ljubljana. [13] Kreuter, W., Hofmann, H. (1998). Electrolysis: The important energy transformer in a world of sustainable energy. International Journal of Hydrogen Energy, vol. 23, no. 8, p. 661-666, DOI:10.1016/S03603199(97)00109-2. [14] Zeng, K., Zhang, D. (2010). Recent progress in alkaline water electrolysis for hydrogen production and applications. Progress in Energy and Combustion Science, vol. 36, no. 3, str. 307-326, DOI:10.1016/j. pecs.2009.11.002.

Mori, M. – Mržljak, T. – Drobnič, B. – Sekavčnik, M.

Received for review: 2013-03-19 Received revised form: 2013-05-20 Accepted for publication: 2013-06-20

Closed-Loop Handling Stability of 4WS Vehicle with Yaw Rate Control Lv, H.M. – Liu, S.N. Hongming Lv* – Shaona Liu

Yancheng Institute of Technology, School of Automotive Engineering, China To improve the vehicle handling stability, a four-wheel steering (4WS) controller with yaw rate tracking is proposed in this paper. A driver/ vehicle closed-loop system is built based on the preview-follower theory to evaluate the system’s manoeuvring performance. Next, the system driving on a double lane-change road and on a low adhesion road are simulated and compared with conventional front-wheel steering vehicle. The Fourier transform method is developed to analyse the dynamics of the closed-loop system in the frequency domain. The results show that the proposed 4WS vehicle has the preferred response characteristics of sideslip angle and yaw rate, and is robust regarding tire cornering stiffness, which guarantees the handling stability in emergency braking condition. Keywords: four-wheel steering, vehicle, handling stability, closed-loop system, driver, yaw rate

0 INTRODUCTION With the rapid development of global transportation, the amount of traffic accidents has been increasing year by year. Consequently, many countries have enhanced the development of automotive active safety systems. For the potential of improving vehicle handling stability, the 4WS (Four-Wheel Steering) system has attracted much attention from companies and researchers over the previous 40 years [1] and [2]. Although 4WS is one of the current focuses of vehicle dynamics control and many researchers have presented various opinions on the configuration and control strategy, automotive companies still tend to adopt the active rear steering configuration, which is simple, economic and reliable [3] to [5]. For a 4WS vehicle, the conventional zero sideslip control strategy [6] can help driver to have the correct feeling of the vehicle body movement when vehicle corners, but it decreases the steady-state gain of the vehicle yaw rate a lot at high speed, and thus leads the vehicle to excessive understeering in that it is impossible to decouple the two variables of sideslip angle and yaw rate completely with only one control variable of the rear wheel steering angle [7]. Due to the above mentioned reasons, some researchers began to incorporate 4WS into the chassis integrated control system with direct yaw-moment control (DYC) or an active suspension system (ASS) and so on [8] to [10]. Some others took the optimising the control objective in order to improve the vehicle handling performance by only using 4WS technology, which not only considered the coordination of the sideslip angle and yaw rate, but also the nonlinearity of tire and the variation of road condition [11] to [14].

In the 1970s, Sorgatz and Weir proposed a driver model to build a driver/vehicle closed-loop system to handle the problem of evaluating the handling characteristics of designed vehicle [15] and [16]. However, for most 4WS control methodologies, the emphases were put on the vehicle dynamics models and 4WS controller logic themselves, i.e. the analysis and synthesis only considered the independent system including the vehicle model and 4WS controller. Thus, there have only been a few studies taking into account the human effect of the driver in the controller design and synthesis of 4WS [17] and [18]. The objective of this paper is to investigate the design of a 4WS controller to reduce the dynamic errors of yaw rate and sideslip angle by tracking the proposed vehicle model, and to evaluate the closedloop system performance including driver and the 4WS vehicle. The paper is organised as follows. In Section 1, a 2DOF (degree-of-freedom) lateral dynamics model of 4WS vehicle is built. A control algorithm with yaw rate tracking is proposed in Section 2. In Section 3, the closed-loop system of driver and 4WS vehicle is built. The handling characteristics of the driver/vehicle system on a double lane-change road and limited road conditions are simulated and compared to those of a front-wheel steering (FWS) vehicle. A frequency analysis method is proposed to evaluate the handling performance of the vehicle/driver closed system. Finally, some conclusions are drawn in Section 4. 1 LATERAL DYNAMICS OF 4WS VEHICLE A vehicle is a very complex system, so many of parameters are usually required if a multi-body dynamics method is adopted to model it in detail. To

*Corr. Author’s Address: Yancheng Institute of Technology, 9# Xiwang Avenue, Yancheng, 224051, China, lhmyg@163.com

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minimise the complexity and difficulty of developing a vehicle dynamics control system, it is common to build a relatively simple vehicle model and thus validate the feasibility in the concept design stage. In order to study the essential vehicle dynamics and simplify the analysis procedure, the influence of roll on lateral motion is assumed to be small and not taken into account in this paper. A 2DOF model containing sideslip and yaw rate, commonly used in vehicle dynamics control, is utilised to study the handling stability of 4WS vehicle, as shown in Fig. 1 [13] and [19].

In general, lateral tire force is a non-linear function of the slip angle. In this study, the cornering stiffness for the front (rear) wheel is denoted by Cf (Cr) and its value depends on the tire-road interaction. As long as the tire slip angle is small, a linear relationship between tire force and slip angle can be justified. Then, the lateral forces generated by the front and rear tires vary linearly with their slip angles. Ff = C f α f

and

Fr = Crα r . (3)

If the sideslip angle β is small and vehicle velocity V varies slowly, αf and αr will be given by:

α f = δ f -β -

lf V

r and α r = δ r -β +

lr r. (4) V

In addition, δf and δr are generally small, cos δ f ≈ 1 and cos δ r ≈ 1. (5)

Fig. 1. 2DOF vehicle lateral dynamic model

The inertial coordinate system, (xe, ye, ze) is fixed on the ground, where the ze axis represents the direction normal to the (xe, ye) plane. This serves as a reference frame for vehicle motions. The body (chassis) coordinate system, denoted by (x, y, z) with its origin at the CG, is introduced to describe vehicle motion. Next, the chassis system, (x, y, z) is rotated a yaw angle φ with respect to the inertial system (xe, ye, ze). In Fig. 1, β and r denote the sideslip angle and yaw rate of vehicle at the CG; m is the vehicle mass；Jz is the yaw moment of inertia about its mass centre z-axis; V denotes the velocity of vehicle; lf and lr are the distances from the CG (centre of gravity) to the front and rear axles; δf and δr denote the steering angles of front and rear wheels; αf and αr are the slip angles of front and rear tires; Cf and Cr denote the lateral stiffnesses of the front and rear tires, respectively. Only considering lateral and yaw motions, the vehicle dynamic equations can be derived by applying Newton’s second law. Lateral motion:

mV ( β + r ) = Ff cos δ f + Fr cos δ r . (1)

Yaw motion, or moments about the vertical z-axis through the CG:

596

J Z r = l f Ff cos δ f − lr Fr cos δ r . (2)

Hence, the expressed by

vehicle

motion

equations

are

2(l f C f − lr Cr )   r mV ( β + r ) = −2(C f + Cr ) β − V  +2C f δ f + 2Crδ r . (6)  2 2  J r = −2(l C − l C ) β − 2(l f C f + lr Cr ) r f f r r  Z V  +2l f C f δ f − 2lr Crδ r Define the state vector x = [ β

r ] , input vector T

u = δ f δ r  and output vector y = [ β r ] , the vehicle model can be written in the state-space form as follows: T

x = Ax + Bδ f + Eδ r

y = Cx

(7)

where, the coefficient matrices are: 2(l C − l C )   2(C f + Cr ) − f f 2 r r − 1  − mV mV , A=  2(l f C f − lr Cr ) 2(l 2f C f + lr2Cr )  − −  JZ J ZV  

Lv, H.M. – Liu, S.N.

 2C f  mV B=  2C f  J  Z

  2Cr    mV  1 0   , C =  . ,E=    − 2Cr  0 1    J  Z   

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2 4WS CONTROLLER One of the main advantages of a 4WS vehicle is that there are more choices to realise the desired handling performance. Proportional control based on steady zero sideslip can improve the vehicle manoeuvrability at low speeds [20]. However, the control strategy may significantly decrease the yaw rate gain in that only one control input is used in the 2DOF system, which is not suitable for the handling of vehicle at high speed. The proposed control objective in this paper is to reduce both the harmonic peak values of yaw rate and sideslip angle as much as possible [13], viz. to improve the vehicle handling stability at high speed while avoid excessive understeer, an optimal controller for 4WS vehicle based on yaw rate tracking is designed as shown in Fig. 2.

The state-space representation of the desired reference vehicle model is given by:  xd = Ad xd + Bd δ f , (12)   yd = Cd xd where the state vector xd and output vector T as follows, xd = [ β d rd ] and yd are written T yd = [ β d rd ] . And the coefficient matrices are:  1  0  − τ β , Ad = [ Ad 1 Ad 2 ] =   1 −   0 τr  

 B  β  Bd =  d 1  =  0  ,  Bd 2   r0 

1 0  Cd =  . 0 1 

The physical interpretation of this control algorithm is that when the front wheel is steered, the yaw rate is measured and compared with an ideal yaw rate response rd. Next, the rear wheel steering angle is controlled by controller K.

The desired reference yaw rate and sideslip angle are first order time lag transfer functions of the steering angle of front wheel, i.e.

rd =

r0 , (8) 1+τrs

βd =

β0 , (9) 1+τ β s

where, τr and τβ are the time constants of yaw rate and sideslip angle respectively; r0 is the steadystate gains of yaw rate of FWS vehicle, and β0 is the corresponding steady-state gains of sideslip angle. They are derived from Eq. (6). r0 =

β β0 = δf

r δf

 =   steady 1 +

1−

V / (l f + lr ) m l f + lr

 lf l − r   2Cr 2C f

ml f 2(l f + lr )lr Cr

l m  lf − r  l f + lr  2Cr 2C f

δ r = K ⋅ (rd − r ). (13)

Fig. 2. 4WS control system

 2 V 

, (10)

lr . (11)  2 (l f + lr ) V 

In order to make the outputs of an actual vehicle to follow the outputs of desired model, the error variables are defined as eβ = βd － β, er = rd - r while T the state vector x =  β r eβ er  and output T vector y = eβ er  . Note that only er is used to control the system directly, and eβ is used to optimise the controller in the design stage. The equivalent state equations of 4WS vehicle including the controller K are obtained from Eqs. (7), (12) and (13). δ  x = Ax + B f , (14)   y = C x

where

0 A =  A A − A A d1  d

K ⋅E  , Ad 2 − K ⋅ E 

 =  0 0 1 0 .  = B , C B B − B 0 0 0 1     d 

For the above standard output feedback problem, the controller K can be designed according to optimal control theory or the H∞ control theory, etc.

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To minimise all the harmonic peak values of vehicle yaw rate and sideslip angle while not decreasing the steady-state response gain of yaw rate, a H∞ optimal controller is designed. The mathematic expression is described as, min J= : ||WS||∞ ,

(15)

where the weight W = [wβ wr]; S is the sensitivity function matrix of β and r to δf. S = eβ

T  ( sI − A) −1 B  . (16) er  = C

It is noted that the weight W is mainly dependent on the experience and experiment. However, it is also found that the values of wβ and wr are related closely to eβ and er in that the sensitivities of them to controller K are quite different. 3 CLOSED-LOOP HANDLING STABILITY OF 4WS VEHICLE 3.1 Modelling of Closed-Loop System including Driver and Vehicle Modelling the driver/vehicle closed-loop system has been a subject of research since the 1970s [15] and [16]. The driver’s use of visual information in controlling the direction of a vehicle is obviously very significant and has been investigated extensively. There is a general consensus that control occurs at two levels [21] and [22]: preview control (openloop feedforward), in which the driver anticipates the path ahead and makes an appropriate steering action based on knowledge of the vehicle dynamics; and compensatory control (closed-loop feedback), in which the driver compensates for errors in the preview control and for disturbances. The compensatory task involves the human operator controlling a system to minimise an error. Guo presented an algorithm to perform the modelling of the closed-loop driver/vehicle directional control system from a mathematical point of view, i.e. preview follower theory, which can be used to

determine the main frame and all parameters of the system. The driver/vehicle closed-loop system mode is built as shown in Fig. 3. Tp denotes the driver’s lead time constant; C0 represents the driver’s compensatory gain, and equals to the reciprocal of the steady-state lateral acceleration gain; Tc is the driver’s correction time constant; td is the driver’s cognitive time delay; Th is a first-order lag time constant which represents the driver’s neuromuscular time delay [23]. Ashkens and McRuer stated that the driver’s delay time constants of td and Th have nothing to do with the characteristics of vehicles and usually take the values about td = 0.2 s and Th = 0.l s while C0, Tp , and Tc depend on the vehicle characteristics, and driver experience [24]. On the basis of the previous investigation [23], Guo promoted the further work of driver model parameter identification by utilising a car driver simulator and presented the parameter’s reference range [25]: Tp ∈ [0.58, 2.072], Tc ∈ [0.02, 2.76], td ∈ [0.17, 0.53], Th ∈ [0.08, 0.36]. The vehicle model in Fig. 3 is rebuilt by defining new state variables [β r φ y]T and output variables [β r y]T while δf and δr are control variables. The yaw rate r is the differential of yaw angle φ.

According to the vehicle dynamic characteristic shown in Fig. 1, the vehicle lateral velocity y can be deduced as, y = V sin( β + ϕ ) ≈ V ( β + ϕ ). (18) The new matrixes A, B, C and E of state equations are rewritten as following,  2(C f + Cr )  − mV   2(l f C f − lr Cr ) A = − JZ   0  V 

Fig. 3. Closed-loop system including driver and vehicle

598

r = ϕ. (17)

Lv, H.M. – Liu, S.N.

2(l f C f − lr Cr )

−1 0 mV 2 2(l 2 C + l 2C ) 0 − f f r r J ZV

−

1 0

0 V

 0   0 ,  0  0 

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 595-603

 2C f   mV  2C f B=  JZ  0   0

  2Cr    mV     1 0 0 0    − 2Cr     , C = 0 1 0 0 , E =  J  . Z    0 0 0 1    0    0    

The test parameters of a Buick LeSabre, a fullsize sedan, are used to build the vehicle model [26] while the driver handling parameter ranges presented by Guo [23] and [25] are referred and synthesised to build the driver model. The key parameters of driver/ vehicle system are summarised in Table 1.

Fig. 4. Trajectories of road, 4WS and FWS vehicle motion

Table 1. Parameters of the driver/vehicle system Parameter Vehicle mass (m) Yaw inertia (Jz)

Value 1740 kg 3214 kg·m2

Front axle to CG (lf)

1.058 m

Rear axle to CG (lr)

1.756 m

Front tire cornering stiffness (Cf)

29000 N/rad

Rear tire cornering stiffness (Cr)

60000 N/rad

Reference yaw rate time lag (τr)

0.1 s

Reference sideslip angle time lag (τβ)

0.1 s

Driver’s lead time (Tp) Driver’s compensatory gain (C0)

Fig. 5. Steering angles of front wheel

1s 1.7 s2/m

Driver’s correction time (Tc)

0.2 s

Driver’s cognitive time delay (td)

0.28 s

Driver’s neuromuscular time delay (Th)

0.1 s

3.2 Simulation of Double Lane-Change Manoeuvre A double lane-change manoeuvre is a typical driving condition and commonly used to estimate vehicle handling performance. In this paper, it is adopted to simulate the lateral dynamics for both the 4WS and FWS vehicles at a speed of 30 m/s, see Fig. 4. The behaviour of front wheel steering angles of them during the manoeuvring process is given as shown in Fig. 5. The trajectories of both vehicles are similar to the actual steering process, which proves the validity of the simulation. The steering angles of them are also very similar, which shows the simulation results of FWS and 4WS vehicles have comparability because they are at the same input condition. Their sideslip angle and yaw rate responses on a double lane-change road are compared in Fig. 6. When a 4WS vehicle is cornering, the maximum

Fig. 6. Responses of vehicle state variables on double lanechange road; a) Sideslip angle, b) Yaw rate

Closed-Loop Handling Stability of 4WS Vehicle with Yaw Rate Control

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sideslip angle, compared to FWS vehicle, is obviously reduced while the maximum yaw rate is decreased to a certain extent. The relationships of β and r to δf of both 4WS and FWS vehicles at a speed of 30 m/s are compared in Fig.7. The response curves of 4WS and FWS are similar to each other, but the maximum values of sideslip angle and yaw rate of 4WS vehicle are smaller than those of FWS vehicle, as the same in Fig. 6. Therefore, the time responses indicate that 4WS can improve the vehicle’s dynamic handling behaviour to make the vehicle easier to drive at high speeds.

b) Fig. 7. Relationship of β and r to δf on double lane-change road; a) β - δf , b) r - δf

3.3 Analysis of Closed-Loop System in the Frequency Domain Though a double lane-change road is usually described its length in space, it can also be converted by time if the road length is divided by vehicle velocity. Thus, for a closed-loop system including driver and vehicle, the road is the time input signal while β and r are the outputs. It is known that the system transfer 600

function is so complex that it cannot be written in a simple format. There is a direct way to obtain the system transfer function: the Fourier Transform method is used to the input and output variables in the simulation. ∞

Y (ω ) F [ y (t )] H ( jω ) = = = X (ω ) F [ x(t )]

∫ y(t )e

− jωt

∫ x(t )e

− jωt

0 ∞

dt . (19) dt

Here, H(jω) is the system transform function; X(ω) and Y(ω) are the input and output expressed by frequency; x(t) and y(t) are the input and output by time, respectively. Based on the above theory, the closed-loop dynamic characteristics in the frequency domain can be obtained by FFT (Fast Fourier Transform). The dynamic characteristics of closed-loop system in frequency domain are depicted in Figs. 8 and 9. The figures show that the resonances of driver/ vehicle closed-loop system mainly lie in the middle frequency range between 1~20 rad/s. The important natural frequencies, caused by vehicle and driver characteristics, are 1.5, 3, 4.5, 6.3, 8 and 9.4 rad/s, individually. They also indicate that the proposed 4WS controller is equivalent to a mid-pass filter for the driver/ vehicle closed-loop system. Compared to the FWS vehicle, the sideslip angle resonance peak values of the 4WS vehicle are decreased distinctly. The second resonance peak value is decreased is from 0.88 to 0.7 deg, the third is from 0.85 to 0.55 deg, and the fourth decreases especially, from 1.1 to 0.57 deg, respectively. The yaw rate resonance peak values of 4WS vehicle are also decreased. The third peak value is decreased from 0.17 to 0.15 rad/s while the fourth is from 0.25 to 0.18 rad/s. In fact, the closed-loop handling stability evaluation is still developing and not effectively applied for the variation of driver’s subjective properties. It is meaningful to evaluate the handling stability of a developing vehicle by utilising the resonance frequency analysis method to the driver/ vehicle closed-loop system. 3.4 Simulation of Emergency Braking Condition Many traffic accidents occur in condition of emergency braking, which lead to the vertical loads of front and rear tires changing greatly. According to the lateral properties of tires, the equivalent

Lv, H.M. – Liu, S.N.

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 595-603

Fig. 8. Closed-loop dynamic characteristics of sideslip angle; a) FWS vehicle, b) 4WS vehicle

Fig. 9. Closed-loop dynamic characteristics of yaw rate; a) FWS vehicle, b) 4WS vehicle

b) Fig. 10. Relationship of β and r to δf in emergency braking condition; a) β - δf , b) r - δf

cornering stiffness of the front tire increases while the rear decreases, which reduces the vehicle’s natural frequency and increases the response peak value of

each state variable. Thus the handling stability of the vehicle is deteriorated, and the vehicle may be out of control.

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To simplify the study, it is assumed that the cornering stiffness of rear tire decreases by 50% [8]. The closed-loop systems of 4WS and FWS vehicles on a double lane-change road are simulated and the relationships of state variables are shown in Fig. 10. When both types of vehicle are steered in such emergency braking condition, all the responses of FWS vehicle exceed the stable region while the 4WS vehicle still has the original characteristics, which shows that the 4WS vehicle is robust to the variation of tire cornering stiffness, and thus guarantees preferable handling stability. 4 CONCLUSIONS According to the lateral dynamics simulation of a driver/vehicle closed-loop system on a double lanechange road, the 4WS vehicle with yaw rate tracking control has the following advantages. It can track the referenced sideslip angle and yaw rate, and thus improves vehicle handling stability. It has a preferable robustness to tire cornering stiffness. The vehicle can remain stable even in emergency braking condition. Though the resonance frequency analysis of driver/vehicle closed-loop system is still in development, it may be an effective method to study the handling stability of vehicle. 5 ACKNOWLEDGEMENT This work was supported in part by the National Science Foundation of China (Grant No. 50575041). 6 REFERENCES [1] Pasternack, S. (1973). Some modern control aspects of automatically steered vehicles. ASME Paper, no. 73-ICT-54. [2] Tadahiko, T., Noritaka, Y., Shigeki, F. (1986). Improvement of vehicle dynamics by vehicle-speedsensing four-wheel steering system. SAE Paper, no. 860624, DOI:10.4271/860624. [3] Fujita, K., Ohashi, K., Fukatani, K. (1998). Development of active rear steer system applying H∞-μ synthesis, SAE Paper, no. 981115, DOI:10.4271/981115. [4] Pascali, L., Gabrielli, P., Caviasso, G. (2003). Improving vehicle handling and comfort performance using 4WS. SAE Paper, no. 2003-01-0961, DOI:10.4271/2003-010961. [5] Topping, R. (2012). Understeer concepts with extensions to four-wheel steer, active steer, and time transients. SAE International Journal of Passenger Cars- Mechanical System, vol. 5, no. 1, p. 167-186, DOI:10.4271/2012-01-0245.

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[6] Sano, S., Furukawa, Y., Shiralshl, S. (1986). Four wheel steering with rear wheel steer angle controlled as a function of steering wheel angle. SAE Paper, no. 860625, DOI:10.4271/860625. [7] Marino, R., Scalzi, S. (2010). Asymptotic sideslip angle and yaw rate decoupling control in four-wheel steering vehicles. Vehicle System Dynamics, vol. 48, no. 9, p. 999-1019, DOI:10.1080/00423110903248686. [8] Nagai, M., Hirano, Y., Yamanaka, S. (1997). Integrated control of active rear wheel steering and direct yaw moment. Vehicle System Dynamics, vol. 27, no. 5, p. 357-370, DOI:10.1080/00423119708969336. [9] Song, J. (2012). Integrated control of brake pressure and rear-wheel steering to improve lateral stability with fuzzy logic. International Journal of Automotive Technology, vol. 13, no. 4, p. 563-570, DOI:10.1007/ s12239−012−0054−z. [10] Shen, X.M., Yu, F. (2008). Design and analysis of an H∞ integrated control system consisting of active suspension and four-wheel steering. International Journal of Vehicle Autonomous Systems, vol. 6, no. 3-4, p. 342-360, DOI:10.1504/IJVAS.2008.023591. [11] Yin, G.D., Chen, N., Wang, J.X, Wu, L.Y. (2011). A study on μ-synthesis control for four-wheel steering system to enhance vehicle lateral stability. Journal of Dynamic Systems, Measurement, and Control, vol. 133, no. 1, p. 1-6, DOI:10.1115/1.4002707. [12] Hiraoka, T., Nishihara, O., Kumamoto, H. (2004). Model-following sliding mode control for active four-wheel steering vehicle. Review of Automotive Engineering, vol. 25, no. 3, p. 305-313. [13] Lv, H.M., Chen, N., Li, P. (2004). Multi-objective H∞ optimal control for four-wheel steering vehicle based on yaw rate tracking. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 218, no. 10, p. 217-223, DOI:10.1177/095440700421801006. [14] Yaniv, O. (1997). Robustness to speed of 4WS vehicles for yaw and lateral dynamics. Vehicle System Dynamics, vol. 27, no. 4, p. 221-234, DOI:10.1080/00423119708969329. [15] Sorgatz, U. (1975). Simulation of directional behaviour of road vehicles. Vehicle System Dynamics, vol. 5, no. 1/2, p. 47-66, DOI:10.1080/00423117508968405. [16] Weir, D., DiMarco, R.J., McRuer, D.T. (1977). Evaluation and Correlation of Driver/Vehicle Data: Summary Report. NHTSA Report STI-TR1068-1-VOL-1, National Highway Traffic Safety Administration, Washington. [17] Bourmistrova, A., Simic, M., Hoseinnezhad, R., Jazar, R. (2011). Autodriver algorithm. Journal of Systemics, Cybernetics and Informatics, vol. 9, no. 1, p. 59-66. [18] Canale, M., Fagiano, L. (2008). Stability control of 4WS vehicles using robust IMC techniques. Vehicle System Dynamics, vol. 46, no. 11, p. 991-1011, DOI:10.1080/00423110701790723. [19] Genta, G. (2003). Motor Vehicle Dynamics. World Scientific, Singapore.

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[20] Nagai, M., Nishizawa, Y., Teranishi, K. (1991). Stability of 4WS vehicle based on side slip zeroing control: Influence of steering system dynamics. SAE Paper, no. 912564, DOI:10.4271/912564. [21] Rix, J.J., Cole, D.J. (2002). Models of human learning applicable to the vehicle steering task. 6th International Symposium on Advanced Vehicle Control (AVEC 2002), Hiroshima, p. 561-568. [22] Donges, E. (1978). A two-level model of driver steering behavior. Human Factors, vol. 20, no.6, p. 691-707, DOI:10.1177/001872087802000607. [23] Guo, K.H., Guan, H. (1993). Modeling of driver / vehicle directional control system. Vehicle System Dynamics, vol. 22, no. 3, p. 141-184, DOI:10.1080/00423119308969025.

[24] Ashkenas, I.L., McRuer, D.T.(1962). A theory of handling qualities derived from pilot-vehicle system considerations. Aerospace Engineering, vol. 21, no. 2, p. 60-102. [25] Guo, K., Ma, F., Kong, F.(2002). Driver model parameter identification of the driver-vehicle-road closed-loop system. Automotive Engineering, vol. 24, no. 1, p. 20-24. (in Chinese) [26] Tan, H.S., Bougler, B., Zhang, W.B. (2002). Automatic steering based on roadway markers â&#x20AC;&#x2019; from highway driving to precision docking. Vehicle System Dynamics, vol. 37, no. 5, p. 315-339, DOI:10.1076/ vesd.37.5.315.3526.

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Received for review: 2013-01-14 Received revised form: 2013-07-05 Accepted for publication: 2013-08-08

On-line Oil Monitoring and Diagnosis

Salgueiro, J. – Peršin, G. – Vižintin, J. – Ivanovič, M. – Dolenc, B. José Salgueiro1,* – Gabrijel Peršin2 – Jože Vižintin1 – Matic Ivanovič3 – Boštjan Dolenc4 2 Cranfield

1 University of Ljubljana, Faculty of Mechanical Engineering, Slovenia University, School of Engineering, Department of Engineering Computing and Cybernetics, UK 3 Institute Jožef Stefan, Department of Systems and Control, Slovenia 4 University of Ljubljana, Faculty of Electrical Engineering, Slovenia

In condition monitoring (CM) of mechanical drives, the analysis of various physical and chemical properties of the operating lubricant can be used to diagnose defects and assess the state of the system. Recent developments in on-line oil condition sensors and advances in signal processing methods have allowed for a system for on-line oil analysis to be developed and applied in the field of predictive maintenance. The System for On-line Oil Analysis (SOOA) has the ability to measure multiple oil properties of interest and detect faults induced by transients in the acquired signals. Transient detection is based on the cumulative sum of errors (CUSUM) technique, where the error represents the difference between the predicted reference value and the current measured value. Detection of abnormal behaviour, based on transient detection, is followed by fault diagnosis, through integrated assessment of oil properties in real time. The system can operate as a standalone unit with an independent user interface or as a part of a complete integrated diagnostic system, merging oil condition evaluation with vibrational analysis and other techniques. This paper focuses on the algorithms within SOOA in charge of transient detection and fault diagnosis. The results of SOOA operation are presented through a demonstration of the method in a laboratory environment with two different sets of tests: gear pitting and water contamination. Keywords: predictive maintenance, oil condition monitoring, on-line oil analysis

0 INTRODUCTION Mechanical systems are composed of different machine elements, with the lubricant being one of the most important machine elements of each rotating machine. The main task of the lubricants is the lubrication of the bearing surfaces in the tribological contacts. It is also responsible for cooling and washing the wear particles away from the contact surfaces. The lubricant in the operating machines is the most important courier of information about the condition of the oil and the machine. During operation, it is difficult to determine the exact time of critical oil degradation and substitution. Because it is difficult to determine when the oil needs changing, periodical (off-line) or permanent (on-line) monitoring of the physical and chemical properties of the lubricant and the presence of wear particles is required [1] and [2]. During the off-line process of oil condition monitoring, samples are taken at pre-defined intervals from random locations in the oil reservoir. Samples are analysed in a laboratory environment using several known techniques, such as total base number (TBN) analysis, viscosity measurements, ferrography, etc. [3] and [4]. The randomness of the sampling location does not, however, ensure that a representative assessment of the machine’s actual condition is being performed [5]. In on-line oil condition monitoring, the system for oil analysis is connected directly to the operating reservoir of the mechanical device. In comparison to 604

the off-line methodology, the processes of sampling and analysis are performed without disruption. The generated wear particles on the lubricated elements can also be tracked continuously and with more accuracy, allowing for an early detection of mechanical failures [4] and [5]. As of today, on-line monitoring systems are being designed and tested by the major oil condition online sensors manufactures, but a system with full oil diagnosis coverage is not yet available on the market. An example of a system integrating several individual on-line sensors has been presented in the context of marine diesel engines monitoring [6]. In that study, intelligent software was developed to autonomously analyse the oil properties and diagnose the machine’s health state. Concerning oil monitoring in realtime, the challenge lies in the application of a robust automated method for change detection and diagnosis. CUSUM (Cumulative Sum of Errors) is a sequential analysis technique used to detect changes in a given time series assumed to have a statistical distribution of a Gaussian-type centred on 0 and a variance equal to 1 [7]. This approach is the basis for the trend extraction algorithm presented by Charbonnier et al. [8]. Alternatively, Vaswani [9] presents a more complex CUSUM method by applying two different likelihood functions, the expected (negative) log likelihood (ELL) and observation likelihood (OL), which are suitable for slow and fast changes, respectively. Other variations in the CUSUM technique are available

*Corr. Author’s Address: University of Ljubljana, Faculty of Mechanical Engineering, Bogišičeva 8, 1000 Ljubljana, Slovenia, jose.salgueiro@tint.fs.uni-lj.si

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 604-612

in Basseville and Nikiforov [7], with theoretical justification. In Section 1, a multisensory oil monitoring unit (ISU) that is able to measure the fault-indicative oil properties on-line will be presented. This will be followed by a detailed description of intelligent algorithms behind the assessment of lubricant condition. Section 2 is reserved for the experimental evaluation of the SOOA system, including two sets of tests: pitting and water contamination. 1 DESCRIPTION OF THE SYSTEM FOR ON-LINE OIL ANALYSIS 1.1 Architecture Fig. 1 shows the system for on-line oil analysis (SOOA), which consists of three main parts: an integrated sensor unit (ISU), an analysis environment (AE), and a maintenance centre (MC).

a) b) Fig. 1. Architecture of the system for on-line oil analysis: (1) electromotor pump; (2) hydraulic valve; (3a) to (3c) on-line oil monitoring sensors; (4) smart node; (5) database; (6) programmable device; (7) server; (8) display screens

1.2 Integrated Sensor Unit (ISU) The Integrated Sensor Unit (ISU) is connected directly to the oil reservoir, Fig. 1a. The ISU includes an electromotor pump (1), a hydraulic security valve (2) and a set of on-line oil monitoring sensors (3a) to (3c). The sensors are able to measure on-line the oil temperature, relative water content and relative dielectric constant, calculate the generated mass of wear particles, and perform counting of ferrous and non-ferrous particles. The counted particles are divided into five size classes and grouped into two main sets, small and large-sized [10]. For data communication, the ISU unit includes the smart node (SN) unit (4). SN unit is a general programmable

platform, responsible for data acquisition and communication with the analysis environment (AE) shown in Fig. 1b. Communication is done wirelessly. Details regarding SN are presented in [11]. 1.3 Analysis Environment As shown in Fig. 1b, the AE consists of a database (DB) (5) and programmable device (6), such as a computer or laptop. The data processed in the AE is also sent to the main server (7) in the MC. The MC’s personnel are able to remotely consult the results provided by the SOOA system over digital display screens (8). The AE is a digital platform designed to evaluate the current oil, wear particles and machine condition based on the measured oil properties and wear particle analysis. In the AE, a graphical user interface (GUI) and a diagnostics module (DM) are included (Fig. 2).

Fig. 2. Architecture of the Analysis Environment

From the DB, the data is channeled for processing at the DM. The main purpose of the DM is to analyse the data from the DM and perform an evaluation of the oil and particles in the oil. The diagnostics module includes two algorithms: a change detection algorithm (CDA) and a decision algorithm (DA). 1.3.1 Change Detection Algorithm (CDA) The CDA is a tool developed in MATLAB to identify trend changes in a series of data x(ti), i = 1, ..., N, acquired from on-line sensors and stored in the database. Besides identification of changes, CDA is also able to classify these changes in comparison to the acquired signal’s history.

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1.3.1.1 Data Acquisition and Normalization

At any given instant t = tn, a vector of the acquired signal, containing the last N samples, is set up, i.e. X(tn) = [x(tn–(N–1)), …, x(tn)], which is called the current data vector. The current data vector is normalized in the following form N(tn) = [x*(tn–(N–1)), …, x*(tn)]. Each value of the normalized vector x*(tj) is calculated by using the root mean square (RMS (Xn)) and average ( X n ) value as follows: x (t j ) − X n x* (t j ) = . (1) RMS ( X n )

The CUSUM value E(tn) is used to classify how the reference trend and current trend comply with each other. Classification is divided in three stages: acceptable, warning and unacceptable: • 0 ≤ | E(tn) | ≤ Th1 : Acceptable; • Th1 < | E(tn) | ≤ Th2 : Warning; Unacceptable. • | E(tn) | > Th2 : When the CUSUM value E(t) exceeds the threshold Th2, i.e. the unacceptable stage, a new reference vector XR(t) must be set. The new reference vector XR(t) contains the acquired signal values x(t), when E(t) is between the thresholds Th1 and Th2, i.e. the warning stage.

1.3.1.2 Evaluation of Trends Quantitative evaluation of the trend is based on evaluating the linear approximation of the time series X(tn). A linear trend model is defined as:

f ( tn ) = a0 + a1t , t ∈ [tn−( N −1) , tn ]. (2)

In Eq. (2), a0 is the value at the origin and a1 the slope of the approximation. Both parameters are obtained by the least squares method [12]. The new vector F(tn) of the approximated values is defined as F(tn) = [f (tn–(N–1)), …, f (tn)]. Using Eq. (2), the normalized vector N(tn) is modeled as F*(tn) = [f* (tn–(N–1)), …, f *(tn)]. The linear regression parameters of F*(tn) are denoted a0* and a1* .

1.3.1.4 Classification of Trends The new reference vector XR(t) which is set with the acquired values x(t), will be modeled according to Eq. (2). The parameter a1, Eq. (2), is transformed into reference slope a1R. The current state of the signal is classified with one of the so-called trend states presented in Table 1. Table 1. Definition of the trend states Trend state “Changing (increasing)” “Unchanging (increasing)” “Stabilizing (increasing)” “Stable” “Stabilizing (decreasing)” “Unchanging (decreasing)” “Changing (decreasing)”

1.3.1.3 Error and CUSUM Computation Having determined a0* and a1* , we predict the function values at tn+1 using Eq. (2). The extrapolated value f* (tn+1) from the vector of the approximated values F(t) is compared with the extrapolated value f R*(tn+1) from the vector of the approximated values FR(t). The vector FR(t) was calculated from the reference vector XR(t). How to obtain the reference vector XR(t) will be detailed later in this section. An error e(tn) is obtained as the difference between the two extrapolated values:

e(tn ) = f * (tn+1 ) − f R* (tn+1 ). (3)

The cumulative sum (CUSUM) technique, suggested by Charbonnier et al. [8], is used to decide whether the current trend complies with the reference trend. This is done by summing the error values, sample by sample. The CUSUM value E(tn) is then given by: 606

E (tn ) = e(tn ) + E (tn−1 ). (4)

s 3 2 1 0 -1 -2 -3

Trend limits from to Q+ +∞ Q– Q+ Th0 Q– –Th0 Th0 –Q– –Th0 –Q+ –Q– –∞ –Q+

The new trend state is determined by comparing a1R and the former reference slope a1r. The parameter a1r is used to define the trend limits presented in Table 1, using two additional thresholds ThQ and Th0 (“Stable” and “Unchanging” thresholds, respectively). ThQ can be set in terms of percentages. Th0 is an absolute parameter which is set in CDA. Th0 should be measured and adjusted according to the oil property or wear particle number. Q– and Q+ are the limits of the “Unchanging” trend state and are defined as follows:

Q+ = a1r + 0.5(a1r · ThQ), Q– = a1r – 0.5(a1r · ThQ). (5)

The possible transitions from one trend state to another have their restrictions. Fig. 3 shows which

Salgueiro, J. – Peršin, G. – Vižintin, J. – Ivanovič, M. – Dolenc, B.

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 604-612

transitions are possible, depending on the original trend state.

In order to eliminate false alarms due to temporary variations in the targeted oil property or wear particle count, the output of the s(tn) value as the latest statetransition value st is purposely delayed. The st value is only kept if s(tn) remains unchanged for a certain pre-chosen amount of time. If the signal returns to a “Stable” state, then the ST vector is reset: if s(t = tn) = 0 then ST = [0].

(7)

1.3.2 Decision Algorithm (DA)

The different transitions are coded by a letter and an index number (Fig. 3). The letter depends on the original trend state: A – “Stable”; S – “Stabilizing”; N – “Unchanging”; C – “Changing”. The index 0 indicates that no change in the trend state occurred. When the indexes are larger than 0 this indicates that the new trend state differs from the previous one.

The DA is a tool developed in MATLAB which uses the information provided by CDA to recognize modeled transient shapes of the signals, which are fault indicative and perform fault diagnosis. DA includes a fault-modes table (FMT), shape recognition and fault diagnosis modules (Fig. 4). The decision algorithm goes through all fault cases in the FMT to determine the probability of each. For the specific fault, DA checks the influence of all oil properties individually.

1.3.1.5 State-Transition Vector Definition

1.3.2.1 FMT Table

The state-transition (ST) vector is defined as ST = [st(1), st(2), …, st(M)]. The st values, i.e. statetransition indicators, are the trend state values s presented in Table 1 that are relevant for signal shape recognition. For recognition to be possible via this method, repetition of st values within the ST vector are to be avoided – the trend state value s(t = tn) will only be added to the ST vector if it differs from the previous value st(M), i.e. if the transition index differs from 0, see the diagram in Fig. 3:

The FMT is a knowledge database that connects faulty events to identifiable transients for each individual oil property. Table 2 shows which oil properties are indicative of a specific fault and how they evolve over time. Note that a fault-free case is included and can be used to assess if the mechanical system being diagnosed is performing under nominal conditions, i.e. if all oil properties are stable. A fault modes table has been established based on the theory and experimental work presented in [1], [5], [10] and [14] and predicts

Fig. 3. Trend state transition diagram

if s(t = tn) ≠ st(M) then ST = [st1, st2, …, stM, s(tn)]. (6)

Fig. 4. Decision algorithm’s process of fault diagnosis On-line Oil Monitoring and Diagnosis

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Table 2. Fault-modes table Fault

Temperature

Rel. water content

Rel. dielectric constant

Oil property Ferrous part. count (small-sized)

Ferrous part. count (large-sized)

Non-ferrous part. count (small-sized)

Non-ferrous part. count (large-sized)

(No influence)

(Fault-free) Water contamination/ Condensation

(No influence)

Chemical contamination/ Oil aging

(No influence)

Excessive wear/ Pitting

(No influence)

Particle contamination

(No influence)

four possible fault events and one additional fault-free case. 1.3.2.2 Shape Recognition From the CDA signal processing of oil property k, the corresponding state-transition vector STk is determined. From the FMT, a model for the signal shape r is also provided in the form of a statetransition vector STr. In order for the similarity test to be possible, these vectors have to be resized to the same length. If STk and STr are composed of N and M elements, respectively, then these are resized into two new L-elements (L = N × M) vectors. The scaling process is done by replicating and proportionally distributing the elements in the original ST into a new resized ST' vector. A sequence similarity test is then performed. For that, a binary scoring function is defined such that:

0, stk (l ) ≠ str (l ) σ ( stk ( l ) , str (l ) ) =  . (8) 1, stk (l ) ≠ str (l )

According to [15], the value of the alignment between the vectors ST'k and ST'r can be calculated by summing all scoring results from Eq. (8):

A = ∑σ ( stk ( l ) , str (l ) ) . (9) l =1

608

(No influence)

In the end, the similarity coefficient is the ratio between the vector alignment value A and the number of elements of any of the resized vectors: sim ( STk , STr ) =

A . (10) L

1.3.2.3 Fault Diagnosis The result for the similarity test is inputted into the fault diagnosis process (Fig. 4). For each fault event defined in the FMT we perform a number K of similarity tests. K is the number of acquired signals from the on-line sensors (Fig. 4). The fault j probability is calculated with the following equation:

p ( j ) = ∑simk ( STk , STr ) × wk , (11) k =1

Where wk is the influence weight of oil property k for the specific fault j. If all oil properties are equally influential in the identification of fault j, then wk = 1/k. If not, then the summation of all wk must still be equal to 1:

∑w k =1

= 1. (12)

The results for all fault probabilities p(j) are then shown on the Graphical User Interface (GUI) as a percentage (Fig. 5b).

Salgueiro, J. – Peršin, G. – Vižintin, J. – Ivanovič, M. – Dolenc, B.

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 604-612

Fig. 5. Graphical user interface; (1) acquired signal plot, (2) last value of the acquired signal, (3) rate of change for the acquired signal, (4) light indicator, (5) fault diagnosis subsection

1.3.3 Graphical User Interface (GUI) The graphical user interface is a visual environment for the AE. The GUI is divided into two parts or subsections (Fig. 5). Element (1) in Fig. 5 shows the plot of the acquired signals for oil properties and wear particles measured by the on-line sensors. The last value (2) and rate of change (3) of the acquired signal are also presented in this subsection. Concerning the traffic light indicator (4), the final status (“Good” – green, “Warning” – yellow and “Critical” – red) is determined for each oil property and wear particle count. Fig. 5 also presents element (5), which is the fault diagnosis subsection showing a monitored machine under nominal conditions of operation. If any fault in the tribological contact is evolving, the “fault-free” percentage will drop in favor of an increase in any probability of fault occurring.

about the state of health of the machine and schedule the necessary interventions. 2 EVALUATION OF THE ON-LINE OIL ANALYSIS SYSTEM In order to test the efficiency of the system, experiments were performed on a laboratory test rig to observe the following faults: pitting (Section 2.2) and water contamination (Section 2.3). 2.1 Experimental Setup The laboratory experimental setup consists of a synchronous electric motor (1) and a brake-generator (3) that imposes resistive torque. A single-stage gearbox (2), with a transmission ratio of 1.5, connects the input and output shafts. Shafts are coupled to the motor and generator by two elastic and one fixed coupling. The ISU unit (4) hydraulic input and output were connected directly to the gearbox (Fig. 6).

1.4 Maintenance Center The MC is composed of a group of maintenance experts that take decisions based on the SOOA’s evaluation of condition of the oil and identification of faults. In the MC, the experts can control the oil condition monitoring process by consulting the visual elements presented in the GUI (Fig. 5). The SOOA system also provides reports about the on-line oil monitoring process upon occurrence of a faultindicative event. The data and diagnostic results are stored in the DB and are available to be included in the local computerized maintenance management system (CMMS). With the CMMS, the maintenance managers can compare the machinery documentation with the on-line oil condition monitoring information. With this, the experts at the MC can make a final decision

Fig. 6. Experimental setup

The synchronous electric motor has a rated power of 12.7 kW and speed of 1470 rpm. The brakegenerator has a 20.2 kW rated power and 110 Nm

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maximum torque. Inside the gearbox, a pair of nitrated spur gears (DIN 42CrMo4) with 16 and 24 teeth was installed. Two liters of gear oil were poured inside the gearbox for each test. Before starting the monitoring process, the CDA’s input parameters were inserted into the DM as presented in Table 3. Table 3. Input parameters for the Change Detection Algorithm (CDA) Oil property Relative water content Ferrous particle count (small-sized) Ferrous particle count (large-sized)

CUSUM thresholds

Trend state thresholds ThQ Th0 [%]

Data window length [h]

Th1

Th2

50 %

20 particl. 20 particl.

200 particl. 200 particl.

6 6

0.5 %/h

25%

100 25% particles/h 20 25% particles/h

All parameters presented in Table 3 were determined from observation and testing of the data from previous experiments on the experimental setup. These are the parameters we recommend for on-line monitoring of gear oil for this specific experimental setup. These parameters can be changed from the GUI at any time by the maintenance expert once the type of lubricant or the machine elements have been changed by the maintenance intervention.

In the running-in period, the fault-free probability is 100%, meaning that no faults occurred. After this period, CDA indicated an increase in the number of small and large-sized wear particles (Fig. 8, “Changing +”). CDA’s indication transmitted to DA which calculated the fault-free probability and excessive wear in the tribological contact. The probability of fault-free then decreased from 100 to 72% (white bold line). On the other hand, the probability of excessive wear increased from 0 to 50% first and to 100% after (black dashed line). The pitting fault was fully indicated by the diagnostics module after 62 hours of the test run. Pitting on the gear teeth surface was confirmed with visual inspection at the 72nd hour. The following variations in CDA’s output and DA’s calculation of fault-free and excessive wear probability are resulted from the variations in imposed torque, Fig. 7.

Fig. 7. The time varying torque profile and temperature evolution

2.2 Pitting Test The pitting test was conducted for 120 hours. The sampling time was 1 minute for every analysed signal. The test was conducted under time varying torque conditions to test the influence of load variation in promoting pitting phenomena. The torque was set to vary in steps of 33% of the motor’s maximum torque every 7 hours, as shown in the profile presented in Fig. 7. The motor speed was set to a constant 1296 rpm. The acquired data was analysed and presented in the GUI (Fig. 5). To present the results of the pitting test from GUI in a more suitable form, we prepared the plots as shown in Fig. 8. The fine black lines in Fig. 8a and b show the number of small and largesized ferrous particles generated during the pitting test. During the running-in period, from 0 to 56 hours, the small and large-sized particles increased very slowly. Following the running-in period, both sizeclass particles started to increase faster (Fig. 8a and b). 610

2.3 Water Contamination Test This test was performed in order to observe and indicate the influence of water contamination in the operating gear oil. The water contamination test lasted for 60 hours. Motor speed was set to a constant 1000 rpm and the torque to constant 28 Nm. After 5 hours of the test run, 1 ml (500 ppm) of tap water was dropped through an inlet socket into the gearbox (white circle in Fig. 9). The water ingression immediately produced an abrupt change in the relative water content. After this change, the relative water content slowly decreased until 60 hours into test run (Fig. 9). After approximately 6 hours, the fault-free indication dropped from 100 to 86% (white bold line). At the same time, DA indicates an increase in the water contamination probability, from 0 to 50% and then to 100% (black bold line). This fully confirms that the gear oil was contaminated with water.

Salgueiro, J. – Peršin, G. – Vižintin, J. – Ivanovič, M. – Dolenc, B.

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 604-612

b) Fig. 8. Results of the pitting experiment; a) Ferrous particle count (small-sized), b) Ferrous particle count (large-sized)

Fig. 9. Results from the water contamination experiment

After 18 hours of operation, CDA detects that the relative water content readings are stable. As a consequence of this, the DA fault-free results increase

back to 100% and the water contamination probability falls to 0%.

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3 CONCLUSIONS The SOOA system can monitor physical and chemical gear oil properties and generation of wear particles, in terms of number, size and mass. Based on the monitoring of oil properties and generated particles, the SOOA system makes a fault diagnosis through integration of individual assessments. An analysis environment for communication with the operator/maintenance centre was developed and tested under live conditions. The evaluation of the diagnostic module was achieved through experimental tests in a laboratory environment. The response of the diagnostic algorithms was achieved in appropriate time, proving that the methods used for transient identification are able to successfully pinpoint the occurrence of the modeled failure events. The SOOA can operate as a separate system or as a part of a larger integrated diagnostic system. In this integrated diagnostic system, full machinery diagnosis can be achieved by merging oil condition monitoring with vibrational and acoustic signal analysis and by monitoring the environment and contact temperature. 4 ACKNOWLEDGEMENTS This work was carried out within the framework of an Applied Project supported by the Slovenian Agency for Research and Development (ARRS) and the Competence Centre for Advanced Control Technologies. It was partly financed by the Republic of Slovenia, Ministry of Higher Education, Science and Technology and the European Union (EU) – European Regional Development Fund within the Operational Programme for Strengthening Regional Development Potentials for the Period 2007 to 2013. 5 REFERENCES [1] Vižintin, J., Kambič, M., Lipušček, I., Hudnik, V. (1995). Application of Wear Particle Analysis to Condition Monitoring of Rotating Machinery in Iron and Steel Works. Lubrication Engineering, vol. 51, no.5, p. 389-393. [2] Gonçalves, A., Cunha R., Lago, D. (2007). Vibration and wear particles analysis in a test stand. Industrial Lubrication and Tribology, vol. 59, no. 5, p. 209-216, DOI:10.1108/00368790710776793. [3] Gonçalves, A., Almeida, L., Mathias, M. (2010). Wear Particle Classifier System Based on an Artificial Neural Network. Strojniški Vestnik – Journal of Mechanical Engineering, vol. 56, no. 4, p. 284-288. [4] Vähäoja, P.O., Pikkarainen, H.V.S. (2010). Trends in industrial oil analysis – a review. International

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Journal of Condition Monitoring, vol. 1, no. 1, p. 4-8, DOI:10.1784/204764211798089057. [5] Kržan, B., Vižintin, J. (2008). On-line wear and lubricant condition monitoring. Proceedings of the 5th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, Edinburgh, (CD-ROM). [6] Knowles M., Baglee D. (2012). Condition Management of Marine Lube Oil and the Role of Intelligent Sensor Systems in Diagnostics. Proceedings of the 25th International Congress on Condition Monitoring and Diagnostic Engineering (COMADEM 2012), vol. 1, p. 59-68, Huddersfield. [7] Basseville, M., Nikiforov, I.V. (1993). Detection of Abrupt Changes: Theory and Application. PrenticeHall, Englewood Cliffs, New Jersey. [8] Charbonnier, S., Garcia-Beltan, C., Cadet, C., Gentil, S. (2005). Trends extraction and analysis for complex system monitoring and decision support. Engineering Applications of Artificial Intelligence, vol. 18, no. 1, p. 21-36, DOI:10.1016/j.engappai.2004.08.023. [9] Vaswani, N. (2005). The modified CUSUM algorithm for slow and drastic change detection in general HMMs with unknown change parameters. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP ‘05), vol. 4, p. iv/701iv/704. [10] Peršin, G., Salgueiro, J., Vižintin, J., Juričić, Đ. (2012). A system for automated online oil analysis. Insight – Non-Destructive Testing and Condition Monitoring, vol. 54, no. 8, p. 428-432, DOI:10.1784/ insi.2012.54.8.428. [11] Ivanovič, M., Boškoski, P., Juričić, Đ. (2012). An environment for efficient design and implementation of condition monitoring systems for mechanical drives. Proceedings of the 9th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, London (CD-ROM). [12] Wasserman, L. (2004). All of Statistics: A Concise Course in Statistical Inference. Springer Science+Business Media, Inc., New York. [13] Holmerg, K., Helle, A., Halme, J. (2008). Machinery reliability future issues: Performance prognostics and e-Maintenance. Proceedings of the Conference on Technical Diagnostics, Lubricants and Fuels, Ljubljana, p. 3-15. [14] Byington, C.S., Schalcosky, D.C. (2000). Advances in Real Time Oil Analysis. Machinery Lubrication, from http://www.machinerylubrication.com/Read/138/realtime-oil-analysis accessed on 2012-09-03 [15] Tompa, M. (1996). Courses in Computer Science and Engineering, CSE 590BI: Algorithms in Molecular Biology. Lecture 2 – Sequence Similarity. University of Washington, Computer Science and Engineering, from http://www.cs.washington.edu/education/courses/ cse527/96wi/, accessed on 2012-09-14

Salgueiro, J. – Peršin, G. – Vižintin, J. – Ivanovič, M. – Dolenc, B.

Received for review: 2012-11-26 Received revised form: 2013-04-12 Accepted for publication: 2013-06-20

Parametric Optimization of the Output Shaft of a Portal Axle using Finite Element Analysis Jong Boon

Ooi1

Ooi, J.B. –Wang, X. – Lim, Y.P. – Tan, C.S. – Ho, J.H. – Wong, K.C. – Xin Wang2,* – Ying Pio Lim1 – ChingSeong Tan3 – Jee-Hou Ho4 – Kok-Cheong Wong4

1Universiti

Tunku Abdul Rahman, Faculty of Engineering and Science, Malaysia University Malaysia, School of Engineering, Malaysia 3Multimedia University, Faculty of Engineering, Malaysia 4University of Nottingham Malaysia Campus, Faculty of Engineering, Malaysia 2Monash

A portal axle unit is a gearbox unit installed on a vehicle for higher ground clearance and driving in off-road conditions. Shafts must be exceptionally tough and light to improve the overall performance of the portal axle unit. In this paper, a hollow shaft with a rib at both ends was proposed. The torsional stress of the three-dimensional shaft model was determined using finite element analysis (FEA) and validated by experimental testing. The hollow shaft thickness, rib thickness, depth of spokes, rib fillet radius, and number of spokes are the five of parameters considered in the torsional strength analysis of the rib. A Taguchi orthogonal array (L25) was applied to determine the optimum set of parameters for the proposed shaft. The strength and weight of the optimized model were calculated and compared to the solid shaft, hollow shaft, and proposed model. The optimized model showed improvement in torsional strength with a slight increase in weight compared to the benchmark model. Keywords: parametric optimization, finite element analysis, shaft design, portal axle

0 INTRODUCTION A portal axle unit is a gearbox unit installed on vehicle for higher ground clearance and driving in off-road conditions. Fig. 1 shows the difference between normal vehicle and vehicle with portal axle. In the event of driving off road, the operating portal axles are frequently subjected to shock, and overloading may eventually lead to failure shafts. Therefore, shafts in the portal axle must be designed with exceptionally high strength and be lightweight for improved reliability and performance.

Fig. 1. Difference between normal axle and portal axle

In most portal axle gearboxes, hollow gear shafts with acceptable thickness are normally manufactured to achieve a higher strength-to-weight ratio. However, extremely high torsion and cyclic loading resulting from driving off-road may cause higher fatigue failure or complete shaft breakage [1]. Torsional, bending and normal forces occur during the working of the shaft [2]. There is evidence of failure in the shaft due to many factors. Heyes [3] studied the common failure types in automobiles and revealed that the failure in

the transmission system elements cover a quarter of all the automobile failures. Vogwell [4] carried out a study on a failed axle and obtained the stresses on the axle via a numerical analysis technique. Other failure parts such as the failure on planetary gear wear were investigated by Yüksel and Kahraman [5] and the failure of the swing pinion shaft were investigated by Ranganath et al. [6]. Shaft designers and engineers are constantly finding solutions for redesigning shaft based on parameters to achieve an improved strength-to-weight ratio. However, they often investigate the effect of a single factor to the shaft strength and obtain the relationship. Investigating one factor at a time can be less effective, because other parameters that are considered may be interdependent and affect the overall shaft strength. Even though there are shaft design standards [7] to [9] such as the American National Standards Institute (ANSI) and the American Society of Mechanical Engineers (ASME) that can be used as a guide for engineers, they are often too general to be applied for specific applications. This is because design standards are limited to certain design criteria and design parameters for shafts. In the case of designing shafts for a portal axle gearbox, it is necessary to propose a customized shaft design for extreme operating conditions. In this paper, a hollow shaft with a rib at both ends is proposed. The hollow shaft thickness, rib thickness, rib fillet radius, depth of spokes and number of spokes of the rib structure are the quantitative parameters considered for the proposed hollow shaft. Fig. 2 shows the schematic

*Corr. Author’s Address: Monash University Malaysia, Jalan Lagoon Selatan, 46150 Bandar Sunway, Selangor Darul Ehsan, Malaysia, wang.xin@monash.edu

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flow diagram of the steps for optimizing the proposed shaft model through parametric analysis. Validation of the FEA hollow shaft model in determining shaft torsional strength through comparison with the experimental and analytical shaft model

Proposed hollow shaft with rib at both end

5 parameters of the rib structure are considered for performing parametric analysis (hollow shaft thickness, rib thickness, depth of spokes, number of spokes, & rib fillet radius)

1 VALIDATION OF THE FEA SHAFT MODEL 1.1 Finite Element Analysis ANSYS v12.0 software is used to determine the maximum torsional stress of the shaft. Firstly, a threedimensional hollow shaft 3 mm thick, 210 mm long and 37 mm in diameter is modelled. The surface boundary conditions are applied to the shaft model as shown in Fig. 3. Fixed support is applied at one end shaft and 100 Nm of torsion is applied on the other end of the shaft. A

Fixed Support

Moment 100 Nm

Application of Taguchi orthogonal array L25 to investigate the effect of the parameters and obtain possible set of optimum parameters

Evaluation of the torsional strength and weight of the optimum shaft model and comparison with the hollow shaft and solid shaft

Fig. 2. Schematic flow diagram for obtaining the optimum set of parameters of the hollow shaft with rib

ANSYS v12 software is used to investigate the torsional stress behaviour of the shaft. The FEA is a widely accepted numerical method in evaluating and verifying shaft design [10]. Recently, the gearbending stress and contact stress of the gears of the portal axle gearbox has been analysed using FEA [11]. Göksenli and Eryürek [12] used the FEA program to simulate stress analysis on the keyway shaft of an elevator to verify their mathematical calculations for determining the maximum stress. Bayrakceken et al. [13] determined the stress conditions of the failed section at the universal joint yoke of the shaft using FEA program. Recently, the use of Taguchi method has been proven effective in the work related to optimizing flow stress input for machining simulation [14]. In this paper, the L25 Taguchi orthogonal array is applied to investigate the five different parameters that may affect the shaft strength. This method is also applied to investigate the sensitivity of the five parameters to the torsional strength of the hollow shaft with the rib and to determine the possible set of optimum parameters. The strength and weight of the optimised model are obtained and compared with the solid shaft, hollow shaft, and proposed shaft. 614

Fig. 3. Applied load and constraint on the hollow shaft

Fig. 4. FEA simulation of the von Mises stress of the hollow shaft

In the mesh settings, four nodes and a linear tetrahedron type element are selected to mesh the shaft model. The average element size was set to 5 mm in the mesh settings. The maximum von Mises stress (torsional stress) of the hollow shaft model calculated in ANSYS is 141.21 MPa. 1.2 Analytical Method Distortion energy theory (DET) is applied to determine the von Mises stress of the hollow shaft. DET (also known as the von Mises criterion) postulates that

Ooi, J.B. –Wang, X. – Lim, Y.P. – Tan, C.S. – Ho, J.H. – Wong, K.C.

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 613-619

failure is caused by the elastic energy associated with shear deformation. The hollow shaft is assumed to be ductile material, thus DET is valid and can be applied. DET considers the maximum axial stress in the transverse direction (perpendicular to the shaft axis) caused by the bending moment and the maximum shear stress caused by torque. For a hollow shaft, the maximum axial stress is:

σx =

32 MD

π ( D4 − d 4 )

. (1)

mm. The cylindrical surface of the hollow shaft is slightly machined to approximately 37 mm in outer diameter and for a smoother surface finish by using the computer numerical control (CNC) lathe machine. The hollow shaft is then pre-assembled with a strain gage rosettes that provide shear strain data. When torsion is applied to the shaft, causing it to twist, shear stresses are induced. The stresses are measured by bonding the strain gauges at 45° to the horizontal torque axis. Fig. 5 shows the bonding of the strain gauge on the hollow shaft.

Similarly, the maximum shear stress is:

τ xy =

16TD

π ( D4 − d 4 )

, (2)

where M is the bending moment, T is the applied torque, D is the external diameter of the hollow shaft, and d is the internal diameter of the hollow shaft. The principal stresses can be determined with the known value of σx and τxy as in the following: 2

σ 1,2

σ +σ y  σ +σ y  2 = x ±  x  + τ xy . (3) 2  2 

For the plane stress state, the principal normal stresses when σy = 0, are:

σ 1 ,σ 2 =

Fig. 5. Bonding of the strain gauge at the centre of the shaft

)

(

16 M ± M 2 + T 2 . (4) πd3

Finally, the general equation for calculating the von Mises stress of the shaft is:

σ e = (σ 12 − σ 1σ 2 + σ 22 ) 2 . (5)

In using the DET for determining the shaft torsional stress, there are few assumptions to be considered: 1. It is based on a two-dimensional schematic diagram. 2. The effect of gravity and the mass of the shaft are neglected. 3. DET is only valid for ductile material. 1.3 Experiment Test In the experiment test, the TiniusOlsen torsion tester is used to apply torsion to the hollow shaft. Firstly, the long rod of normalized AISI 4340 alloy steel with one-and-a- 37 mm in outer diameter and 3 mm hollow shaft thickness is cut to a length of 210

Fig. 6. Mounting of the shaft to the TiniusOlsen torsion testing machine

Fig. 6 shows the mounting of the hollow shaft on the Tinius Olsen torsion tester. Both ends of the shaft are gripped and tightened using the jaw and chuck. This machine comes with a built-in data acquisition system in which the computer retrieves all the measured data required. The LabView program reads all data and writes to a text ﬁle that is readable into Microsoft Excel spreadsheet.

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1.4 Comparison of the FEA model with the experimental and analytical model The FEA model is compared with the experimental and analytical model. With the same size and dimension of the solid shaft, the torsional stresses of the models are plotted against the increasing torque, as shown in Fig. 7. All models show linear relationship when torsional stress is plotted against the linear increment of torque. The experimental model has higher torsional stress compared to the FEA and the analytical model. However, the torsional stress calculated for the FEA model is quite close to the torsional stress measured from the experimental model. The average percentage of difference between them is only 9.83%. This shows the FEA model agrees well with the experimental model. The huge difference between the analytical model with the FEA and experimental models is due to the consideration of the shaft analysis of the shaft in one dimension, and many assumptions are made to perform the calculations.

Fig. 7. Comparison between the FEA model, experimental model, and the analytical model by plot of the torsional stress against increasing torque

2 PARAMETRIC OPTIMIZATION OF THE HOLLOW SHAFT WITH RIB 2.1 Modelling of the Hollow Shaft with Rib A hollow shaft with a rib at both ends is proposed for the output shaft of the portal axle. The proposed shaft with five parameters is modelled, as shown in Fig. 8. Table 1 shows the material properties and the dimensions used for modelling the shaft. The proposed shaft is used as a benchmarking shaft for comparison with the optimized shaft and the hollow shaft in later section. Similarly, the proposed model of the hollow shaft with a rib, a torque of 100 Nm is applied at one end of the shaft and the other end of the shaft is fixed. In Fig. 9, the von Mises stress determined in the ANSYS FEA software is 102.72 MPa.

Fig. 8. Model of the hollow shaft with rib and the 5 parameters

Max

2.2 Parametric Optimization The L25 Taguchi orthogonal array (OA) is applied to determine the optimum combination of the five parameters (the hollow shaft thickness, rib thickness, depth of spokes, rib fillet radius, and the number of spokes) that will result in the lowest torsional stress. These parameters are set in the DOE++ software. The parametric design of the shaft is set with five different factors (5 parameters) and five levels (5 variables) as shown in Table 2. In Table 3, 25 unique combination parameters are generated by using DOE++ software. Then FEA simulation is conducted 616

Fig. 9 FEA simulation of the von Mises stress of the hollow shaft with rib

for each combination parameters. Thus, the maximum stress of each combination is obtained by using FEA simulation.

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the ANOVA of the five factors and the regression of each factor. In general, the notation of the applied L25 Taguchi array (5 to the power of 6) should consist of six factors in the Taguchi array. Since there are only five factors used in the Taguchi array, the remaining factor from the Taguchi array provides four degrees of freedom for the residual, which is calculated as a difference between the total sum-of-squares and the model’s sum-of-squares. The F-statistic (F-ratio) for each factor is calculated by taking the mean squares of the factor divided by the mean squares of the residual. A high value of the F-statistic of a factor implies that the effect of the factor is relevant and significant. In this case, the hollow shaft thickness contribute to the highest value of F-statistic.

Table 1. Dimensions and material properties of the proposed shaft model Length Outer diameter Material Ultimate tensile stress (UTS) Tensile yield strength Young’s Modulus Poisson’s Ratio Density

210 mm 37 mm AISI 4340 alloy steel (normalized at 870˚C) 1279.0 MPa 861.8 MPa 210 GPa 0.30 7850 kg/m3

In the next step, Analysis of Variance (ANOVA) is generated to determine the ‘F Ratio’ and ‘P value’ so that the level of significance of the parameters to the output (objective) can be distinguished. Table 4 shows Table 2. Factorial design of the shaft model using 5 factors with 5 levels A B C D E

Factor hollow shaft thickness rib thickness depth of spokes rib fillet radius no. of spokes

Unit [mm] [mm] [mm] [mm] [-]

Type qualitative qualitative qualitative qualitative qualitative

Level 1 1 1 5 1 2

Level 2 2 2 10 1.25 3

Level 3 3 3 15 1.5 4

Level 4 4 4 20 1.75 5

Level 5 5 5 25 2 6

Table 3. Taguchi orthogonal array L25 design factors Standard order 23 13 19 5 24 14 3 2 1 22 8 17 7 25 11 16 18 21 12 10 6 15 4 20 9

Run order 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Hollow shaft thickness [mm] 5 3 4 1 5 3 1 1 1 5 2 4 2 5 3 4 4 5 3 2 2 3 1 4 2

Rib thickness [mm] 3 3 4 5 4 4 3 2 1 2 3 2 2 5 1 1 3 1 2 5 1 5 4 5 4

Depth of spokes [mm] 10 25 10 25 15 5 15 10 5 5 20 25 15 20 15 20 5 25 20 5 10 10 20 15 25

Rib fillet radius [mm] 1.00 1.25 2.00 2.00 1.25 1.50 1.50 1.25 1.00 2.00 2.00 1.50 1.75 1.50 2.00 1.25 1.75 1.75 1.00 1.25 1.50 1.75 1.75 1.00 1.00

Parametric Optimization of the Output Shaft of a Portal Axle using Finite Element Analysis

No. of spokes 6 5 4 6 2 6 4 3 2 5 2 2 6 3 3 6 3 4 4 4 5 2 5 5 3

von Mises stress [MPa] 119.1 215.5 88.56 263.6 109.9 123.8 270.4 293.1 226.8 121.2 206.7 135.3 193.8 92.24 158.5 186.0 132.1 129.0 149.7 194.8 186.5 147.3 229.7 126.9 179.4

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Table 4. Analysis of variance (ANOVA) of the 5 parameters Source of variation Model A: hollow shaft thickness B: rib thickness C: depth of spokes D: rib fillet radius E: no. of spokes Residual Lack of fit Total

Degrees of freedom 20 4 4 4 4 4 4 4 24

Sum of squares [Partial] 7.56E+04 6.27E+04 5307.9926 1657.4014 5325.5358 587.819 992.3096 992.3096 7.66E+04

Table 5. Diagnostic analysis of the L25 design factors Run order 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Standard order Actual value (Y) Fitted value (YF) 23 119.1 122.816 13 215.5 223.316 19 88.56 96.376 5 263.6 266.436 24 109.9 112.736 14 123.8 119.096 3 270.4 260.736 2 293.1 288.396 1 226.8 234.616 22 121.2 111.536 8 206.7 201.996 17 135.3 139.016 7 193.8 201.616 25 92.24 100.056 11 158.5 162.216 16 186 176.336 18 132.1 134.936 21 129 124.296 12 149.7 152.536 10 194.8 198.516 6 186.5 189.336 15 147.3 137.636 4 229.7 233.416 20 126.9 122.196 9 179.4 169.736

In contrast, the P-value is the probability value that is determined from the F-distribution curve. With a known degree of freedom of a factor and the residual for a given F-statistic, the P-value of that factor can be determined. The lowest P value implies that the factor has the highest level of significance to the output response. From the ANOVA table, the level of significance in the ascending order is the number of spokes, depth of spokes, rib thickness, rib fillet radius, and hollow shaft thickness. This means that the hollow shaft thickness affects the torsional stress of the shaft 618

Mean squares [Partial] 3780.4376 1.57E+04 1326.9982 414.3504 1331.384 146.9548 248.0774 248.0774 -

F ratio 15.2389 63.2162 5.3491 1.6702 5.3668 0.5924 -

P value 0.0085 0.0007 0.0666 0.3157 0.0663 0.6878 -

the most in comparison to the other four factors. The regression information indicates the level of error. Finally, a diagnostic analysis is carried out as shown in Table 5. The one highlighted is the optimum design parameters in which the actual value corresponds to the lowest torsional stress, whereas the highlighted run order #8 corresponds to the highest actual value of the torsional stress. Therefore, the standard order 25 is the optimum set of parameter for the hollow shaft with rib in which the hollow shaft thickness is 4 mm, rib thickness is 4 mm, depth of spokes is 10 mm, rib fillet is 2 mm, and the number of spokes is 4. In addition, the fitted value (YF) is the prediction value that is dependent upon the actual value. The YF is calculated based on the equation: Fitted value (YF) = Actual value(Y) – Residual. 2.3 Strength and Weight Comparison of the Optimized Shaft Model The optimized shaft is compared with the benchmark shaft, hollow shaft, and solid shaft with regard to the torsional stress and weight reduction. The torsional stress and the weight of four different shafts are obtained by FEA simulation. Table 6 shows the torsional stress and weight reduction comparisons between the four types of shaft. The weight reduction is calculated by comparing with the mass of the solid shaft which is the heaviest. From the shaft comparisons, the optimized shaft has lower torsional stress compared to the benchmark shaft and the hollow shaft. The weight of the shaft is measured using ANSYS software to determine the percentage of weight reduction. The hollow shaft is the lightest, thus having the highest weight reduction. In order to evaluate the shaft with overall most improved strength and amount of weight reduction, the stress-to-weight reduction ratio is calculated for each shaft. The optimized shaft has the lowest stress-

Ooi, J.B. –Wang, X. – Lim, Y.P. – Tan, C.S. – Ho, J.H. – Wong, K.C.

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 613-619

Table 6. Shaft models comparison in strength and weight reduction

Length [mm] Diameter [mm] Material Torque [Nm] Hollow shaft thickness [mm] Rib thickness [mm] Depth of spokes [mm] Rib fillet radius [mm] Number of spokes Torsional stress [MPa] Weight [kg] Weight reduction [kg] compared to solid shaft Stress-to-weight reduction ratio

Optimized Benchmark Hollow shaft shaft shaft 210 37 AISI 4340 alloy steel (normalized at 870˚C) 100

Solid shaft

4 10 2 4 88.56 0.744

3 15 1.5 4 102.70 0.595

141.76 0.552

81.28 1.796

1.052

1.201

1.244

84.18

85.51

113.96

to-weight reduction compared to the benchmark shaft and hollow shaft, which indicates it has most significant improvement in both torsional strength and weight reduction. The optimized shaft has an improved strength by 13.77% but an increase of 20% in weight compared to the benchmark model. 3 CONCLUSION The shaft models were modelled using FEA and validated through comparisons with the experimental results. In the analysis of the output shaft of the portal axle, the hollow shaft with a rib is proposed and the final element model is built. The optimum set of parameters of the hollow shaft with a rib is determined by using the Taguchi method: the hollow shaft thickness is 4 mm, rib thickness is 4 mm, depth of spokes is 10 mm, rib fillet radius is 2 mm, and the number of spokes is 4. It is found that the hollow shaft thickness affects the torsional strength of the hollow shaft with a rib the most compared to the other four parameters. The optimized shaft has an improvement in strength of 13.77% but an increase of 20% in weight compared to the benchmark shaft. 4 ACKNOWLEDGEMENT The authors would like to thanks the Centre for Vehicular Technology, Universiti Tunku Abdul Rahman and the Department of Mechanical Engineering, Tunku Abdul Rahman College for the facilities and system supports.

5 REFERENCES [1] Xu, X.L., Yu, Z.W., Ding, H.X. (2006). Failure analysis of a diesel engine gear-shaft. Engineering Failure Analysis, vol. 13, no. 8, p. 1351-1357, DOI:10.1016/j. engfailanal.2005.10.015. [2] Heisler, H. (1999). Vehicle and Engine technology. 2nd ed. SAE International, London. [3] Heyes, A.M. (1998). Automotive component failures. Engineering Failure Analysis, vol. 5, no. 2, p. 129-141, DOI:10.1016/S1350-6307(98)00010-7. [4] Vogwell, J. (1998). Analysis of a vehicle wheel shaft failure. Engineering Failure Analysis, vol. 5, no. 4 p. 271-277, DOI:10.1016/S1350-6307(98)00022-3. [5] Yüksel, C., Kahraman, A. (2004). Dynamic tooth loads of planetary gear sets having tooth wear. Mechanical Machine Theory, vol. 39, no. 7, p. 695715, DOI:10.1016/j.mechmachtheory.2004.03.001. [6] Ranganath, V.R., Das, G., Tarafder, S., Das, S.K. (2004). Failure of a swing pinion shaft of a dragline. Engineering Failure Analysis, vol. 11, p. 599-604, DOI:10.1016/j.engfailanal.2003.08.004. [7] Shigley, J.E., Mischke, C.R. (1989). Mechanical Engineering Design, 5th ed. McGraw Hill Publication, New York. [8] ANSI/ASME Standard B106.1M-1985 (1985). Design of Transmission Shafting. American Society of Mechanical Engineers, New York. [9] Spotts, M.F. (1991). Design of Machine Elements, 6th ed. Prentice Hall India, New Delhi. [10] Opalić, M., Kranjčević, N., Habuš, S. (2011). Proof of strength of shafts and axles using finite element method. FAMENA, vol. 35, no. 2, p. 63-71. [11] Ooi, J.B., Wang, X., Tan, C.S., Ho, J.H., Lim, Y.P. (2012). Modal and stress analysis of gear train design in portal axle using finite element modeling and simulation. Journal of Mechanical Science and Technology, vol. 26, no. 2, p. 575-589, DOI:10.1007/ s12206-011-1040-5. [12] Göksenli, A., Eryürek, I.B. (2009). Failure analysis of an elevator drive shaft. Engineering Failure Analysis, vol. 16, no. 4, p. 1011-1019, DOI:10.1016/j. engfailanal.2008.05.014. [13] Bayrakceken, H., Tasgetiren, H., Yavuz, I. (2007). Two cases of failure in the power transmission system on vehicles: A universal joint yoke and a drive shaft. Engineering Failure Analysis, vol. 14, no. 4, p. 716724, DOI:10.1016/j.engfailanal.2006.03.003. [14] Vijay Sekar K.S., Pradeep Kumar, M. (2012). Optimising Flow Stress Input for Machining Simulations Using Taguchi Methodology. International Journal of Simulation Modelling, vol. 11, no. 1, p. 1728, DOI:10.2507/IJSIMM11(1)2.195.

Parametric Optimization of the Output Shaft of a Portal Axle using Finite Element Analysis

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Received for review: 2012-12-18 Received revised form: 2013-04-18 Accepted for publication: 2013-06-20

Experimental Investigation on Road Vehicle Active Suspension Krishnasamy, P. – Jayaraj, J. – John, D. Prabu Krishnasamy* – Jancirani Jayaraj – Dennie John

Anna University, Department of Automobile Engineering, India This paper presents an investigation report for an electronically controlled pneumatic suspension system. The performance improvement in the passenger’s comfort and attitude behaviour are evaluated for a proportional integral derivative (PID) controlled pneumatic suspension design. An appropriate mathematical model is developed for a single wheel suspension with the passenger seat system. The simulation is accomplished through LABVIEW and lab-based experimental analysis is conducted. Based on simulation and experimental values, the enhanced performance is shown through comparative results. The proposed system with the PID control improves the ride comfort and provides better road-holding characteristics, as compared to the passive suspension system. Keywords: PID, actuator, pneumatic suspension, active control, refined Zi-Ni control

0 INTRODUCTION The primary function of the vehicle suspension system is to provide vehicle body isolation from road irregularities, to maximise passenger ride comfort and retain continuous contact between the tyre and road surface. The ability of the suspension to filter out vibrations on an uneven road surface determines ride comfort [1]. Concerning the suspension system, good ride comfort is offered by soft suspension, whereas a stiff suspension is required for suddenly applied loads. A good suspension requires a trade-off between these two parameters. In a passive suspension, the suspension spring stiffness and damping values are predefined. In a semi-active suspension, the system can be controlled only in one direction: opposite to the velocity of the damper extension. In contrast, an actively controlled pneumatic suspension system adapts according to changing road conditions by continuously varying its stiffness value, and produces better sprung mass isolation. Vehicle suspension has been extensively studied for quarter-car models with two-mass systems [2] to [4]. For this study, a three mass, single-wheel suspension system with a passenger seat is considered; the masses are the passenger seat with a passenger, the vehicle body and the axle assembly [5]. The control algorithms are usually based on linear quadratic, fuzzy logic, linear quadratic regulator, linear quadratic Gaussian and a proportional integral derivative (PID) controller based on feedback control approaches. Some of the researchers preferred a PID controller, which is used with a simple control algorithm, and a robust and efficient control [6]. Therefore, the pneumatic spring stiffness controlled by the PID algorithm has been adopted for this study. From previous research, it is found that the PID control for the pneumatic suspension system 620

can improve the occupant’s comfort [7] and [8]. Simulation and an experimental study based on onequarter T suspensions with PID controllers are utilized [9] and [10] in this paper. This study focuses on the compromise of the comfort and road holding of the vehicle by the influence of the pneumatic actuator and examines the performance of the suspension system through simulation and experiment. The paper has the following sections: Section 1 introduces the need for a pneumatic suspension system and their control methods. A system is proposed based on the information gathered from the literature review that has been carried out in this area. In Section 2, the assumptions made to develop the mathematical model and the simulation model of the pneumatic suspension system are presented. The control algorithm, controller parameters, effect of control parameters on the system performance and controller design are illustrated in Section 3. The road bump model, simulation, experimental methods and its results are presented in Section 4. The simulation and testing results of the actively controlled pneumatic seat suspension system are compared with the passive suspension system. 1 PNEUMATIC SUSPENSION DYNAMICS The dynamic model of the proposed system is developed according to the following assumptions: passenger seat, vehicle body and axle assembly are both considered as a rigid body; the tyre is considered as a linear spring with equivalent stiffness and damping force of the tyre is neglected. Passive suspension is considered to be a spring and vibration damper in parallel connection; additionally, an active pneumatic spring of varying stiffness is connected, which is used as a force actuator. This spring allows for dynamic variations in stiffness characteristics

*Corr. Author’s Address: Department of Automobile Engineering, Anna University, Chennai, India, kprabuannuniversity@gmail.com

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 620-625

while the vehicle is in motion. Vehicle passenger mass with the seat mass is taken as mps. The sprung mass which represents a portion of the total mass of the vehicle is ms and the un-sprung mass mus representing one of the wheel and axle. The radial tyre stiffness and suspension spring stiffness are represented by kt and ks, respectively. The damping coefficient of the damper is represented by cs, and kcs represents the stiffness of the pneumatic spring. The input xr is assumed to be the road bump, xps, xs and xus are the vertical displacement of the passenger seat, sprung and un-sprung mass, respectively. Based on Newton’s second law, the dynamic equation of motion for a single wheel suspension system is obtained using the above assumptions.

Choose state space output vector:

xT =  x ps x ps xs xs xus xus  , (4)

yT =   x ps  xs x ps − xs xs − xus xus − xr  . (5) State space suspension model:

x = Ax + Bu ,

(6)

y = Cx + Du ,

(7)

 0  k  − ps  m ps   0 A =  k ps   ms   0   0 

Fig. 1. Pneumatic suspension system

The pneumatic vehicle suspension model is shown in Fig. 1. The inputs are the random road surface and pneumatic actuator force. Pneumatic actuator force (Fb) is the product of the relative displacement of the sprung, un-sprung mass and the dynamic stiffness of the pneumatic system (kcs), which acts on the vehicle body. The equation of motion for a pneumatic suspension model is represented by the following differential equations, Eqs. (1) to (3).

(

)

(

)

m ps  x ps + c ps ( x ps − xs ) + k ps x ps − xs = 0, (1)

(

)

ms  xs + c ps xs − x ps + k ps xs − x ps +

+cs ( xs − xus ) + k s ( xs − xus ) − kcs ( xs − xus ) = 0, (2) mus  xus + kt ( xus − xr ) + cs ( xus − xs ) +

−

1 c ps

0 k ps

0 c ps

m ps

0 c ps

0 −(k ps + k s + kcs )

0 ks + kcs mus

−

0 0

1 (c ps + cs )

0 ks + kcs ms

mus

0 cs mus

−(k s + kcs + kt ) mus

0   0    0   cs  , ms   1  c  − s  mus 

 0   0     0    B =  0  , C = [1 0 0 0 0 0].  0     kt  m   us 

The suspension travel is the relative displacement of the vehicle body and the axle assembly. The differences of un-sprung mass displacement and road height is the tyre displacement. In the air spring dynamics the force (Fb) is the total pressure ( multiplied with the area (Ae). As a result, pressure increases with displacement. Based on the above statement, the dynamic stiffness Eq. (9) is derived where the reservoir pressure (Pr), gauge pressure (Pg) and atmospheric pressure (Pa) are taken into account. Therefore the static stiffness of the pneumatic spring is given by Eq. (8)

(

kcs = Pr + Pa + Pg

) AV

+ Pg

dAe . (8) dy

In the dynamic condition, the change in effective area with deflection is extremely small i.e., constant effective area, the dynamic stiffness of the pneumatic system is:

(

kcs ,dynamic = γ Pr + Pa + Pg

) AV

(9)

+ k s ( xus − xs ) + kcs ( xs − xus ) = 0.. (3) Experimental Investigation on Road Vehicle Active Suspension

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2 PID CONTROLLER The performance of the suspension system can be optimised by a proper design and implementation of the controller. The PID feedback controller is used, which is essential to maintain the position of the passenger seat and car body under equilibrium. The function of the controller is to minimise the difference between the set point level and the measured value. The proportional gain (KP), integral gain Ki and derivative gain Kd are the parameters which influence the controller design. The Ziegler-Nichols and refined Ziegler- Nichols (RZN) tuning methods are considered while designing the PID controller. The performance of the controller is enhanced through proper selection of the PID control parameters which brings down the rattle space of the system, the acceleration of the passenger seat of the vehicle body and improves the tyre-road contact. The ability of the PID controller for the pneumatic suspension system is evaluated with a simulation of the passive suspension system. Ziegler and Nichols [11] described a method for tuning the parameters of PID controller. There are two methods in Ziegler-Nichols: the closed loop method and open loop method. The present paper implements the closed loop control method in a pneumatic suspension system. The closed loop method applies a time delay constant.

The Ziegler-Nichols tuning mathematically represented in Eq. 10.

K p e + K i ∫e dt + K p (

de ). (10) dt

2.1 Refined Ziegler- Nichols (RZN) The refined Ziegler Nichols settings are introduced by adding a new parameter β [11] to [13] in the proportional action of the controller. The new parameter β can reduce the overshoot to acceptable levels, and thus gives a good set point response. When the β value is less than unity, the control mathematical equation of the proposed controller is expressed [12] as:

t   ut ( s ) = K p  β e ( t ) + 1 ∫e (τ ) dτ  − Ti 0   dy   (11) . − K p  (1 − β ) y + Td dt  

3 SIMULATION AND EXPERIMENT The suspension plant model is developed using motion equations. The simulation model, shown in Fig. 2, is aided with LABVIEW software and the road profile is modelled as a mathematical cosine function in Eq.

Fig. 2. Simulation pneumatic suspension model

622

method

Krishnasamy, P. – Jayaraj, J. – John, D.

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 620-625

(12) to investigate the performance of the pneumatic suspension under general road conditions. A single bump is given as road input for which the dynamic response of a single wheel is investigated. In this study, a typical road disturbance is used and it is mathematically represented as:

a [1 − cos8π t ] xr ( t ) =  0 

( 0.5 ≤ t < 0.75) , (12) otherwise

where a denotes the bump amplitude. The single bump input, with height of 0.05 m is obtained by setting a = 0.025 [13] and [14], and is used to study the system response. Furthermore, the maximum travel distance of the suspension travel is aimed to be limited within ±8 cm. The mathematical model of the system defined in Eqs. (6) and (7) and the RZN proportional integral derivative controller in Eq. (11) were simulated, see Fig 2. The performance of the passive system is compared to the PID-controlled pneumatic suspension system. The simulation parameters value based on the laboratory test bench [11] are as follows: passenger, sprung and un-sprung mass plates are 5.25, 18.2 and 7.25 kg, respectively. Suspension stiffness and radial tyre stiffness are 1570 and 4850 N/m. The damping value of the damper is 100 Ns/m. An experimental test rig is used to analyse the performance of the pneumatic suspension system, which is shown in Fig. 8. The main structure of the test bench comprises a steel frame with two vertical guide rods to guide the mass plates through the linear bearing. A set of coil springs is placed at the appropriate locations to provide suspension and tyre stiffness. The 0.05 m excitation is generated by the cam, powered by speed reduction gear box along with motor setup. In order to accomplish the design objective of an active suspension system i.e. to increase the ride comfort and road holding, there are the three parameters to be observed in simulations and experiments: the passenger acceleration, car body acceleration and the suspension deflection. From the observation, the descending value of the peak acceleration of the passenger seat is recorded by the uncontrolled passive system, experiment and simulation. This evidently shows that RZN control offers the better result in the passenger seat acceleration and travel as shown in Figs. 3 and 4. Fig. 4. Illustrates that the proposed system can effectively absorb the vehicle vibration in comparisons to the PID method and the passive system. The body acceleration, shown in Fig. 5, in the PID design system is reduced

Fig. 3. Results showing the seat acceleration for 5 cm bump road input

Fig. 4. Results showing the seat travel for 5 cm bump road input

Fig. 5. Results showing the body accelerations for 5 cm bump road input

significantly, which guarantees better ride comfort. The suspension travels (see Fig. 6) of a controlled active suspension system and a uncontrolled passive suspension system are used for comparison purposes. The result shows that the suspension travel is limited within the travel limit i.e. ±8 cm. The highest

Experimental Investigation on Road Vehicle Active Suspension

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suspension travel is recorded by the passive system, shown in Fig. 6, and the RZN PID performs better as compared to the other one. Tyre travel, shown in Fig. 7, is noticeably small in simulation and slightly raised in the experimental setup for the proposed controller. In the case of body acceleration, it is observed that the lowest possible acceleration is made by RZN and the maximum by the passive system (Table 1). 4 CONCLUSION

Fig. 6. Results showing the suspension travel for 5 cm bump road input

From the results obtained, the RZN control algorithm produces the lowest accelerations at the passenger seat. Considering the simulation and test results, the RZN control algorithm is effective and reliable for pneumatic suspension systems. All other parameters are found to be within satisfactory limits. Although the peak tyre travel is increased, it lasts for a very short duration in relation to the passive system. This results in improved road holding characteristics when the vehicle is negotiating the bump. Therefore, it is concluded that the active pneumatic suspension system with the PID controller improves ride comfort while retaining road holding characteristics, as compared to the passive suspension system. Thus, the results are also confirmed if the study has extended to examine the behaviour at different road amplitudes. 5 REFERENCE

Fig. 7. Results showing the tyre travel for 5 cm bump road input Table 1. Quantitative result with passive system comparison of peak values Parameters

Passive

Simulation

Experiment

Seat acceleration [m/s²]

3.3194

2.0782

2.7085

Body acceleration [m/s²]

4.0721

2.0855

2.3998

Seat travel [m]

0.0215

0.011

0.0021

Suspension travel [m]

0.0388

0.0237

0.0245

Tyre travel [m]

0.0543

0.0537

0.0576

Fig. 8. Experimental setup; 1. steel base structure, 2. passenger mass, 3. seat damper, 4. sprung mass, 5. pneumatic actuator, 6. coil spring, 7. unsprung mass, 8. guide plate, 9. disturbance actuator, 10. guide rods, 11. accelerometer sensors

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[1] Genta, G., Morello, L. (2009). The Automotive Chassis, Vol. 2: System Design, Mechanical Engineering Series, Springer, DOI:10.1007/978-1-4020-8675-5. [2] Karnoop, D. (1986). Theoretical limitations in active vehicle suspension. International Journal of Vehicle Mechanics and Mobility, vol. 15, no. 1, p. 41-54, DOI:10.1080/00423118608968839. [3] Herdrick, J.K., Batsuen, T. (1990). Invariant properties of automotive suspension. Proceedings of the institution of mechanical engineers, Part D: Journal of Automobile Engineering, vol. 204, no. 1, p. 21-27, DOI:10.1243/PIME_PROC_1990_204_128_0. [4] Hrovat, D. (1993). Application of optimal control to advance automotive suspension design. Transaction of ASME, Journal of Dynamics Systems, Measurement And Control, vol. 115, p. 328-342, DOI: DOI:10.1115/1.2899073. [5] Rakheja, S., Afework, Y., Sankar, S. (1994). An analytical and experimental investigation of driver seat suspension system. Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility, vol. 23, no. 1, p. 501-524, DOI:10.1080/00423119408969072. [6] Li, M., Li, Z., Shen, X., Guo, J. (2010). Study on PID control for semi active air suspension for riding

Krishnasamy, P. – Jayaraj, J. – John, D.

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comfort. Second WRI Global Congress on Intelligent System, p. 47-50, DOI:10.1109/GCIS.2010.82. [7] Toyofuku, K., Yamada, C., Kagawa, T. (1999). Study on dynamic characteristics analyses of air spring with auxiliary chamber. JSAE Review, vol. 20, no. 3, p. 349355, DOI:10.1016/S0389-4304(99)00032-6. [8] Pooya, M., Reza, K. (2008). Active stiffness and damping control of air mounted system. ASME International Mechanical Engineering Congress and Exposition, Volume 11: Mechanical Systems and Control, p. 531-544, DOI:10.1115/IMECE2008-66272. [9] Renn, J-C., Wu, T.H. (2007). Modeling and control of new ¼T servo hydraulic vehicle active suspension system. Journal of Marine Science and Technology, vol. 15, no. 3, p. 265-272. [10] Koch, G., Pellegrini, E., Spirk, S., Lohmann, B. (2010). Design and modeling of a quarter car vehicle test rig

for active suspension control. Technical Reports on Automatic Control, vol. TRAC-5, p.1-28. [11] Ziegler, J.G., Nichols N.B. (1942). Optimum settings for automatic controllers. Transactions of the ASME, vol. 64, p. 759-768. [12] Astrom, K.J., Hagglund, T. (2004). Revisiting the Ziegler-Nichols step response method for PID control. Journal of Process Control, vol. 14, no. 6, p. 635-650, DOI:10.1016/j.jprocont.2004.01.002. [13] Krishnasamy, P., Jayaraj, J. (2012). Vehicle active suspension system simulation with optimum PID technique. Wulfenia Journal, vol. 19, no. 9, p. 119-121. [14] Lin, J.S., Kanellakopoulos, I. (1997). Nonlinear Design of Active Suspension. IEEE Control System Magazine, vol. 17, no. 3, p. 45-59, DOI:10.1109/37.588129.

Experimental Investigation on Road Vehicle Active Suspension

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Received for review: 2013-04-12 Received revised form: 2013-07-09 Accepted for publication: 2013-08-20

Hardness Effects on Abrasive Flow Machining Gov, K. – Oer Eyercioglu, O. – Cakir, M.V. Kursad Gov1,* – Omer Eyercioglu1 –Mehmed Veysel Cakir2 1Gaziantep

2Vocational

University, Mechanical Engineering, Turkey School of Kilis, Mechanical Department, Turkey

Abrasive flow machining (AFM) is a manufacturing technique that uses the flow of a pressurized abrasive media to remove workpiece material. In comparison with other polishing technique, AFM is very efficient, suitable for the finishing of complex inner surfaces. In this paper, the effect of workpiece hardness on the AFM process has been investigated. An experimental study was carried out on AISI D2 tool steel hardened to 31, 45 and 55 HRC. The specimens were cut by using wire electro discharge machining (WEDM) and then finished with AFM. The results show that the white layer formed during WEDM is successfully removed by AFM in a few cycles. Although the surface quality is improved by AFM for all hardness groups, the results show that harder materials have more surface improvement than the softer ones. Keywords: abrasive flow machining, surface roughness, wire electro discharge machining

0 INTRODUCTION Abrasive flow machining (AFM) is a nontraditional machining process that was developed in the USA in the 1960s. AFM can be described as pressurized media passing through the surfaces to reach the desired surface quality. There are mainly two types of abrasive flow machines according to flow of media: one-way and two-way. The media consists of a type of polymeric carrier and abrasive particles that are SiC, Al2O3, diamond, etc. AFM is used to deburr, polish, radius, and remove recast layers. Applications of AFM include the finishing of extrusion dies, medical implants and aerospace components. AFM can be successfully applied to different kinds of engineering materials from soft to hard, ferrous to nonferrous, and also metallic to nonmetallic. With the use of AFM, excellent surface finishes and close geometric tolerances can be attained economically. The media in the AFM process is used to polish difficult-to-reach areas, and to follow complex geometries and microholes. The success of the AFM process depends on a number of process parameters that can be classified into three groups: AFM parameters, polishing media parameters and workpiece parameters. The AFM parameters are pressure, flow speed, number of cycles and machining time. The polishing media parameters are viscosity, temperature, abrasive material, mesh size and its concentration. The work-piece parameters are mainly the shape, the material hardness, the premachining process and surface texture orientation. Rhodes [1] studied AFM processes and evaluated process parameters such as pressure, speed of the flow, volume of the media, types of abrasive, which affects the polishing of work pieces. Jain and Adsul 626

[2] studied the AFM process parameters, such as four kinds of mesh sizes and concentrations, the number of process cycles, different media flow speeds on aluminum and brass, simulating soft and hard materials, respectively. They concluded that dominant parameters are the number of process cycles, mesh size and abrasive concentration, and the effect of flow rate is less in comparison to the other AFM process parameters. Jain et al. [3] studied the AFM media parameters (abrasive mesh size and concentration, temperature and viscosity) and found that the media viscosity increases with increasing abrasive concentration and decreases with the increase in mesh size and media temperature. Higher the viscosity results in high MRR and better surface roughness. Flether and Fioravanti [4] studied the rheology of the polyborosiloxane as media. They concluded that the concentration of abrasive is a more dominant parameter than grain size and polymer media. Gorana et al. [5] studied the AFM media parameters such as media pressure, abrasive concentration and mesh size for silly putty (Dow Corning 3179) and silicon carbide. They were measured the acting forces on the specimens during process and showed the influence of media parameters on the resulting surface roughness. Agrawal et al. [6] used polyborosiloxane as media and predicted the viscoelastic properties of media such as viscosity, creep compliance and bulk modulus. Loveless et al. [7] compared AFM process on the various machined surfaces obtained from grinding, milling, turning, and wire electro-discharge machine (WEDM) operations. They reported that the best improvement in the surface quality was obtained on the WEDMed surfaces. Kenda et al. [8] studied the influence of process parameters on surface integrity. They used EDM AISI

*Corr. Author’s Address: Mechanical Engineering, Gaziantep University, 27360, Gaziantep, Turkey, gov@gantep.edu.tr

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 626-631

D2 pre-machined hardened tool steel as a work piece and reported that AFM is capable of removing an EDM damaged surface and significantly improving the surface roughness as well as of inducing high compressive residual stresses onto the machined surface. Bähre et al. [9] developed a setup to measure the axial forces under applied media pressure during the AFM of AISI 4140 steel. They presented the influence of media pressure on the surface quality and form tolerance. However, there is only one study available in the literature [2] considering the effect of the hardness of the workpiece material, but the study [2] was carried out for brass and aluminum as hard and soft materials, respectively. Therefore, it is necessary to show the influence of the hardness of the steel on the AFM process. This paper is focused on the effect of workpiece hardness on the AFM process. Three groups of AISI D2 tool steel (hardened to 31, 45 and 55 HRC) were cut by WEDM. The surface roughness values were measured and scanning electron microscope (SEM) images were taken from the surfaces before and after AFM for various processing cycles. The improvement of the surfaces with respect to the hardness was observed. 1 EXPERIMENTAL WORKS

the cylinder and the complete stroke of the piston. Thus, the cycle time depends on the flow rate (i.e. piston speed) and one cycle in the experimental study takes one minute. Table 1. Surface roughness values of the specimens before AFM Hardness

31 HRC

45 HRC

55 HRC

Specimen No A13 A14 A15 A16 A17 A18 B13 B14 B15 B16 B17 B18 C13 C14 C15 C16 C17 C18

Ra [µm] 2.5 2.4 2.4 2.5 2.5 2.5 2.4 2.6 2.4 2.6 2.4 2.4 2.4 2.6 2.4 2.4 2.4 2.5

Rz [µm] 10.34 12.88 11.42 10.09 9.17 12.33 14.2 11.93 11.5 9.34 8.49 10.42 12.05 11.65 12.19 9.9 10 12.11

Rq [µm] 2.96 2.52 2.48 2.61 2.84 3.19 2.85 2.42 2.38 2.93 2.77 2.11 3.02 2.57 2.52 2.75 2.88 3.14

Table 2. Abrasive flow machine

1.1 Workpiece Material The workpiece is made of the heat treated AISI D2 tool steel. 10×40×500 mm slabs were cut from the same stock and heat treated to 31, 45 and 55 HRC. The specimens were cut from the slabs by using WEDM to 10×40×10 mm. The WEDM parameters were kept constant for all specimens to ensure the presurface characteristics of the specimens. The surface roughness values of the specimens after WEDM are given in Table 1 (Ra, Rq, Rz).

Machine Specification Pump Pressure

350 to 1000 psi (24 to 200 bar)

Media capacity Stroke Bore diameter

2 litre 250 mm 120 mm

1.2 The Abrasive Flow Machine The AFM machine used in the study is given in Fig. 1; it was designed and constructed in the Advanced Machining Center of The University of Gaziantep. It is a one-way AFM and the specifications of the machine are given in Table 2. The machine has three units: the main frame, the control system, and the hydraulic unit. The hydraulic unit ensures adequate movement and media pressure that can be manually configured. The control system is designed to control the volume of polishing media and the number of cycles. A cycle in one-way AFM is composed of the filling stage of

Fig. 1. Abrasive flow machine

1.3 Polishing Media The polishing media used in the experiments is a mixture of a polymer-based carrier and abrasive

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particles. The specifications of the medium are summarized in Table 3. Table 3. Polishing Media Parameters Viscosity Abrasive type Mesh size Abrasive concentration

60 Pa.s Al2O3 180 70 %wt.

1.4 Experimental Procedure The experiments were performed on the three groups of specimens (31, 45 and 55 HRC). A fixture (see Fig. 2) was used to hold the specimens allowing the flow of polishing media through the WEDMed surfaces with an opening of 2×20 mm. A total of 2000 g of polishing media was flowing through in one cycle and the experiments were carried out for 1, 3, 5, 10, 20 and 100 cycles. The machining parameters were chosen according to the successful results of preliminary studies. These parameters are generally depending on the capacity of the machine, viscosity and abrasive concentration of the media, geometry of the surface and the opening. The AFM pressure was 10 MPa and the flow rate was 50 g/s. The experiments were repeated for three specimens in each condition and the averages of the 18 surface roughness measurements were taken. A Mitutoyo SJ 400 surface measuring machine was used and the cut of length was chosen as 0.8 mm. The SEM images of the surfaces were taken by using a Jeol JSM-6390 LV electron microscope.

in Fig. 3; the values are the average of 18 measures along and perpendicular to the flow directions. As seen from the figures, the surface roughness decreased with increase in number of cycles for all hardness values. The results show that the surface roughness of the WEDMed surfaces significantly changed in first 20 cycles and then settled to a saturated level gradually. The surface roughness after 50 cycles is decreased slightly. Although the trends are similar for all hardness groups, the final surface roughness values are different. The harder the workpiece is better the surface roughness according to the final measures. This result has to be explained together with the SEM images of the surfaces which are given in the following section. It should be observed that these surface roughness values were obtained by using 180 mesh abrasives, better finishing may be obtained low abrasive sizes with subsequent operations.

Fig. 2. Fixture with workpiece

2 RESULTS AND DISCUSSION 2.1 Measurements of Surface Roughness The results of the selected roughness parameters (Ra, Rq, Rz) for three hardness groups are presented 628

c) Fig 3. Change in surface roughness with respect to AFM cycles; a) Ra, b) Rq and c) Rz

Gov, K. – Oer Eyercioglu, O. – Cakir, M.V.

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 626-631

45 HRC

55 HRC

100 Cycles AFM

20 Cycles AFM

5 Cycles AFM

1 Cycle AFM

Before AFM

31 HRC

Fig. 4. SEM images of AFM at 190× magnification Hardness Effects on Abrasive Flow Machining

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2.2 SEM Images SEM images of taken from the surfaces of the specimens are given in Fig. 4. During the wire EDM process, rapid heating and cooling occurs, and causes a heat-affected layer on the machined surface to form. This layer is generally formed by the sticking of the non-removed debris and also the depth of material affected from high temperature. The top of the layer consists of a re-solidified layer and has a re-cast structure. The recast layer seems to have a white colour when examined with a microscope and thus it is commonly referred to as “the white layer”. The

45 HRC

55 HRC

5 cycles AFM

Before AFM

31 HRC

white layer is very hard and brittle, and has microcracks. In Fig. 5, Microscopic photographs of the white layers for three groups of specimens are given. Due to the nature of the EDM, the white layer thickness is taken as an average value. The average white layer thickness values of the three groups of specimens are near to each other (about 22 µm). In the first five cycles of the AFM process, the white layer is removed for all specimens. This can be seen from Fig. 5, as the improvement in the surface roughness values of all groups are similar up to five AFM cycles.

Fig 5. Microscopic photographs of before and after AFM process 45 HRC

100 Cycles AFM

Before AFM

31 HRC

Fig 6. SEM images of AFM at 1000× magnification

630

Gov, K. – Oer Eyercioglu, O. – Cakir, M.V.

55 HRC

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, 626-631

After removal of the white layer, abrasion the behaviors of the three hardness groups were changed. In the softer specimens (31 HRC), the indentation of the abrasive particles to the surfaces was observed. For the harder group (55 HRC), smearing and plowing were less frequent and the final surface roughness is better. The differences between the initial and final surfaces of the three groups of specimens can be seen from Fig. 6 at a magnification ratio of 1000.

[2]

[3]

3 CONCLUSIONS In this paper, the effect of hardness of WEDMed AISI D2 tool steel on abrasive flow machining was investigated. From the experimental results, the following conclusions have been derived: • The white layer formed during WEDM is successfully removed by AFM in a few cycles. The removal of this layer eliminates surface cracks and thus the fatigue strength may increase. • The surface quality is improved by AFM for all hardness groups. The results show that the surface roughness of the WEDMed surfaces significantly changed in the first 20 cycles and then settled to a saturated level gradually. The surface roughness after 50 cycles is decreased slightly. • Although the trends of surface roughness measurements are similar for all hardness groups, the results show that harder material has more surface improvement than the softer ones.

[4]

[5]

[6]

[7]

4 ACKNOWLEDGEMENTS The authors would like to thank Gaziantep University Scientific Research Council (BAP) due to financial support to this research.

[8]

[9]

5 REFERENCES [1] Rhodes, L. (1991). Abrasive flow machining: A case study. Journal of Materials Processing Technology,

vol. 28, no. 1-2, p 107-116, DOI:10.1016/09240136(91)90210-6. Jain, V.,K., Adsul, S.,G. (2000). Experimental investigation into abrasive flow machining. International Journal of Machine Tools and Manufacture, vol. 40, no. 7, p. 1003-1021, DOI:10.1016/S0890-6955(99)00114-5. Jain, V.K., Ranganatha, C., Muralidhar, K. (2001). Evaluation of Rheological properties of medium for AFM process. Machining Science and Technology: An International Journal, vol. 5, no. 2, p, 151-170, DOI:10.1081/MST-100107841. Flether, A.J., Fioravanti, A. (1996). Polishing and honing processes: An investigation of the thermal properties of mixture of polyborosiloxane and silicon carbide abrasive. Proceedings of the Institution of Mechanical Engineers. Part C: Journal of Mechanical Engineering Science, vol. 210, no. 3, p. 255-265, DOI:10.1243/PIME_PROC_1996_210_195_02. Gorana, V.,K., Jain, V.,K., Lal, G.,K, (2004). Experimental investigation into cutting forces and active grain density during abrasive flow machining. International Journal of Machine Tools and Manufacture, vol. 44, no 2-3, p. 201-211, DOI:10.1016/j.ijmachtools.2003.10.004. Agrawal, A., Jain, V.K., Muralidhar, K. (2005). Experimental determination of viscosity of abrasive flow machining media. International Journal of Manufacturing Technology and Management, vol. 7, no. 2-4, p. 142-156, DOI:10.1504/IJMTM.2005.006828. Loveless, T.R., Williams, R.E., Rajurker, K.P. (1994). A study of the effects of abrasive-flow finishing on various machined surfaces. Journal of Materials Processing Technology, vol. 47, no. 1-2, p. 133-151, DOI:10.1016/0924-0136(94) 90091-4. Kenda, J., Pusavec, F., Kermouche, G., Kopac, J. (2011). Surface integrity in abrasive flow machining of hardened tool steel AISI D2. Procedia Engineering, vol. 19, p. 172-177, DOI:10.1016/j.proeng.2011.11.097. Bähre, D., Brünnet, H., Swat, M. (2012). Investigation of one-way abrasive flow machining and in-process measurement of axial forces. Fifth CIRP Conference on High Performance Cutting, vol. 1, p. 419-424, DOI:10.1016/j.procir.2012.04.075.

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Vsebina Strojniški vestnik - Journal of Mechanical Engineering letnik 59, (2013), številka 10 Ljubljana, oktober 2013 ISSN 0039-2480 Izhaja mesečno

Razširjeni povzetki člankov Elvira Džebo, Dušan Žagar, Matjaž Četina, Gregor Petkovšek: Skrajšanje računskega časa simulacij po metodi SPH z uporabo povezanega 2D/3D-modela Mitja Mori, Tilen Mržljak, Boštjan Drobnič, Mihael Sekavčnik: Karakteristike delovanja in proizvodnje vodika v elektrolizerju za elektrolizo alkalne vodne raztopine Hongming Lv, Shaona Liu: Bočna vodljivost zaprtozančnega sistema voznika in vozila s sistemom 4WS s krmiljenjem hitrosti vrtenja okrog navpične osi José Salgueiro, Gabrijel Peršin, Jože Vižintin, Matic Ivanovič, Boštjan Dolenc: Sistem za sprotno analizo olja in delcev v olju Jong Boon Ooi, Xin Wang, Ying Pio Lim, ChingSeong Tan, Jee-Hou Ho, Kok-Cheong Wong: Parametrična optimizacija odgonske gredi portalne preme z analizo po metodi končnih elementov Prabu Krishnasamy, Jancirani Jayaraj, Dennie John: Eksperimentalna raziskava aktivnega vzmetenja cestnih vozil Kursad Gov, Omer Eyercioglu, Mehmed Veysel Cakir: Vpliv trdote na obdelavo z abrazivnim tokom Osebne vesti Doktorske disertacije, znanstvena magistrska dela, specialistična dela, diplomska dela

SI 115 SI 116 SI 117 SI 118 SI 119 SI 120 SI 121

SI 122

Prejeto v recenzijo: 2013-01-02 Prejeto popravljeno: 2013-04-24 Odobreno za objavo: 2013-06-17

Skrajšanje računskega časa simulacij po metodi SPH z uporabo povezanega 2D/3D-modela Džebo, E. – Žagar, D. – Četina, M. – Petkovšek, G. Elvira Džebo1,* – Dušan Žagar1 – Matjaž Četina1 – Gregor Petkovšek2 1 Univerza

v Ljubljani, Fakulteta za gradbeništvo in geodezijo, Slovenija 2 HR Wallingford, Velika Britanija

Metoda hidrodinamike zglajenih delcev (ang. Smoothed particle hydrodynamics, SPH) je brezmrežna Lagrangeova metoda, ki se uporablja za simulacije toka s prosto gladino. Metoda je uporabna predvsem za simulacije hipnih sprememb vodne gladine, ena izmed večjih slabosti metode pa je zelo dolg računski čas simulacij. Pri modeliranju pogosto naletimo na primere, ko je tok dlje širinsko povprečen, nato pa se spremeni tlorisna oblika obravnavane domene. Primer tovrstnega toka je npr. tok v kanalu z zožitvijo ali/in razširitvijo. Ker je tok pred zožitvijo/razširitvijo kanala širinsko povprečen, je na tem območju smiselno uporabiti hitrejši 2D-model SPH. Gladina in hitrost vode za razširitvijo/zožitvijo kanala pa se naglo spreminjata, zato pride na tem območju v poštev samo zamuden 3D-model SPH. Cilj naše raziskave je med seboj povezati modele SPH različnih dimenzij in s tem skrajšati računski čas simulacij. V študiji smo uporabili model Tis Isat, ki smo ga razvili na Katedri za mehaniko tekočin z laboratorijem, uporabljamo pa ga za simulacije toka vode po metodi SPH. Delovanje povezanega modela Tis Isat smo preverili na dveh primerih: 1. na primeru toka v kanalu z razširitvijo, ter 2. na primeru toka v kanalu z zožitvijo in razširitvijo. Znanih je več študij, ki predlagajo različne načine krajšanja računskega časa: a) s povezovanjem modela SPH in drugega konvencionalnega mrežnega modela, ter b) z medsebojnim povezovanjem modelov SPH različnih ločljivosti. V pričujočem delu pa smo javnosti prvič predstavili nov način, pri katerem računski čas simulacij krajšamo z medsebojnim povezovanjem modelov SPH različnih dimenzij. Rezultate simulacij, ki smo jih dobili s povezanim 2D/3D-modelom Tis Isat, smo primerjali z različnimi vrednostmi: 1. z meritvami, izvedenimi na fizičnem modelu, 2. z rezultati simulacij, ki smo jih dobili s 3D-modelom Tis Isat, in 3. z rezultati simulacij, izvedenih s konvencionalnim modelom. Pokazali smo, da lahko z uporabo povezanega 2D/3D-modela SPH bistveno skrajšamo računski čas simulacij, s tem pa ne vplivamo na točnost rezultatov. Zaradi pozitivnih ugotovitev bi bilo v nadaljevanju smiselno razviti še pristop, ki bi omogočal povezovanje zamudnega SPH-modela Tis Isat z drugimi hitrejšimi mrežnimi modeli, ki jih imamo na razpolago v naši raziskovalni skupini. V tem primeru bi model SPH uporabili samo na območjih, kjer se gladina vode izrazito spreminja. Krajšanje računskega časa, potrebnega za simulacije SPH, predstavlja izziv mnogim raziskovalcem omenjene metode, zato menimo, da bomo s tem delom pripomogli k nadaljnjim raziskavam in k razvoju tovrstnih pristopov. Ključne besede: hidrodinamika zglajenih delcev, metoda SPH, skrajšanje računskega časa simulacij, povezani 2D/3D-model SPH, tok s prosto gladino, porušitev pregrade

*Naslov avtorja za dopisovanje: Univerza v Ljubljani, Fakulteta za gradbeništvo in geodezijo, Jamova 2, Ljubljana, Slovenija, elvira.dzebo@fgg.uni-lj.si

SI 115

Prejeto v recenzijo: 2012-11-09 Prejeto popravljeno: 2013-06-20 Odobreno za objavo: 2013-07-08

Karakteristike delovanja in proizvodnje vodika v elektrolizerju za elektrolizo alkalne vodne raztopine Mori, M. – Mržljak, T. – Drobnič, B. – Sekavčnik, M. Mitja Mori* – Tilen Mržljak – Boštjan Drobnič – Mihael Sekavčnik Univerza v Ljubljani, Fakulteta za strojništvo, Slovenija

Vodikove tehnologije kažejo vse značilnosti, da bi lahko v prihodnosti postale pomemben dejavnik na področju oskrbe z energijo, saj je vodik energijski vektor, ki ga lahko proizvajamo po različnih metodah in iz različnih energentov (ne samo fosilnih), lahko ga transportiramo in skladiščimo, za razliko od obnovljivih virov energije (OVE) pa ga lahko porabljamo nadzorovano takrat, ko obstaja potreba po energiji. Nove tehnologije seveda predstavljajo nove izzive in nove omejitve. Postavljen je bil vodikov laboratorij, ki je sestavljen iz elektrolizerja, rezervoarja za vodik, gorivne celice in električnega bremena, predstavlja pa napreden energetski sistem, ki ga lahko vključimo v obstoječo elektroenergetsko infrastrukturo. Glavni cilj, predstavljen v prispevku, je bil eksperimentalno raziskati obratovalne in energijske karakteristike obravnavanega elektrolizerja. V ta namen smo določili bilančne meje elektrolitske celice in sistema kot celote, definirali enačbe za popis energijskih in obratovalnih karakteristik, preučili značilnosti obstoječega merilnega sistema ter zasnovali potek in izvedbo meritev. Predstavljeni so rezultati meritev delovanja komercialnega elektrolizerja HySTAT-A-S za elektrolizo alkalne vodne raztopine proizvajalca Hydrogenics v njegovih značilnih obratovalnih točkah in pri stacionarnih delovnih pogojih. Njegova največja zmogljivost je 15 Nm3 vodika na uro pri tlakih do 25 bar. Določen je energijski izkoristek elektrolitske celice kot osnovne enote elektrolizerja in izračunan celotni energijski izkoristek sistema elektrolizerja z vsemi podsklopi. Identificirani so glavni energijski tokovi okrog bilančne lupine ter ovrednotene izgube vodika znotraj sistema. Raziskan pa je tudi vpliv temperature elektrolita na energijski izkoristek elektrolitske celice. Na podlagi eksperimentalnih podatkov so bile določene konstante empiričnega modela odvisnosti električne napetosti od električnega toka pri različnih vrednostih delovnega tlaka, ki predstavlja osnovno karakteristiko elektrolizerja. Na podlagi rezultatov je bilo ugotovljeno, da je energijski izkoristek elektrolitske celice pri značilnih obratovalnih pogojih (jakost električnega toka med 180 in 400 A) med 73 in 83%, pri čemer z večanjem jakosti električnega toka izkoristek pada. Razširjena merilna negotovost energijskega izkoristka je pri konstantnih obratovalnih pogojih, relativno gledano, približno 0,2 %. Specifična raba električne energije v elektrolitskem skladu je med 4,2 in 4,8 kWh/Nm3H2. Sprememba delovnega tlaka med 16 in 20 bar g v elektrolitskem skladu nima vpliva na karakteristike delovanja sklada, je pa očitno, da ima večja proizvodnja vodika za posledico večje energijske izgube. Temperatura elektrolita ima velik vpliv na proces v alkalnem elektrolizerju, saj nihanje temperature med 59 in 65 °C pri konstantnem električnem toku 266 A prinaša nihanje izkoristka med 77 in 79 %. Višje temperature elektrolita pomenijo večje energijske izkoristke. Energijski izkoristek celotnega sistema znaša med 50 in 60 % in je odvisen od delovnega tlaka, saj višji tlaki pomenijo večje izgube vodika v sistemu. Izgube vodika znotraj mej obravnavanega sistema so pri vseh eksperimentih med 10 in 25 %, pretvorjene v energijo pa znašajo do 7 kW (pri 16,9 bar → izgube H2: 16,1 % ter pri 17,6 bar → izgube H2: 18,1 %). Pri tem se poraja vprašanje, ali bi sistem z drugim načinom napajanja demi-vode (črpalka) imel višji energijski izkoristek. Specifična raba električne energije za celoten sistem je med 6,3 in 6,5 kWh/Nm3H2. Delovanje elektrolizerja v primeru hitrih sprememb moči ima negativen vpliv na čistost proizvedenega vodika in posledično tudi na energijski izkoristek. To delo je v primerjavi z ostalimi objavami s tega področja usmerjeno širše kot zgolj na sam elektrolizer in proces elektrolize. Tako je obravnavan celoten sistem z vso periferijo (izpihovanje, čiščenje, prenos toplote, polnjenje vode itd.), ki ima svojo naravo delovanja in značilnosti. Izpostavljene so značilnosti in omejitve celotnega realnega sistema glede na znano teorijo elektrolize, podani pa so tudi ustrezni zaključki in smernice. Ključne besede: napredni energetski sistemi, obnovljivi viri energije, elektroliza bazične vodne raztopine, elektrolizer, proizvodnja vodika, energijske karakteristike

SI 116

*Naslov avtorja za dopisovanje: Univerza v Ljubljani, Fakulteta za strojništvo, Aškerčeva 6, 1000 Ljubljana, Slovenija, mitja.mori@fs.uni-lj.si

Prejeto v recenzijo: 2013-03-19 Prejeto popravljeno: 2013-05-20 Odobreno za objavo: 2013-06-20

Bočna vodljivost zaprtozančnega sistema voznika in vozila s sistemom 4WS s krmiljenjem hitrosti vrtenja okrog navpične osi Lv, H.M. – Liu, S.N. Hongming Lv* – Shaona Liu

Institut za tehnologijo v Yanchengu, Šola za avtomobilsko tehniko, Kitajska

Z vse hitrejšim razvojem globalnega transporta je vsako leto tudi več prometnih nesreč in prav zato so mnoge države okrepile razvoj sistemov aktivne varnosti v avtomobilih. Sistemi štirikolesnega krmiljenja (4WS) so bili v zadnjih 40 letih deležni veliko pozornosti podjetij in raziskovalcev zaradi potenciala za izboljšanje bočne vodljivosti vozila. Pri vozilih s 4WS lahko konvencionalna strategija krmiljenja ničelnega odklona stranskega zdrsa sicer pomaga vozniku pri nadzoru lege šasije vozila med vožnjo v ovinek, pri visokih hitrostih pa tudi močno zmanjša ojačenje hitrosti vrtenja vozila okrog navpične osi v stacionarnem stanju in zato povzroča čezmerno podkrmarjenje vozila. Spremenljivk odklona stranskega zdrsa in hitrosti vrtenja okrog navpične osi namreč ni mogoče ločiti samo z eno krmiljeno spremenljivko, t.j. s kotom odklona zadnjih koles. Cilj tega članka je preučiti zasnovo krmilnika 4WS, ki zmanjšuje dinamično napako hitrosti vrtenja okrog navpične osi in odklona stranskega zdrsa po predlaganem modelu vozila, ter vrednotenje zmogljivosti zaprtozančnega sistema, sestavljenega iz voznika in vozila s 4WS. Najprej je postavljen model bočne dinamike vozila s 4WS z dvema prostostnima stopnjama, namenjen preučitvi osnovne dinamike vozila ter poenostavitvi postopka analize. Upoštevata se samo dve spremenljivki – odklon stranskega zdrsa in hitrost vrtenja okrog navpične osi, medtem ko je vpliv nagibanja na bočno gibanje majhen in zato v tem članku ni upoštevan. V drugem koraku je zasnovan optimalen krmilnik za vozila s 4WS na osnovi sledenja hitrosti vrtenja okrog navpične osi. Predlagan je krmilni algoritem s sledenjem hitrosti vrtenja okrog navpične osi. Naloga krmiljenja je zmanjšanje harmonskih vršnih vrednosti hitrosti vrtenja okrog navpične osi in odklona stranskega zdrsa na minimum za izboljšanje bočne vodljivosti vozila pri visokih hitrostih ter odpravo čezmernega podkrmarjenja. Končno je postavljen zaprtozančni sistem voznika in vozila s 4WS po modelu preview-follower. S kombinacijo parametrov preskusa vozila in referenčnih parametrov modela voznika so bili določeni ključni parametri sistema voznika/vozila. Opravljena je bila simulacija bočne vodljivosti sistema voznika in vozila s 4WS pri menjavi voznega pasu ter pri zaviranju v sili in primerjana z lastnostmi vozila s sprednjo krmiljeno osjo (FWS). Za vrednotenje bočne vodljivosti zaprtega sistema vozilo/voznik je predlagana metoda frekvenčne analize. Pri krmiljenju obeh vozil v pogojih zaviranja v sili je odgovor vozila s FWS že zunaj stabilnega območja, medtem ko vozilo s 4WS ohranja originalne značilnosti. Vozilo s 4WS je robustno na spremembe togosti pnevmatik pri zavijanju in oprijema s cestiščem, zato zagotavlja želeno vodljivost. Predlagano vozilo s 4WS in sledenjem hitrosti vrtenja okrog navpične osi ima naslednje prednosti: lahko sledi referenčnemu odklonu stranskega zdrsa in hitrosti vrtenja okrog navpične osi, s tem pa izboljšuje bočno vodljivost vozila; in ima robustnost pri spreminjajočem se oprijemu s cestiščem in togosti pnevmatik pri zavijanju. Vozilo ohrani stabilnost tudi pri zaviranju v sili. Čeprav je frekvenčna analiza zaprtozančnega sistema voznik/vozilo šele v fazi razvoja, pa je ta metoda lahko učinkovita za preučevanje bočne vodljivosti vozila. Ključne besede: štirikolesno krmiljenje, vozilo, bočna vodljivost, zaprtozančni sistem, voznik, hitrost vrtenja okrog navpične osi

*Naslov avtorja za dopisovanje: Institut za tehnologijo v Yanchengu, Šola za avtomobilsko tehniko, 9# Xiwang Avenue, Yancheng, 224051, Kitajska, lhmyg@163.com

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Prejeto v recenzijo: 2013-01-14 Prejeto popravljeno: 2013-07-05 Odobreno za objavo: 2013-08-08

Sistem za sprotno analizo olja in delcev v olju

Salgueiro, J. – Peršin, G. – Vižintin, J. – Ivanovič, M. – Dolenc, B. José Salgueiro1,* – Gabrijel Peršin2 – Jože Vižintin1 – Matic Ivanovič3 – Boštjan Dolenc4 1 Univerza

v Ljubljani, Fakulteta za strojništvo, Slovenija Univerza, Fakulteta za inženirstvo, Velika Britanija 3 Inštitut Jožef Stefan, Oddelek za sisteme in regulacije, Slovenija 4 Univerza v Ljubljani, Fakulteta za elektrotehniko, Slovenija 2 Cranfield

Mazalno sredstvo je ključni nosilni strojni element vsakega mehanskega sistema. Za zanesljivo in trajno delovanje mehanskega sistema, je potrebno mazalno sredstvo ustrezno dimenzionirati, v uporabi pa njegovo delovanje sprotno spremljati. Nezadostno mazanje nosilnih kontaktnih površin, ki se relativno gibljejo pod obremenitvijo, lahko povzroči preobremenitev sistema, pomankanje maziva v kontaktu ali pa izgubo lastnosti maziva med obratovanjem. Sprememba lastnosti maziva in prevelika prisotnost obrabnih delcev v mazivu omogočajo neposredni kontakt med vršički nosilnih površin v tribološkem kontaktu. Posledica neposrednega kontakta na vršičkih je povečana torna toplota, obraba in poškodbe v nosilnem tribološkem kontaktu. Za oceno delovanja mazalnega sredstva v tribološkem kontaktu je potrebno sprotno spremljati količino in velikost obrabnih delcev, spremembe fizikalno-kemijskih lastnosti ter stopnjo kontaminacije maziva z delci iz okolice in drugimi fluidi kot so voda, gorivo ter ostali kontaminanti. Mazalna sredstva lahko vzdržujemo kurativno po potrebi, preventivno plansko ali napovedno po stanju. Pri kurativnem vzdrževanju lahko mazivo zamenjamo prezgodaj ali prepozno, ker so spemembe lastnosti maziva časovno odvisne od vrste maziva, načina mazanja ter okoljskih in delovnih pogojev pri katerih izbrani mehanski sistem obratuje. Napovedna tehnologija vzdrževanja ali vzdrževanje po stanju zahteva sprotno spremljanje stanja olja. Izvaja se lahko s periodično analizo maziva v laboratoriju ali med obratovanjem mehanskega sistema. Za periodično analizo maziva je potrebno ročno odvzeti vzorce maziva na predpisani lokaciji in v naprej dogovorjenih intervalih med mirovanjem mehanskega sistema. Postopek odvzema vzorca olja je natančno predpisan. Odvzeti vzorec maziva mora zagotavljati reprezentativnost stanja celotne količine maziva v sistemu in obratovalnih pogojev. Problem je predvsem pri izbiri lokacije, delovne temperature ter časa in načina odvzema vzorca maziva. Razviti sistem za sprotno analizo olja in delcev v olju omogoča zajem in analizo parametrov za oceno stanja ter sprotno analizo trendov, s ciljem diagnostike in prognostike stanja maziva ter mazanih komponent stroja ali naprave. Sistem lahko deluje kot samostojna enota, ki preko razvitega uporabniškega vmesnika komunicira z operaterji, ali kot del večjega diagnostičnega sistema, ki združuje več različnih diagnostičnih tehnik in tehnologij. Okolje za analizo zajetih podatkov vsebuje grafični vmesnik, ki omogoča komunikacijo med diagnostičnim sistemom in operaterjem oziroma strokovnjakom za vzdrževanje. Vmesnik prikazuje zgodovino izmerkov in trenutno stanje posameznega parametra. Po nastanku spremembe, vmesnik generira poročilo, ki vsebuje vse vrednosti izmerjenih parametrov olja in delcev v olju, rezultate analize stanja in napoved verjetnosti za nastanek sprememb stanja. Vse podatke sistem shrani na strežnik v centru vzdrževanja. Stanje posameznega parametra je ovrednoteno kot “dobro”, “opozorilno” in “kritično”, končna ocena stanja sistema pa temelji na tehnikah razpoznavanja vzorcev. Rezultati, ki smo jih dobili s preizkusi so pokazali, da je avtomatsko zaznavanje sprememb v olju dosegljivo z manjšim zamikom. Sistem je uspešno zaznal kontaminacijo reduktorskega olja s hidravličnim oljem kljub temu, da je bila koncentracija kontaminanta relativno nizka, približno 2,5% in tudi prisotnost vode v olju. Uspešno je zaznal jamičenje zobnih bokov zobniškega para v začetnem stanju kar je bilo tudi potrjeno z vizualnim pregledom zobnih bokov. Sistem se lahko uporablja za diagnosticiranje lastnosti olja in obrabnih delcev v olju na mehanskih prenosnikih moči in v hidravličnih sistemih. Ključne besede: napovedno vzdrževanje, spremljanje stanja, sprotna analiza olja in delcev v olju, diagnostika, monitoring

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*Naslov avtorja za dopisovanje: Univerza v Ljubljani, Fakulteta za strojništvo, Bogišičeva 8, 1000 Ljubljana, Slovenija, jose.salgueiro@tint.fs.uni-lj.si

Prejeto v recenzijo: 2012-11-26 Prejeto popravljeno: 2013-04-12 Odobreno za objavo: 2013-06-20

Parametrična optimizacija odgonske gredi portalne preme z analizo po metodi končnih elementov Jong Boon

Ooi1

Ooi, J.B. –Wang, X. – Lim, Y.P. – Tan, C.S. – Ho, J.H. – Wong, K.C. – Xin Wang2,* – Ying Pio Lim1 – ChingSeong Tan3 – Jee-Hou Ho4 – Kok-Cheong Wong4 1Univerzitetni

kolidž Tunku Abdul Rahman, Malezija Monash, Kampus Sunway, Malezija 3Univerza za multimedije, Malezija 4Univerza v Nottinghamu, Kampus v Maleziji, Malezija 2Univerza

Namen članke je priprava zasnove odgonske gredi portalne preme z izboljšano vzvojno trdnostjo ter optimizacija parametrov odgonske gredi portalne preme. Portalna prema z gonili je enota, ki se vgrajuje v vozila zaradi večje oddaljenosti od tal za terensko vožnjo. Portalne preme imajo pomembno vlogo v motošportu, saj dajejo vozilom z vsekolesnim pogonom potrebno oddaljenost od tal za vožnjo po terenu. Portalne preme so med vožnjo po terenu pogosto izpostavljene udarcem in preobremenitvam, ki lahko sčasoma privedejo do loma gredi. Pri konstruiranju gredi za gonila portalnih prem je zato potrebna posebna zasnova po meri izjemno zahtevnih delovnih pogojev. Gredi portalne preme morajo imeti izjemno veliko trdnost, hkrati pa morajo biti lahke za večjo zanesljivost in zmogljivost. Torzijski preskusi gredi portalne preme so dragi in zahtevajo veliko časa za pripravo. Zato je predstavljena zasnova optimizirane odgonske gredi portalne preme z izboljšanimi vzvojnimi lastnostmi, ki je bila pripravljena z analizo po metodi končnih elementov. V članku je predlagana votla gred z rebri na obeh koncih. V članku je predstavljen tridimenzionalni CAD-model gredi in priprava analize s programsko opremo ANSYS FEA za vrednotenje torzijske gredi z ozirom na stroškovno učinkovitost, manjšo porabo časa in uporabo v manjših delavnicah. Razmerje med trdnostjo in težo votlih gredi portalne preme je izboljšano s parametrično optimizacijo. Za določitev optimalnega nabora konstrukcijskih parametrov za največje razmerje med trdnostjo in težo votle gredi je bila uporabljena metoda Taguchi. Tridimenzionalni model vzvojnih obremenitev gredi je bil določen z analizo po metodi končnih elementov (FEA) in validiran z eksperimentalnim preskusom. 3D-model gredi, razvit po metodi FEA, lahko učinkovito napove vzvojno trdnost gredi. Debelina votle gredi je dominanten parameter, ki močno vpliva na vzvojno trdnost votle gredi z rebrom. Optimalen nabor parametrov za votlo gred z rebrom je bil uspešno določen s postopkom L25 Taguchi OA. Optimizirana gred ima največje razmerje med trdnostjo in maso: v primerjavi z referenčno gredjo je trdnost večja za 13,77, za 20 % pa je večja tudi masa. Postopek L25 Taguchi OA je primeren za določanje optimalnih parametrov votle gredi z rebrom. Pri analizi vzvojne trdnosti rebra se obravnava pet parametrov: debelina votle gredi, debelina rebra, globina prečk, polmer zaokrožitve reber in število prečk. Optimalen nabor parametrov predlagane gredi je bil določen z ortogonalnim poljem Taguchi L25. Trdnost in masa optimiziranega modela sta bili izračunani in primerjani s polno gredjo, votlo gredjo in predlaganim modelom. Optimizirani model prinaša izboljšanje vzvojne trdnosti ob nekoliko večji masi glede na referenčni model. Raziskave zasnov gredi portalnih prem so še v povojih. S poglobljenim raziskovalnim delom pa bi bilo mogoče še dodatno izboljšati mehansko trdnost gredi. 1. Pri vrednotenju vzvojne trdnosti gredi je s simulacijo FEA vseh elementov gonila, kot so ležaji in pogonska gred, mogoče priti do bolj realističnih rezultatov. 2. Modalna analiza in vibracijska analiza gredi portalne preme s simulacijo FEA. 3. Raziskave na področju utrujenostnih odpovedi in cikličnih obremenitev za preučitev obrabnih lastnosti gredi v delovnih pogojih. Razviti model FEA votle gredi z rebrom in parametrična optimizacija rebra po metodi Taguchi dajeta učinkovito rešitev za optimizacijo konstrukcijskih parametrov modela gredi, ki prinaša občutno izboljšanje vzvojne trdnosti gredi. Predstavljena metodologija za določanje vzvojnih obremenitev gredi in nabor optimalnih parametrov sta na voljo konstruktorjem kot izhodišče za optimizacijo konstrukcije gredi. Ključne besede: parametrična optimizacija, analiza po metodi končnih elementov, zasnova gredi, portalna prema, votla gred, metoda Taguchi, zasnova eksperimenta

*Naslov avtorja za dopisovanje: Univerza Monash, Kampus Sunway, Jalan Lagoon Selatan,46150 Bandar Sunway, Selangor Darul Ehsan, Malaysia. wang.xin@monash.edu

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Prejeto v recenzijo: 2012-12-18 Prejeto popravljeno: 2013-04-18 Odobreno za objavo: 2013-06-20

Eksperimentalna raziskava aktivnega vzmetenja cestnih vozil Krishnasamy, P. – Jayaraj, J. – John, D. Prabu Krishnasamy* – Jancirani Jayaraj – Dennie John Univerza Anna, Oddelek za avtomobilsko tehniko, Indija

Glavni cilj študije je zasnova in razvoj hibridnega vzmetenja ter analiza njegove primernosti za uporabo v potniških vozilih pri cestnih razmerah, ki prevladujejo v Indiji. S študijo naj bi se izboljšala tako udobje potnikov in lega vozila na cesti, kakor tudi določil delovni prostor sistema vzmetenja s predlagano izpopolnjeno Ziegler-Nicholsovo proporcionalno-integralno-diferencialno (RZNPID) zasnovo krmiljenja. Simulacija in eksperimentalna študija sta bili usmerjeni v opazovanje treh parametrov za doseganje teh ciljev: pospešek potnikov, pospešek šasije vozila in hod vzmetenja. Razvit je bil primeren matematični model za hibridno vzmetenje, ki upošteva tudi sistem potniških sedežev. Matematični model sistema je po drugem Newtonovem zakonu opredeljen z diferencialno enačbo drugega reda. Simulacijski model je bil razvit in preskušen na podlagi matematičnega modela sistema. Zmogljivost sistema hibridnega vzmetenja, razvitega v tej študiji, je bila primerjana z zmogljivostjo pasivnega sistema v okolju LABVIEW. Rezultati simulacije so bili potrjeni z eksperimentalno analizo. Preskuševališče je sestavljeno iz jeklenega ogrodja z vertikalnimi vodili za vodenje vzmetenih in nevzmetenih mas s pomočjo linearnih ležajev. Set vijačnih vzmeti na ustreznih mestih zagotavlja togost vzmetenja in pnevmatik. Za vzbujanje sistema skrbi odmikač, pritrjen na sestav motorja in reduktorja. V simulaciji in v eksperimentu je bila zabeležena vršna vrednost pospeška potniškega sedeža in izkazalo se je, da daje predlagano krmiljenje sistema očitno boljši rezultat pri pospešku voznikovega sedeža. Ugotovljen je bil tudi najmanjši pospešek šasije pri predlaganem sistemu ter največji pospešek šasije pri pasivnem sistemu. Občutno zmanjšanje pospeškov v sistemu pomeni tudi več udobja pri vožnji. Gibi vzmetenja so znotraj omejitev. Večji hod ima vzmetenje pasivnega sistema, predlagani sistem pa se bolje odreže tudi po tem kriteriju. Hod pnevmatik je bil majhen v simulaciji in nekoliko večji pri eksperimentalni preiskavi predlaganega sistema. Najmanjši pospešek potniškega sedeža zagotavlja Ziegler-Nicholsov izpopolnjeni krmilni algoritem. Na osnovi rezultatov simulacije in preskusov je mogoče sklepati, da algoritem RZN učinkovito krmili sistem pnevmatskega vzmetenja. Vsi ostali parametri so znotraj sprejemljivih meja. Čeprav je gib pnevmatik večji, pa v primerjavi s pasivnim sistemom traja le zelo kratek čas in s tem se izboljša lega na cesti, ko vozilo prevozi grbino. Sistem aktivnega pnevmatskega vzmetenja s krmilnikom RZNPID torej izboljša udobje med vožnjo, hkrati pa ohranja lego na cesti sistema pasivnega vzmetenja. Študija bo v prihodnje razširjena z raziskavo vedenja pri različnih amplitudah cestnih neravnin in pri različnih hitrostih. Vzmetenje z izboljšano zmogljivostjo bo tudi praktično preskušeno v lahkem potniškem vozilu. Ključne besede: PID, pnevmatski izvršni člen, hibridno vzmetenje, aktivno krmiljenje, izpopolnjeno Ziegler-Nicholsovo krmiljenje

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*Naslov avtorja za dopisovanje: Oddelek za avtomobilsko tehniko, Kampus M.I.T, Univerza Anna, Chennai-44, Indija

Prejeto v recenzijo: 2013-04-12 Prejeto popravljeno: 2013-07-09 Odobreno za objavo: 2013-08-20

Vpliv trdote na obdelavo z abrazivnim tokom Gov, K. – Oer Eyercioglu, O. – Cakir, M.V. Kursad Gov1,* – Omer Eyercioglu1 – Mehmed Veysel Cakir2 1Univerza

v Gaziantepu, Oddelek za strojništvo, Turčija Kilis, Visoka poklicna šola v Kilisu, Turčija

2Univerza

Članek obravnava vpliv trdote obdelovanca na postopek obdelave z abrazivnim tokom. Obdelava z abrazivnim tokom (AFM) je postopek odvzemanja materiala obdelovanca s tokom abrazivnega medija pod tlakom. Postopek AFM v primerjavi z drugimi tehnikami poliranja zelo učinkovito obdela tudi zahtevnejše notranje površine. V članku je preučen vpliv trdote obdelovanca na postopek AFM. Eksperimentalna študija je bila opravljena s tremi skupinami obdelovancev iz orodnega jekla AISI D2 (kaljenimi na 31, 45 in 55 HRC), izrezanimi z žično elektroerozijo. Izmerjena je bila površinska hrapavost in narejeni so bili posnetki površine z vrstičnim elektronskim mikroskopom pred in po posameznih korakih obdelave AFM. Opazovano je bilo izboljšanje površine v odvisnosti od trdote. Obdelovanci so bili vpeti in nato je bil vzpostavljen tok polirnega medija preko elektroerozijsko obdelanih površin odprtine dimenzij 2×20 mm. V vsakem ciklu se je pretočilo 2000 g polirnega medija, opravljeni pa so bili eksperimenti z 1, 3, 5, 10, 20 in 100 cikli. Tlak AFM je bil 10 MPa in pretok 50 g/s. Eksperimenti so bili ponovljeni trikrat z obdelovanci v različnih stanjih in nato je bilo izračunano povprečje 18 meritev površinske hrapavosti. Postopek AFM v par ciklih učinkovito odstrani belo plast, ki se oblikuje med elektroerozijsko obdelavo. Z belo plastjo se odpravijo tudi površinske razpoke in se poveča trajna dinamična trdnost. AFM izboljša kakovost površine pri vseh skupinah trdote. Rezultati kažejo, da se površinska hrapavost elektroerozijsko obdelanih površin občutno spremeni v prvih 20 ciklih, nato pa se postopoma ustali. Površinska hrapavost se po 50 ciklih nekoliko zmanjša. Čeprav je trend meritev površinske hrapavosti pri vseh skupinah trdote podoben, pa rezultati kažejo, da je izboljšanje površine večje pri trših kot pri mehkejših materialih. Prispevek, novosti, vrednost: V literaturi je mogoče najti samo eno študijo vpliva trdote obdelovanca pri postopkih AFM [2]. V študiji [2] pa sta bila za predstavnika trdega in mehkega materiala izbrana medenina in aluminij. V tem članku je predstavljen vpliv trdote obdelovanca na postopek AFM. Ključne besede: obdelava z abrazivnim tokom, površinska hrapavost, žična elektroerozijska obdelava

*Naslov avtorja za dopisovanje: Univerza v Gaziantepu, Oddelek za strojništvo, 27360, Gaziantep, Turčija, gov@gantep.edu.tr

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Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, SI 122-130 Osebne objave

Doktorske disertacije, znanstvena magistrska dela, specialistična dela, diplomska dela

DOKTORSKE DISERTACIJE Na Fakulteti za strojništvo Univerze v Ljubljani so obranili svojo doktorsko disertacijo: ● dne 6. septembra 2013 Martin ČESNIK z naslovom: »Strukturna dinamika pri vibracijskem utrujanju« (mentor: prof. dr. Miha Boltežar, somentor: izr. prof. dr. Janko Slavič); V raziskavi je predstavljena numerična in eksperimentalna analiza vpliva strukturne dinamike pri vibracijskem utrujanju. Predstavljena in numerično potrjena je nadgrajena SMURF metoda za izračun odziva strukture pri kinematskem vzbujanju. Nadalje je predstavljena metodologija pospešenega preizkusa utrujanja za pridobitev Wöhlerjeve krivulje na podlagi dinamskega odziva sistema. Eksperimentalno je potrjena konservativna ocena življenjske dobe zaradi sprememb dinamskega odziva pri vzbujanju z naključnim signalom. Na primeru kovičenega spoja je predstavljena tudi metodologija pridobitve parametrov utrujanja pri vzbujanju dinamskega sistema z naključnim signalom; ● dne 18. septembra 2013 Marko ŠIMIC z naslovom: »Karakterizacija in modeliranje hidravličnega digitalnega piezoventila« (mentor: izr. prof. dr. Niko Herakovič); Pri visoko-dinamičnih procesih, krmiljenih s hidravličnimi krmilji, so ključnega pomena krmilni ventili in njihove dinamične lastnosti. Klasični hidravlični servoventili, kljub nenehnemu optimiranju konstrukcijskih elementov ter krmilnih algoritmov, v takšni zasnovi in obliki ne morejo popolnoma zadostiti zahtevam po visokih dinamičnih lastnostih. V doktorski disertaciji so zato podrobno obravnavani in analizirani najpomembnejši vplivni parametri, ki lahko občutno izboljšajo dinamiko ventilov ob hkratnem zmanjšanju rabe energije. Posebej je obravnavana možnost uporabe baterije digitalnih hidravličnih piezoventilov, kot alternativne zamenjave servoventilom. Zaradi kompleksnosti ventilskega sistema so v nalogi v ločenih korakih obravnavani posamezni sklopi baterije digitalnih ventilov, v katerih so zajeti tako analitični pristopi, kot tudi modeliranje, simulacija in eksperimentalna analiza. Osnova za izdelavo matematičnih in simulacijskih modelov je izhodiščna idejna zasnova digitalnega hidravličnega ventila s piezo-aktuatorskim sistemom za krmiljenje posameznega preklopnega ventila, kakor tudi posameznih segmentov. Rezultati simulacije so podrobneje analizirani in primerjani z rezultati eksperimentalne analize, ugotovitve pa so uporabljene SI 122

za izboljšavo matematičnih modelov. Pomemben del doktorske naloge je tudi razvoj prototipa preklopnega ventila s podrobno analizo in minimiziranjem mrtvih volumnov tlačnih komor ventila. Posebej je obravnavana CFD analiza z namenom zmanjševanja tokovnih sil na krmilni bat ventila in zmanjšanja njegove mase, kar bistveno vpliva na izboljšanje dinamičnih lastnosti ventila. Velik poudarek je podan razvoju krmilne elektronike in krmilnega algoritma digitalnega piezoventila, ki sta bistvenega pomena za doseganje kratkega odzivnega časa piezoventila. Po podrobni teoretični in eksperimentalni analizi novega ventila, ki dosega odzivni čas pod 0,2 ms, je narejena primerjava rezultatov statičnih in dinamičnih karakteristik novega digitalnega hidravličnega ventila z obstoječimi hidravličnimi visoko-dinamičnimi preklopnimi, proporcionalnimi in servoventili; ● dne 18. septembra 2013 Marko LANGERHOLC z naslovom: »Metoda absolutnih vozliščnih koordinat v nelinearni dinamiki gibajočega se dvodimenzionalnega kontinuuma« (mentor: prof. dr. Miha Boltežar, somentor: izr. prof. dr. Janko Slavič); Raziskava v prvem delu predstavi eksperimentalno karakterizacijo magnetostrikcije elektropločevine. Preizkuševališče, izdelano v ta namen, omogoča meritev magnetostrikcije v širokem frekvenčnem pasu brez vpliva lastne dinamike sistema. Analiza pojava je izvedena v časovni in v frekvenčni domeni, za posamezne frekvenčne komponente je vpeljana potenčna aproksimacija. V drugem delu je predstavljen pristop k numerični analizi magnetostriktivno samovzbujene dinamike lameliranih struktur iz elektropločevine. Souporaba veljavnega strukturnega modela in vpeljanega modela magnetostrikcije omogoča obravnavo magnetostriktivnih vibracij v ravnini pločevine in v njeni normalni smeri; ● dne 24. septembra 2013 Matija JAVORSKI z naslovom: »Magnetostriktivno samovzbujena dinamika lameliranih struktur« (mentor: prof. dr. Miha Boltežar, somentor: izr. prof. dr. Janko Slavič); Raziskava v prvem delu predstavi eksperimentalno karakterizacijo magnetostrikcije elektropločevine. Preizkuševališče, izdelano v ta namen, omogoča meritev magnetostrikcije v širokem frekvenčnem pasu brez vpliva lastne dinamike sistema. Analiza pojava je izvedena v časovni in v frekvenčni domeni, za posamezne frekvenčne komponente je vpeljana potenčna aproksimacija. V drugem delu je predstavljen pristop k numerični analizi magnetostriktivno samovzbujene dinamike

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, SI 122-130

lameliranih struktur iz elektropločevine. Souporaba veljavnega strukturnega modela in vpeljanega modela magnetostrikcije omogoča obravnavo magnetostriktivnih vibracij v ravnini pločevine in v njeni normalni smeri; ● dne 25. septembra 2013 Lovro KUŠČER z naslovom: »Sistemi za določitev položaja in karakterizacijo gibanja oddaljenih objektov« (mentor: prof. dr. Janez Diaci); Doktorska disertacija obravnava razvoj in eksperimentalno delo s sistemi za določitev položaja oddaljenih objektov. V tem okviru sta bila razvita dva eksperimentalna merilna sistema, ki temeljita na komercialno dostopnih merilnih napravah. Za ovrednotenje karakteristik merilnih sistemov je bila izvedena analiza na osnovi simulacij ter terenskih meritev. V nadaljevanju razvoja je bil eden izmed razvitih merilnih sistemov nadgrajen z uporabo računalniškega vida, s čimer je bilo omogočeno ponavljajoče se izvajanje meritev položaja in s tem karakterizacija gibanja oddaljenega objekta v realnem času. Delovanje je bilo preizkušeno na primeru merjenja trajektorij vozil in ljudi. Razviti pristop karakterizacije gibanja je bil prirejen tudi za novo aplikacijo optičnega merjenja premikov in deformacij v industrijskem okolju; ● dne 26. septembra 2013 Domen ROVŠČEK z naslovom: »Normiranje lastnih oblik v dinamiki majhnih in lahkih struktur na osnovi občutljivostne analize« (mentor: prof. dr. Miha Boltežar, somentor: izr. prof. dr. Janko Slavič); Raziskovalno delo obravnava normiranje lastnih oblik majhnih in lahkih struktur na osnovi občutljivostne analize. Delo se osredotoča na odpravo težav, ki se pojavijo pri meritvah modalnih parametrov struktur majhnih mas in dimenzij (strukturi dodana masa, visoke lastne frekvence, itd.). Opravljen je pregled obstoječih metod za merjenje modalnih parametrov v okviru eksperimentalne in obratovalne modalne analize. Bolj podrobno so obravnavane metode, ki so primerne za majhne in lahke strukture. Analizirano je masno normiranje obratovalnih lastnih oblik na osnovi občutljivostne analize. V delu so predstavljene tri inovativne metode, ki so bile razvite za določanje modalnih parametrov majhnih in lahkih struktur. S primerjavo rezultatov meritev in numeričnega modela je potrjeno pravilno delovanje teh treh metod, pri čemer je največ pozornosti posvečene masnemu normiranju lastnih oblik; ● dne 27. septembra 2013 Jurij GREGORC z naslovom: »Dinamika stičnih struktur toka plina in kapljevine v razdelilniku mini dimenzij« (mentor: prof. dr. Iztok Žun); Obravnavan je bil primer ločevanja dvofaznega toka v razdelilniku mini dimenzij s tremi zaporednimi

T-spoji. Meritve so bile opravljene pri različnih kombinacijah pretokov plina in kapljevine. V razvodnem kanalu so bili prepoznani naslednji tokovni vzorci: mehurčkasti tok, mehurčkastočepasti tok, čepasti tok, semi-kolobarjasti tok in razpenjeni tok. Opaženi so bili sistematični trendi ločevanja v odvisnosti od tokovnega vzorca na T-spoju. Numerično modeliranje z metodo VOF je bilo izbrano za napovedovanje ločevanja faz. V prvi vrsti je bilo potrebno razviti algoritem, ki popisuje vstopne pogoje in omogoča napovedovanje relevantnih struktur faznega stika. V ta namen je bila opravljena eksperimentalna raziskava dvofaznega toka v cevki mini dimenzij. Na podlagi eksperimentalne raziskave so bili prepoznani ključni parametri nastajanja tokovnega vzorca. Slednji so bili uporabljeni pri razvoju algoritma, ki posnema eksperimentalne vstopne pogoje. Tako je možno napovedati različne tokovne vzorce samo s spreminjanjem pretokov. Validacija, ki bila opravljena z eksperimentalnimi podatki, kaže na dobro ujemanje tudi pri napovedovanju prehoda med mehurčkastim in čepastim tokom. Predstavljeni algoritem za popis vstopnih pogojev je bil uporabljen tudi pri simulacijah ločevanja faz za primer mehurčkastega in čepastega tokovnega vzorca v razvodnem kanalu. Ujemanje z eksperimentom je dobro. Na podlagi rezultatov prikazanega numeričnega pristopa je možna obravnava ravnotežja sil na T-spoju, ki opredeljuje ločevanje faz; ● dne 27. septembra 2013 Izidor SABOTIN z naslovom: »Razvoj mikrostrukturnega sistema za sintezo ionskih tekočin z vidika elektroerozijske obdelave« (mentor: doc. dr. Joško Valentinčič, somentorja: prof. dr. Mihael Junkar, izr. prof. dr. Polona Žnidaršič – Plazl (UL, FKKT)); Miniaturizacija izdelkov in njihovih komponent predstavlja trend na mnogih področjih uporabe in v zadnjem desetletju močno narašča. Razvoj na področju mikroinženirskih izdelovalnih tehnologij omogoča fleksibilno izdelavo mikrostruktur iz različnih materialov in konkurirajo uveljavljenim mikrosistemskim tehnologijam, ki temeljijo na obdelavi silicija. Eno izmed aktualnih področij uporabe predstavljajo mikroreaktorske tehnologije. Mikroreaktor je mikrostrukturni sistem, ki omogoča kemijske in biokemijske procese v strukturah mikrometrskih dimenzij. V tem delu smo se osredotočili na razvoj mikroreaktorskega sistema za uporabo v sintezi ionskih tekočin z vidika mikroinženirskih izdelovalnih tehnologij in vključevanja posamezne enote v mini-tovarno. Predstavili smo konceptualni model sistema načrtovanja izdelovalnih procesov za mikroizdelke, ki vključuje identificirane specifičnosti mikroinženirskih tehnologij. Mikromešalnik je SI 123

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, SI 122-130

ključna funkcionalna enota mikroreaktorskega sistema. Z uporabo numeričnih simulacij smo določili mikromešalnik z utori kot najprimernejšo geometrijo za implementacijo v mikroreaktorski sistem. Identificirali smo ključne dejavnike mešanja in postavili metodologijo za optimizacijo mikromešalnika z utori. S preizkusi smo verificirali rezultate optimizacije z numeričnimi simulacijami. Pokazali smo, da je tehnologija mikro EDM dolbenja primerna za izdelavo mikrostrukturiranega reaktorja in za to tehnologijo razvili tehnološki model. Z uporabo tehnološkega modela smo določili optimalno geometrijo mikromešalnika glede na tehnologijo in s tem dokazali primernost predlagane metodologije optimizacije mikromešalnika. Mikromešalnik smo postavili v mikroreaktorski sistem in eksperimentalno potrdili primernost konstrukcije za uporabo v sintezi ionskih tekočin. V sklopu dela smo razvili izdelovalno verigo procesov za izdelavo mikroreaktorske monomere in način njenega povezovanja po konceptu povečevanja števila (angl. ‘numbering up’). * Na Fakulteti za strojništvo Univerze v Mariboru so obranili svojo doktorsko disertacijo: ● dne 10. septembra 2013 Matej STEINACHER z naslovom: »Kovinsko-keramični material z infiltrirano magnezijevo zlitino« (mentor: prof. dr. Franc Zupanič); V tem delu je obravnavan kovinsko-keramični material z infiltrirano magnezijevo zlitino (kompozit IPC; ang. interpenetrating phase composite). Preiskovani kompozit IPC je bil izdelan s postopkom gravitacijskega kokilnega litja, karakteriziran s svetlobno mikroskopijo, vrstično in presevno elektronsko mikroskopijo, energijsko disperzijsko spektroskopijo in rentgensko difrakcijo ter bil mehansko preiskan. Osnova kompozita, magnezijeva zlitina AE44, ki je vsebovala 4,94 mas. % Al in 4,42 mas. % kovin redkih zemelj (RE), je bila sestavljena iz primarnih kristalov večkomponentne trdne raztopine ?-Mg in intermetalnih faz Al11RE3, Al2RE in Al10RE2Mn7. Utrjevalna sestavina kompozita, keramična pena, je bila sestavljena iz ?-Al2O3, ?-SiC, ß-SiC in SiO2. Keramična pena je imela odprto primarno in večinoma zaprto sekundarno poroznost. Pri litju je talina infiltrirala v primarne pore, v sekundarne pore pa se je delno infiltrirala, delno pa penetrirala skozi mostičke keramične pene. V mejnih območjih med zlitino AE44 in keramično peno se je pojavila močna reakcija, ki je vplivala na mikrostrukturo nastalega kompozita IPC, zato je bilo največ dela usmerjenega v natančno opredelitev mehanizmov in kinetike kemijskih reakcij v mejnih SI 124

območjih. V ta namen so bili izvedeni poskusi litja z različnimi parametri, modelni preskusi interakcij tudi s kompaktno keramiko ter teoretični študij termodinamskih reakcij. Glavni reakcijski produkti v mejnih območjih AE44 - keramična pena in v penetriranih mostičkih keramične pene so bili MgO, AlSiRE in AlMgSiRE. Kot prvi je nastal MgO z redukcijo SiO2 in Al2O3 z magnezijem. Nato je na MgO nastala faza AlSiRE, na kateri je kasneje epitaksialno kristalizirala faza AlMgSiRE, katere delež se je z daljšim reakcijskim časom povečeval. Z mikrokemijsko analizo je bila opredeljena njuna kemijska sestava, dognano pa je bilo tudi, da imata fazi tetragonalno kristalno strukturo s povsem jasno medsebojno kristalografsko orientacijo. Ugotovljeno je bilo, da fazi AlSiRE in AlMgSiRE ne ustrezata nobeni znani fazi. Kompozit IPC je imel v vseh preiskanih stanjih večjo napetost tečenja in modul elastičnosti ter manjšo upogibno in tlačno trdnost kot zlitina AE44. Faza AlMgSiRE je imela manjšo trdoto in manjši modul elastičnosti od faze AlSiRE; ● dne 20. septembra 2013 Janez EKART z naslovom: »Razvoj metodologije in postopkov proizvodnje trdnih goriv iz odpadkov« (mentor: prof. dr. Niko Samec); Na področju ravnanja z odpadki smo v zadnjem času priča vse večjim zahtevam za pravilno ravnanje z odpadki, katerih cilj je snovna in energijska izraba odpadkov z minimiziranjem ostanka na odlagališčih. Ostanek odpadkov po razvrščanju, ki ga ni mogoče snovno izrabiti, istočasno pa ga ni dovoljeno odlagati na odlagališča zaradi prevelike energijske vrednosti in vsebnosti skupnega biorazgradljivega ogljika, je po hierarhiji ravnanja z odpadki edina možna rešitev. V doktorski nalogi je bilo v obsegu raziskav izvedenih vrsto laboratorijskih analiz vzorcev frakcij nenevarnih odpadkov in analiz za pripravo referenčnih vzorcev trdnega goriva iz nenevarnih odpadkov. Naloga obsega razvoj uporabe mešalnega modela, s katerim lahko poljubno spreminjamo kakovost trdnega goriva v svojstvu njegovih kemijskih in fizikalnih lastnosti, pri čemer uporabimo iteracijo različnih vzorcev vhodnih odpadnih materialov z določenimi deleži in različnimi kemičnimi in fizikalnimi lastnostmi. Razviti mešalni model omogoča hitre izračune porabe posameznih odpadnih materialov in napoved razvrstitve trdnega goriva na osnovi deležev posameznih odpadnih materialov v kakovostne razrede. Je osnova za določitev približne kakovosti trdnega goriva in posledično razreda trdnega goriva. Hkrati omogoča, da proizvajalec trdnega goriva ugotovi, ali proizvedeno trdno gorivo ustreza zahtevam uporabnika, kar bo v bodoče za proizvajalce trdnih goriv iz odpadkov izjemnega pomena. Z njegovo uporabo so dane možnosti za

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, SI 122-130

optimiranje kakovosti trdnih goriv iz razpoložljive mase odpadkov pri proizvajalcu. Priprava vzorcev trdnega goriva in izvedba meritev elementarnih sestavin nekovinskega in kovinskega dela v trdnem gorivu v laboratoriju pred njegovim sežigom ter podatki njihovih emisijskih vrednosti pri zgorevanju v pilotni kurilni napravi so z vidika raziskovanja pomembna ugotovitev, kako pomemben je proces sežiga trdnega goriva pri zadostni zgorevalni temperaturi, zadrževalnem času in prisotnosti kisika ter kakšna je sledljivost kovinskih elementov pri sežigu v plinastem in trdnem agregatnem stanju. Sicer so z vidika obratovalnih parametrov kurilnih naprav najpomembnejši anorganski parametri dušik, klor, fluor in žveplo, medtem ko je z okoljskega vidika zelo pomembna vsebnost težkih kovin v dimnih plinih, še posebno v primeru, ko je trdno gorivo pridobljeno iz odpadkov, namenjeno uporabi v konvencionalnih kurilnih napravah, ki nimajo visoko zmogljivega sistema čiščenja plinov, kot to imajo denimo sežigalnice. Emisijske vrednosti pri sežigu vzorcev trdnega goriva so pokazale sorazmerno veliko prekoračitev skupnega prahu v primerjavi s parametri, ki veljajo za srednje in velike kurilne naprave ter sežigalnice. V primeru sežiga ali sosežiga trdnega goriva je zato nujno potrebna vgradnja sistema za odstranjevanje trdnih delcev. Glavni delež nastalih hlapnih organskih snovi so tvorili vodik, metan in ogljikov monoksid skupaj s primarno nastalim ogljikovim dioksidom, manjši delež pa ogljikovodiki (cca 10%). Z okoljskega in tehnološkega vidika je izjemno pomemben klor, ki na eni strani povzroča tvorbo kislih komponent v emisijah, tehnološko pa je neugoden zaradi korozije v kurilnih napravah, zato je primerna uporaba trdnega goriva z nizko vsebnostjo klora, v nasprotnem primeru pa je nujna dogradnja sistema za čiščenje kislih dimnih plinov. Izmerjene koncentracije polikloriranih dibenzodioksinov in dibenzofuranov dajejo jasno zahtevo za skrbno vodenje procesa zgorevanja trdnih goriv iz odpadkov, in sicer tako da trdno gorivo popolnoma zgori, ter kažejo, da je potrebno proces zgorevanja trdnih goriv iz odpadkov voditi izjemno skrbno, tako da popolnoma zgorijo in da preprečimo njihov nastanek. Pepel od sežiga referenčnih vzorcev trdnega goriva je mogoče brez težav odložiti na odlagališče nenevarnih odpadkov, saj je bila večina analiziranih vrednosti daleč pod mejnimi vrednostmi, ki veljajo za ta odlagališča.

SPECIALISTIČNA DELA Na Fakulteti za strojništvo Univerze v Mariboru je z uspehom zagovarjal svoje magistrsko delo: ● dne 30. septembra 2013 Klemen KOPAČ z naslovom: »Primerjava toplotnih lastnosti ploščatih sončnih kolektorjev s sončnimi kolektorji s toplotno cevjo« (mentor: izr. prof. dr. Jure Marn); ● dne 30. septembra 2013 Toni NOVAK z naslovom: »Ocena uporabnosti vodnega prenosa tiska na malih gospodinjskih aparatih« (mentor: izr. prof. dr. Bojan Dolšak). DIPLOMSKA DELA Na Fakulteti za strojništvo Univerze v Ljubljani so pridobili naziv univerzitetni diplomirani inženir strojništva: dne 3. septembra 2013: Mladen KRČIĆ z naslovom: »Izboljšava orodja za brizganje tuljavnikao« (mentor: doc. dr. Davorin Kramar, somentor: prof. dr. Janez Kopač); Anamarija MIKLIČ z naslovom: »Izobraževanje in preverjanje usposobljenosti varilcev« (mentor: prof. dr. Janez Tušek); Sebastjan ŠTARKEL z naslovom: »Študija kalibracije valjanja palic« (mentor: izr. prof. dr. Tomaž Pepelnjak). * Na Fakulteti za strojništvo Univerze v Ljubljani je zagovarjal svoje diplomsko delo na univerzitetnem študijskem programu strojništva: dne 3. septembra 2013: Carlos VILLAPLANA VELASCO z naslovom: »Algoritem za korekcijo lokalne vremenske napovedi / Algorithm for correction of local site weather forecast « (mentor: prof. dr. Sašo Medved). * Na Fakulteti za strojništvo Univerze v Mariboru so pridobili naziv univerzitetni diplomirani gospodarski inženir: dne 26. septembra 2013: Uroš TRUPEJ z naslovom: »Uvajanje računalniško podprte proizvodnje« (mentor: prof. dr. Jože Balič, somentor: prof. dr. Vojko Potočan).

SI 125

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, SI 122-130

* Na Fakulteti za strojništvo Univerze v Ljubljani je pridobil naziv magister inženir strojništva: dne 3. septembra 2013: Margerita FLORJANČIČ z naslovom: »Zmanjšanje izmeta pri procesu litja z metodami obvladovanja kakovosti« (mentor: doc. dr. Davorin Kramar, somentor: prof. dr. Janez Kopač). * Na Fakulteti za strojništvo Univerze v Ljubljani je zagovarjal svoje magistrsko delo na magistrskem študijskem programu 2. stopnje Strojništvo: dne 3. septembra 2013: Javier GAVILÁN MORENO z naslovom: »Model toplotnega odziva netermostatiranih stavb / Thermal response model of free-running buildings« (mentor: prof.dr. Sašo Medved, somentor: doc. dr. Ciril Arkar). * Na Fakulteti za strojništvo Univerze v Mariboru je pridobil naziv magister inženir strojništva: dne 26. septembra 2013: Tadej NOVAK z naslovom: »Izboljšava kvalitete mreže končnih elementov z večnamensko optimizacijo« (mentor: izr. prof. dr. Marko Kegl, somentor: izr. prof. dr. Jožef Predan). * Na Fakulteti za strojništvo Univerze v Mariboru je pridobil naziv magister gospodarski inženir: dne 26. septembra 2013: Matej GOLAVŠEK z naslovom: »Analiza ekonomske upravičenosti izločanja plastike za energetsko ali snovno izrabo odpadkov« (mentor: prof. dr. Niko Samec, somentorica: prof. dr. Polona Tominc). * Na Fakulteti za strojništvo Univerze v Mariboru je pridobil naziv magister inženir mehatronike: dne 11. septembra 2013: Aljaž KRAMBERGER z naslovom: »Vodenje z uporabo meritev možganskih valov« (mentor: izr. prof. dr. Karl Gotlih, somentorja: doc. dr. Miran Rodič, prof. dr. Miro Milanovič); Denis KRAJNC z naslovom: »Stroj za razširjanje tankostenskih cevi« (mentor: izr. prof. dr. Ivan Pahole, somentorja: izr. prof. dr. Karl Gotlih, izr. prof. dr. Aleš Hace); SI 126

Miljenko ŠARIĆ z naslovom: »Stroj za robljenje tankostenskih cevi« (mentor: izr. prof. dr. Ivan Pahole, somentorja: izr. prof. dr. Karl Gotlih, izr. prof. dr. Aleš Hace); Tom PEINKIHER z naslovom: »Akvarijski računalnik Aquabee« (mentor: doc. dr. Uroš Župerl, somentor: izr. prof. dr. Aleš Hace). * Na Fakulteti za strojništvo Univerze v Ljubljani so pridobili naziv diplomirani inženir strojništva (UN): dne 2. septembra 2013: Miha DELAVEC, dne 2. septembra 2013: Miha DEŽMAN, dne 2. septembra 2013: Ana Marija FILIPIČ, dne 2. septembra 2013: Blaž VERBIČ, dne 3. septembra 2013: Martin BEM, dne 3. septembra 2013: Jakob GAJŠEK, dne 3. septembra 2013: Dominik KOZJEK, dne 3. septembra 2013: Nejc MEDVED, dne 3. septembra 2013: Matija PEČNIK, dne 3. septembra 2013: Uroš PEVEC, dne 3. septembra 2013: Aleš SODJA, dne 3. septembra 2013: Jurij ŠALAMON, dne 3. septembra 2013: Jernej TOMAŽIN, dne 4. septembra 2013: Armin KEĆANOVIĆ, dne 5. septembra 2013: Matic PODPEČAN, dne 6. septembra 2013: Matic BRANK, dne 6. septembra 2013: Miloš MIJALKOVIĆ, dne 6. septembra 2013: Branka MIRJANIĆ, dne 6. septembra 2013: Urban PRIJANOVIČ, dne 6. septembra 2013: Andrej ZADRAVEC, dne 6. septembra 2013: Z Andraž UPAN, dne 9. septembra 2013: Žan ANDERLE, dne 9. septembra 2013: Luka BONEŠ, dne 9. septembra 2013: Peter KOLAR, dne 9. septembra 2013: Aleksander KOPAČ, dne 9. septembra 2013: Andraž KREK, dne 9. septembra 2013: Rok NARAGLAV, dne 9. septembra 2013: Matic PAJK, dne 9. septembra 2013: David PETERKA, dne 9. septembra 2013: Nina TOMAŽIČ, dne 9. septembra 2013: Gregor VIRANT, dne 10. septembra 2013: Andrej GOLJAR, dne 10. septembra 2013: Martin KRAŠČEK, dne 10. septembra 2013: Luka MLAKAR, dne 10. septembra 2013: Matej PREŠERN, dne 10. septembra 2013: R Primož ESMAN, dne 10. septembra 2013: Andraž VELIKONJA, dne 11. septembra 2013: Matej PEROŠA, dne 11. septembra 2013: Miha PIKON, dne 11. septembra 2013: Vid RESNIK, dne 11. septembra 2013: Klemen ZAFRED, dne 16. septembra 2013: Teja JEREB, dne 16. septembra 2013: Erik LISJAK,

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, SI 122-130

dne 16. septembra 2013: Davorin POMPE, dne 16. septembra 2013: Anej ROVAN, dne 16. septembra 2013: Rasim ŠABIĆ, dne 16. septembra 2013: Rok URH, dne 17. septembra 2013: Dušan VASIĆ, dne 18. septembra 2013: Miha HROVAT, dne 20. septembra 2013: Miha PETERNELJ, dne 23. septembra 2013: Grega FINŽGAR, dne 23. septembra 2013: Jure GABRŠČEK, dne 23. septembra 2013: Luka MAJER, dne 23. septembra 2013: Marko MIHELIČ, dne 24. septembra 2013: Rok LAVRIČ, dne 27. septembra 2013: Dino GRBIĆ. * Na Fakulteti za strojništvo Univerze v Mariboru je pridobil naziv diplomirani inženir strojništva (UN): dne 5. septembra 2013: Matjaž VELIKONJA z naslovom: »Analiza gospodarnosti avtomatiziranega skladišča« (mentor: doc. dr. Iztok Palčič, somentor: izr. prof. dr. Borut Buchmeister); dne 6. septembra 2013: Jernej KIRBIŠ z naslovom: »Sanacija kotlovnice v hotelu Orel« (mentor: prof. dr. Hribernik Aleš); dne 11. septembra 2013: Nika STRNIŠNIK z naslovom: »Vrednostna analiza upravljalne plošče vgradne pečice HomeCHEF Gorenje« (mentor: doc. dr. Marjan Leber, somentor: doc. dr. Iztok Palčič). dne 12. septembra 2013: Janez JEŽ z naslovom: »Analiza prevlek aktivnih delov preoblikovalnih orodij pri preoblikovanju visokotrdnostne Al pločevine« (mentor: izr. prof. dr. Ivan Pahole). Nejc NOVAK z naslovom: »Karakterizacija togosti UniPore celične strukture z računalniškimi simulacijami« (mentor: prof. dr. Zoran Ren, somentor: doc. dr. Matej Vesenjak). Tilen ŠTEFANE z naslovom: »Sanacija ogrodja prijemala manipulatorja aluminijastih bram« (mentor: prof. dr. Zoran Ren, somentor: asist. dr. Matej Borovinšek). Mario ŽLENDER z naslovom: »Primerjava zasnove mostnih dvigal po SIST DIN 15018 in SIST EN 13001-2 pristopa« (mentor: prof. dr. Iztok Potrč, somentor: izr. prof. dr. Tone Lerher); dne 13. septembra 2013: Primož STRELEC z naslovom: »Fizikalno modeliranje hidroelektrarne« (mentor: izr. prof. dr. Bojan Dolšak, somentor: dr. Jasmin Kaljun);

dne 17. septembra 2013: Rok BOBNARIČ z naslovom: »Fotogrametrična meritev vodil stiskalnice Fritz Müller Esslingen« (mentor: izr. prof. Vojmir Pogačar); Benjamin BOKAN z naslovom: »Tehnološka in ekonomska analiza prilagodljivih obdelovalnih sistemov - Arcont d.d.« (mentor: prof. dr. Jože Balič); Tomaž BOSIO z naslovom: »Predelava konstrukcije standardne elektro uporovne komorne peči za termično obdelavo« (mentor: prof. dr. Srečko Glodež, somentor: doc. dr. Janez Kramberger); Matija CERJAK z naslovom: »Koncept in zasnova gibkega prijemala za kardanske zgibe, ki jih barva robot« (mentor: izr. prof. dr. Stanislav Pehan); Aleš CVIKL z naslovom: »PEM gorivne celice« (mentor: prof. dr. Breda Kegl, somentor: izr. prof. dr. Stanislav Pehan); Jernej ČASAR z naslovom: »Dimenzioniranje elementov hidravlične stiskalnice« (mentor: prof. dr. Srečko Glodež, somentor: doc. dr. Janez Kramberger); Sašo DERVARIČ z naslovom: »Določevanje dobe trajanja sintranih zobnikov z napetostno metodo« (mentor: prof. dr. Srečko Glodež); Benjamin GERJEVIČ z naslovom: »Kako variti proti kislinam odporno jeklo 316L, debeline 11 mm« (mentor: izr. prof. dr. Vladimir Gliha, somentor: doc. dr. Tomaž Vuherer); Anemari GRAČNAR z naslovom: »Terciarno odškajanje med vročim valjanjem jeklenih profilov« (mentor: prof. dr. Miran Brezočnik); Klemen HLEBEC z naslovom: »Sistem vodenja in zagotavljanje kakovosti pri izvedbi neporušitvene kontrole na velikem energetskem objektu« (mentor: prof. dr. Bojan Ačko); Marcel JEVŠNIK z naslovom: »Vpliv različnih tehnologij izdelave na lastnosti vzmetnih jekel« (mentor: prof. dr. Ivan Anžel, somentor: prof. dr. Nenad Gubeljak); Aljaž KRANER z naslovom: »Elektromotorni pogon za manipulacijo zaklepnega mehanizma« (mentor: doc. dr. Mitja Kastrevc, somentor: prof. dr. Nenad Gubeljak); Srečko KRENKER z naslovom: »Numerična optimizacija dizelskega motorja« (mentor: prof. dr. Breda Kegl, somentor: asist. Luka Lešnik); Jure LORENČIČ z naslovom: »Izdelava pokrova sklopa kondenzatorja 136/25/0,8« (mentor: doc. dr. Iztok Palčič); Klemen STERKUŠ z naslovom: »Določitev dobe trajanja nosilca izpušnega sistema motornega kolesa« (mentor: doc. dr. Janez Kramberger, somentor: prof. dr. Srečko Glodež); Aljaž VIDOVIČ z naslovom: »Oblikovna prenova avtomobilske maske z uporabo vzvratnega inženirstva« (mentor: izr. prof. Vojmir Pogačar); SI 127

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Jure ZORIČ z naslovom: »Konstruiranje temperaturno klimatske testne komore« (mentor: izr. prof. dr. Miran Ulbin); dne 26. septembra 2013: Aleš KRUMPAK z naslovom: »Fizikalno modeliranje jopiča z zračno blazino« (mentor: izr. prof. dr. Bojan Dolšak, somentor: dr. Jasmin Kaljun); Simon LESJAK z naslovom: »Načrtovanje in preverjanje kakovosti žerjava« (mentor: prof. dr. Bojan Ačko, somentor: doc. dr. Andrej Godina); Jernej PELAN z naslovom: »Določevanje upogibne trdnosti sintranih zobnikov po AGMA standardu« (mentor: prof. dr. Srečko Glodež); dne 27. septembra 2013: Rene BRDNIK z naslovom: »Razvoj gonila kuhalne enote espresso kavnega aparata« (mentor: prof. dr. Zoran Ren). * Na Fakulteti za strojništvo Univerze v Mariboru je pridobil naziv diplomirani gospodarski inženir (UN): dne 5. septembra 2013: Davor GOLEC z naslovom: »Dejavniki tveganja pri razvoju novega izdelka« (mentor: doc. dr. Marjan Leber, somentorica: prof. dr. Majda Bastič); dne 10. septembra 2013: Aljaž ŽUGMAN z naslovom: »Prenova postopkov skladiščenja v centralnem skladišču podjetja Petrol d.d., enota Zalog« (mentor: prof. dr. Iztok Potrč, somentorica: prof. dr. Majda Bastič); dne 11. septembra 2013: Žiga HERGAMAS z naslovom: »Raziskava trga za razvoj novega izdelka - večfunkcijskega stola« (mentor: doc. dr. Marjan Leber, somentorica: doc. dr. Aleksandra Pisnik Korda); Tim JAKL z naslovom: »Upoštevanje kupčevih želja v razvoju izdelka s pomočjo metode QFD« (mentor: doc. dr. Marjan Leber, somentorica: izr. prof. dr. Zdenka Ženko); Aleš RORIČ z naslovom: »Upravičenost razvoja novega stikala za kuhinjski aparat v podjetju Eurel d.o.o.« (mentor: doc. dr. Marjan Leber, somentor: prof. dr. Miroslav Rebernik); dne 17. septembra 2013: Jan BERDEN z naslovom: »Organizacija proizvodnje v podjetju Transpak d.o.o.« (mentor: doc. dr. Iztok Palčič, somentor: prof. dr. Vojko Potočan); Timi GOMBOC z naslovom: »Sinteza in analiza strehe stadiona po metodi končnih elementov« (mentor: doc. dr. Boštjan Harl, somentorja: prof. dr. Marko Kegl, izr. prof. dr. Zdenka Ženko); Jurij KOKALJ z naslovom: »Uporaba metod vrednostne analize pri razvoju novega izdelka« SI 128

(mentor: doc. dr. Iztok Palčič, somentor: doc. dr. Lidija Hauptman); Miha REČNIK z naslovom: »Optimizacija postavitve proizvodnje za izdelavo rampne deske pri podjetju Uniforest d.o.o.« (mentor: doc. dr. Iztok Palčič, somentor: prof. dr. Vojko Potočan); Marjan STRAŠEK z naslovom: »Optimizacija serijske proizvodnje mesoreznic« (mentor: doc. dr. Iztok Palčič, somentor: prof. dr. Vojko Potočan). * Na Fakulteti za strojništvo Univerze v Mariboru je pridobil naziv diplomirani inženir mehatronike (UN): dne 12. septembra 2013: Robert OJSTERŠEK z naslovom: »Avtomatizacija servo obračalne mize s Siemens Step7 in WinCC flexible« (mentor: izr. prof. dr. Karl Gotlih, somentorja: dr. Simon Brezovnik, izr. prof. dr. Aleš Hace); dne 17. septembra 2013: Marko ANTONČIČ z naslovom: »Merilni in nadzorni modul za termoelektrični sistem« (mentor: izr. prof. dr. Karl Gotlih, somentorja: doc. dr. Miran Rodič, prof. dr. Miro Milanovič); Žiga BOBEK z naslovom: »Androidni programski vmesnik za zajemanje merilnih podatkov preko WI-FI povezave« (mentor: izr. prof. dr. Karl Gotlih, somentor: izr. prof. dr. Vojko Matko); Bojan ČONTALA z naslovom: »Načrtovanje in izdelava merilnega sistema na dirkalniku Formula Student in analiza dobljenih podatkov« (mentor: izr. prof. dr. Karl Gotlih, somentor: doc. dr. Edvard Cibula); Peter LUPŠA z naslovom: »Nastavitev programa STARTER za krmilnik SIEMENS Sinamics S120 in SIEMENS S7-300 s pripadajočimi elektro vezji« (mentor: izr. prof. dr. Karl Gotlih, somentorja: dr. Simon Brezovnik, izr. prof. dr. Aleš Hace); Matej MIHELČIČ z naslovom: »Avtomatizacija vpenjalne priprave na obdelovalnem stroju« (mentor: doc. dr. Uroš Župerl, somentor: izr. prof. dr. Aleš Hace); Benjamin OŠLAJ z naslovom: »DC-DC pretvornik za termoelektrični sistem« (mentor: izr. prof. dr. Karl Gotlih, somentorja: doc. dr. Miran Rodič, prof. dr. Miro Milanovič); Tomaž RAJH z naslovom: »Postopki za izdelavo v robotizirani celici (priročnik)« (mentor: izr. prof. dr. Karl Gotlih, somentor: izr. prof. dr. Aleš Hace); Stanko SKLEDAR z naslovom: »Mehanska obdelava z robotom ACMA XR701« (mentor: izr. prof. dr. Karl Gotlih, somentor: izr. prof. dr. Aleš Hace);

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, SI 122-130

Miha TERTINEK z naslovom: »Robotska delovna celica s 6-osnim paralelnim robotom FANUC M-1it« (mentor: izr. prof. dr. Karl Gotlih, somentor: izr. prof. dr. Aleš Hace). * Na Fakulteti za strojništvo Univerze v Ljubljani so pridobili naziv diplomirani inženir strojništva (VS): dne 3. septembra 2013: Luka AMBROŽIČ z naslovom: »Razvoj zaščitne opreme za sistem laserskega spajanja termostatov« (mentor: doc. dr. Matija Jezeršek); Miha ČEGOVNIK z naslovom: »Razvoj eksperimentalnega sistema za proučevanje vpliva vibracij na lasersko graviranje« (mentor: doc. dr. Matija Jezeršek); Grega KALAN z naslovom: »Multiplikator za pršenje zaščitnih sredstev« (mentor: prof. dr. Jožef Duhovnik, somentor: prof. dr. Marko Nagode); Cene ORTAR z naslovom: »Krmiljenje položaja mačka pri žerjavu z vrtljivo nadgradnjo« (mentor: doc. dr. Primož Podržaj, somentor: doc. dr. Boris Jerman); Tomo VELDIN z naslovom: »Izdelava tehniške dokumentacije za obnovitveno vzdrževanje polzirnega stroja SCHENK« (mentor: izr. prof. dr. Jernej Klemenc); Simon ZUPANČIČ z naslovom: »Modeliranje in preverjanje spremembe koloteka gospodarskega vozila« (mentor: izr. prof. dr. Robert Kunc, somentor: prof. dr. Ivan Prebil); * Na Fakulteti za strojništvo Univerze v Mariboru so pridobili naziv diplomirani inženir strojništva (VS): dne 5. septembra 2013: Tadej UMEK z naslovom: »Snovanje in konstruiranje prednje traktorske hidravlike« (mentor: izr. prof. dr. Miran Ulbin, somentor: doc. dr. Aleš Belšak); dne 11. septembra 2013: Albin KODRIČ z naslovom: »Pregled metod za izvedbo vitke proizvodnje« (mentorica: doc. dr. Nataša Vujica Herzog, somentor: izr. prof. dr. Borut Buchmeister); Aleš KRALJ z naslovom: »Optimiranje zalog v maloserijski proizvodnji« (mentor: izr. prof. dr. Borut Buchmeister, somentor: doc. dr. Iztok Palčič); Davorin PIŠOTEK z naslovom: »Načrtovanje proizvodnje žaluzij v podjetju ALU-REFLEX d.o.o.« (mentor: izr. prof. dr. Borut Buchmeister, somentor: doc. dr. Iztok Palčič);

dne 12. septembra 2013: Borut ŽELODEC z naslovom: »Postopek uveljavitve pravilnika o varnosti strojev za robotizirano proizvodno celico« (mentor: izr. prof. dr. Karl Gotlih, somentor: prof. dr. Bojan Ačko); dne 17. septembra 2013: Žan BUKŠEK z naslovom: »Modeliranje in izdelava stroja za pripravo viter« (mentor: prof. dr. Miran Brezočnik); Jožef HAUŽAR z naslovom: »Simulacija hidravličnega podajalnega pogona tračne žage« (mentor:doc. dr. Uroš Župerl, somentor: prof. dr. Stanislav Pehan); Urban HRIBERNIK z naslovom: »Razširjen celoviti energetski pregled javnega podjetja za distribucijo energentov« (mentor: dr. Filip Kokalj); Kristijan MARKEŽ z naslovom: »Primerjava varnosti starih in sodobnih kmetijskih strojev« (mentor: doc. dr. Janez Kramberger, somentor: doc. dr. Mirko Ficko); Blaž SMONKAR z naslovom: »Optimizacija postopkov odrezavanja za izdelavo gredi profilnega koluta« (mentor: prof. dr. Franci Čuš); Mitja ŠTIH z naslovom: »Konstruiranje vrat protivlomno odpornih blagajn serije Starprim 3N« (mentor: prof. dr. Srečko Glodež); Boštjan VIDMAR z naslovom: »Načrtovanje teleskopskega obešala« (mentor: doc. dr. Aleš Belšak, somentor: izr. prof. dr. Miran Ulbin); dne 26. septembra 2013: Simon GAŠPARIČ z naslovom: »Integracija in analiza CAD/CAM sistema za izdelavo erodirnih elektrod v orodjarnah« (mentor: prof. dr. Jože Balič); Gregor KAČIČ z naslovom: »Prvi zagon vrtalnega stroja s hidravličnim podajanjem« (mentor: doc. dr. Darko Lovrec, somentor: doc. dr. Mitja Kastrevc); Matic OČKO z naslovom: »Konstrukcija orodja za utopno kovanje spodnjega križa prednjih vilic motorja« (mentorica: dr. Marina Novak, somentor: dr. Jasmin Kaljun); Tine PEČELIN z naslovom: »Konstruiranje gonila in analiza ohišja reduktorja« (mentor: doc. dr. Aleš Belšak); Matic PEČNIK z naslovom: »Izdelava orodja za brizganje plastičnih mas« (mentor: izr. prof. dr. Ivan Pahole, somentor: mag. Tomaž Brajlih); Josip PINTAR z naslovom: »Sistematičen pristop k mazanju strojev« (mentor: doc. dr. Samo Ulaga, somentor: doc. dr. Darko Lovrec); Urban POLEGEK z naslovom: »Načrtovanje nastavljive delovne mize« (mentor: doc. dr. Aleš Belšak, somentor: izr. prof. dr. Miran Ulbin); Matevž RAČIČ z naslovom: »Konstrukcija orodja za tlačno litje pokrova elektromotorja iz SI 129

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)10, SI 122-130

aluminija« (mentor: dr. Marina Novak, somentor: izr. prof. dr. Bojan Dolšak); Rok RAMŠAK z naslovom: »Izdelava gravurnega vložka orodja za brizganje plastičnih mas« (mentor: prof. dr. Jože Balič). * Na Fakulteti za strojništvo Univerze v Mariboru so pridobili naziv diplomirani inženir strojništva: dne 13. septembra 2013: Matej ZORKO z naslovom: »Razvoj vgradne električne smetišnice« (mentor: viš. pred. mag. Borut Golob);

SI 130

dne 17. septembra 2013: Simon ŠIKOVEC z naslovom: »Uporaba umetne inteligence za reševanje zahtevnih inženirskih problemov« (mentor: prof. dr. Miran Brezočnik, somentor: asist. dr. Simon Klančnik); dne 26. septembra 2013: Uroš JANČAR z naslovom: »Zagotavljanje vzdrževalnosti klimatskih naprav v telekomunikacijskih centralah« (mentor: izr. prof. dr. Igor Drstvenšek, somentor: doc. dr. Marjan Leber).

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59 (2013) 10

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Papers

575

Elvira Džebo, Dušan Žagar, Matjaž Četina, Gregor Petkovšek: Reducing the Computational Time of the Smoothed Particle Hydrodynamics Method with a Coupled 2-D/3-D Approach

Mitja Mori, Tilen Mržljak, Boštjan Drobnič, Mihael Sekavčnik: Integral Characteristics of Hydrogen Production in Alkaline Electrolysers

595

Hongming Lv, Shaona Liu: Closed-Loop Handling Stability of 4WS Vehicle with Yaw Rate Control

604

José Salgueiro, Gabrijel Peršin, Jože Vižintin, Matic Ivanovič, Boštjan Dolenc: On-line Oil Monitoring and Diagnosis

613

Jong Boon Ooi, Xin Wang, Ying Pio Lim, ChingSeong Tan, Jee-Hou Ho, Kok-Cheong Wong: Parametric Optimization of the Output Shaft of a Portal Axle using Finite Element Analysis

620

Prabu Krishnasamy, Jancirani Jayaraj, Dennie John: Experimental Investigation on Road Vehicle Active Suspension

626

Kursad Gov, Omer Eyercioglu, Mehmed Veysel Cakir: Hardness Effects on Abrasive Flow Machining

585

Journal of Mechanical Engineering - Strojniški vestnik

Contents

10 year 2013 volume 59 no.

Strojniški vestnik Journal of Mechanical Engineering