BPro RC 1 2016/17_Abundance

ABUNDANCE MIKAELA PSARRA ELENI CHALKIADAKI JINGWEN HE

2016–2017 LO N D O N

ABUNDANCE

The Bartlett School of Architecture, UCL Faculty of the Built Environment 22 Gordon Street, London WC1H 0QB September 2017

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AD

RESEARCH CLUSTER¹ WONDERLAB

MArch in Architectural Design The Bartlett School of Architecture, UCL MIKAELA PSARRA ELENI CHALKIADAKI JINGWEN HE tutors

Daghan Cam Andy Lomas Soomeen Hahm Alisa Andrasek 2016–2017 LOND ON

ACKNOWLEDGEMENTS

We would like to thank DaÄ&#x;han Çam for our initiation in computational logic, Andy Lomas for his contribution in aesthetics, visualisation and shaping of our project, digitally and virtually, Stamatios Psarras for precious scientific advice on computation, optimization and design, Vincente Soler for valuable technical support in robotics and algorithmic process & Yusuf Ali for essential advice on VR, hardware and software, as well as exclusively providing us with the HTC Vive for testing and exhibition.

CONTENTS

ABSTRACT

10-11

NON-DETERMINISM MULTI-AGENT SYSTEMS

THEORETICAL FRAMEWORK

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60-89

.

INDETERMINACY

14-17

RANDOMNESS

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POLY-SCALAR DESIGN

18-21

PERLIN NOISE

90-97

ROBOTIC FABRICATION

22-25 APPLICATIONS

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AREAS OF RESEARCH

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DESIGN PROPOSALS

BIG DATA

28-29

COMPUTING & SIMULATION

30-33

SUPER-PERFORMANCE

DISCRETENESS

34-35

LIGHT INFILTRATION

REFERENCES

.

MARS DUNES

36-39

BONE TISSUES

40-43

ARCHITECTURE

44-47

ABUNDANCE PROJECT WORKFLOW DATA MANAGEMENT VOXELS

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50-51 .

52-59

POST VALIDITY-CHECK OPTIMIZATION CONNECTIVITY VARIATIONS POTENTIALITY CONSTRUCTION SCHEME FABRICATION VIRTUAL REALITY

98-123 .

124-145 .

146-165 .

166-177 .

178-223 .

ABUNDANCE VR

224-229

REFERENCES

232-235

ABSTRACT

ABUNDANCE /* This is a project about abundant data control. Big data aesthetics impose the starting point and then management and classification of the information generates articulated and polyscalar matters of architecture. The experimentation with computational design and cutting-edge technology, is consequently the foreground of dealing with the capacities and limitations of the hardware; GPU computing, CPU processing and memory usage. Computational processing is effectively reflected to a high-resolution architectural fabric. The latter is conceived, developed and optimized, as well as, materialised with robotics and experienced with Virtual Reality, in a computational logic and workflow. The behavior of the proposal as a spatial lattice is structurally intelligent using less material and easily fabricated by robotics. Industrial robots can spatially extrude material and three-dimensionally print faster and cheaper cutting down manual labor.

In parallel, VR experience gives the opportunity to the designer of experiencing and evaluating in advance, the power and the virtues of these architectural paradigms.

Architecture is reflected here through cloud-like structures of high resolution, as resilient, porous and computationally efficient developments. The suggested light super-performance is generated by large amounts of inter-connected voxels, as a data cloud which controls and translates emergent behavior into a geometry of wonder.

*/

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MIKAELA PSARRA ELENI CHALKIADAKI JINGWEN HE

theoretical fr amework

sp ecu lati ons

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THEORETICAL FRAMEWORK | INDETERMINACY

INDETERMINACY Novel architecture paradigms which are now being developed through computational design are expressed as well-formed instances of indeterminacy. A scientific vocabulary which is derived from physics and mathematics and it is applied to computer science, is therefore adjusted to the demanding needs of high-end architecture as this has blossomed in the early 21st century.

Emergent mechanisms in nature have been a starting point for research on architectural computation to flourish and expand 1; it is important though, as J.Frazer states, to consider this as an incentive to explore natural mechanisms as information rather than an analogy to describe form. Interestingly enough, simple behaviors, motivated by respectively simple rules can force unpredictable phenomena to happen, resulting in a highly disordered system of varied and diverse, emergent molecular combinations.

‘A new generation of artists writing genomes as fluently as Blake and Byron wrote verses, might create an abundance of new flowers and fruit and trees and birds to enrich the ecology of our planet. Most of these artists would be amateurs, but they would be in close touch with science, like the poets of the earlier Age of Wonder.’

Freeman John Dyson, referencing The Age Of Wonder by Richard Holmes

1 J Frazer, An Evolutionary Architecture, Architectural Association: London, 1995, pp 9-21.

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MIKAELA PSARRA ELENI CHALKIADAKI JINGWEN HE

Indeterminacy may be also randomness, stochasticity, entropy, contingency, unpredictability, uncertainty, unprovability and more. However, to get a closer look of how this could be more strictly defined, it is crucial to provide the context it is set up and the concept it delivers. For instance, in mathematics, indeterminacy could reflect different meanings depending on the related field; logic, statistics, computer science. In philosophy on the other side, indeterminacy could be illustrated as part of the phenomena of vagueness and ambiguity in the philosophy of language, or even part of underdetermination in the philosophy of science, respectively.

This versatile approach, aims, on the one hand, to collate a related cohesive vocabulary for computational design and on the other hand to interrogate the implications and contributions of it as a contemporary working principle. It is important to develop, however, a clear argument of how is indeterminacy reflected in the foreground of this research and more generally in analogous current paradigms. The affinity with algorithmic design transforms conventional materiality and systematic behavior to invisible and aleatory sequences of numbers. Tackling with algorithms at present, is a way, to release design from its static meaning, engage with variables and consecutive simulations and then instantiate design-forms as distinct aspects of an emerging mechanism 1, leading to “the birth of the non-standard” as well depicted by Mario Carpo 2. Control and causality do not have equivalent significance anymore, in the same fashion as in the repetitively reproduced Uncertainty Principle. Articulated in 1927 by Werner Heisenberg, the later expresses the limitation of operational potentialities as such imposed in quantum mechanics, by using the following inequality to describe the position and momentum of a particle: Δq · Δp ≥ ħ/2. According to this, it is impossible to control or measure position and momentum at the same time, or measure either value without disturbing the one left , which imposes a profound difficulty in predicting or regulating. While in quantum physics

3

vagueness has an operational role, in architecture it is rather a foundational property which can inherently generate form; this should be consistently adapted to the argument this case study is hereinafter attempting to make.

1 A Andrasek, Open Synthesis/ Toward a Resilient Fabric of Architecture. Log, 25, Summer 2012, pp 45-54.

2 M Carpo, “Parametric Notations: The Birth of the Non-Standard”, AD, February 2016 3 P Bausch, T Heinonen, P Lahti, Heisenberg’s Uncertainty Principle, Physics Reports 452, May 2007, pp 155-176

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T H E O R E T I C A L F R A M E W O R K | P O LY- S C A L A R D E S I G N

P O LY- S C A L A R D E S I G N Scale in architectural design is defined as the relationship between various dimensions of buildings, structures, organised space and their relationship to the human viewer. According to Miyasaka1, since architectural design begins purely from a generic idea that needs to be developed further and gain physical form, architects are regularly navigating between theoretical and em¬pirical worlds, between small scale and large scale. Working with scale – scaling up or down according to the designer needs – can help to carry out a design idea into reality by taking into consideration the details of material and structural systems, how they act and how they can help into making the design a reality. Adapting between different scales and knowing what each one is useful for can help a designer to concretize the abstract and visualize details more clearly as progress is made toward the final design solution1 .

Into this research, the need to work in different scales came up due to the interest in designing on high resolution, while working with an abundance of data, in order to achieve complexity as well as consistency on a massive scale. An investigation is made on how the handling of large amount of data can yield different quantities both in the micro-scale and the macro-scale.

Furthermore, if further exploring the transition from one scale to the other, the designer will have the ability to acquire a more spherical and integrated perception of the outcome that he want to accomplish.

The need of transferring between scales in this project is rather essential. It is a constant going back and forth process between those scales solving different issues each time. And while each scale has limitations, it is quite interesting to observe the influence that they have between them. On the microscale, you may not be able to see how the choices you make affect the total but it is the foundation of your outcomes, the source of the project. On the macroscale, on the other hand, you get to see the outcomes but you miss basic structural constrains that could be 1 Miyasaka, T., 2014. Seeing and making in architecture. 1st ed ed. New York: Routledge

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really harmful for the structure. And finally, from a middle scale you get some of both. You get to solve problems from macroscale or guide better the rules given on the microscale. However, you still don’t get to see the final results that you get on a really precise and high resolution scale.

This is why each scale is crucial both with its benefits and drawbacks.

Moreover, one thing that is different to this approach is that the design does not start from a microscale and then is being en¬larged to a greater scale to fit in it. Each scale used on this project acquires a specific purpose from the beginning. Design only occurs on the macroscale, but if one notices more carefully, the other two scales are actually visible from that scale depending on how far or close you are. You experience a model, (a wall, a pavilion) but within that you can recognize specific patterns that arise from the mesoscale and if you get even closer you can see that actually the whole structure consists of single lines that rotate on different angles.

Working in different scales can answer different questions regarding the design in architectural computation. How many scales needed or the usage that each one could have is up to the designer and the results he wants to achieve. In this case, the three scales used refer back to the whole research around handling an abundance of data. In the microscale, it was efficient to obtain simplicity, as in swarms the individual follows simple rules that form its behaviour. Each voxel gets data information and acts accordingly. In the mesoscale, complexity is the outcome of the different answers that each “individual” gives in the microscale and the way the “colony” combines those answers creating endless possibilities of connections and directions giving a catalogue of complexity into the whole structure. And finally, when observing the whole structure from a macroscale, one can see a complete harmony that arises from the combination of the simplicity of the rules that an agent follows and from the complexity of those rules combined into the whole system. Architectural design is reflected here through cloud-like structures of high resolution, as resilient, porous and computationally efficient developments. The excessive resolution allows for all scales to merge into one, hence the architectural experience could be rather interesting from any distance producing a new meaning on how the scales could be used in computational design.

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T H E O R E T I C A L F R A M E W O R K | R O B O T I C FA B R I C AT I O N

R O B OT I C FA B R I C AT I O N 3D PRINTING is an evolving fabrication realm where could still have bunches of possibilities to be explored. Our research explores somehow abundant possibilities in generating voxelbased connections which provide different qualities in macro scale. In terms of concrete rationalization of those poly-scalar behavioral performances of geometry derives from computational representation, the proposed design methodology associated with large scale spatial printing is trying to outline the optimization of traditional 3D printing techniques in a way bridge the inherent structural logic of self-supporting mechanisms in multidimensional space between virtual simulation and physical environment with a customized printing end effector carried by a 6-axis robot to deposit the extruded material in a stratified but holistic manner. The pivotal discussions are critically involved in the efficient toolpath finding algorithms that would have a considerable impact on the consuming time and energy during real fabrication and still in turn inspire the designers to implement further feasible 3D data that expands design possibilities by exposing the realizations of robotic workflow manufacturing, which heralds a potential shift in the making of architecture facilitated by materialization procedure.

Stemmed from our initial attempts to achieve the verification of spatial extruding process which differs from the traditional layer-by-layer printing method, the path-finding algorithm is introduced according to the particularity of our stratification of feasible robotic movement and discrete connections. Embedded in several structural scanning processes which gradually converted the initial computational design into real conditional fabrication, the following set of robot agents were created to generate segments of toolpath that contain the important values of directionality of each target in one polyline, especially the spatial extruding path.It is obvious that the robotic printing process making use of plastic extrusion is a bottom-up, layer by layer procedure. The innovated proposed methodology is that the printing layers are being classified as horizontal or vertical, originated from the connections generated in neighbouring

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MIKAELA PSARRA ELENI CHALKIADAKI JINGWEN HE

voxels in the same level or in adjacent level. The stratified printing logic initiates the code of testing rationalized computational models to start from the bottom drawing horizontal lines in planar frame first of all and jump to the next layer to generate spatial connections, vertically or diagonally, building up additively along Z axis. The visibility and controlling executive commands defined as digital outputs in robot console for understanding the structural attributes and restrictions is in associated with specific plug-in for grasshopper in rhino during the research working process. For instance, we just input a polyline and a set of variables and generates the list of targets. It will automate different behavior like changes in extrusion speed, robot speed, wait times as well as add additional helper targets (to avoid collisions or stop ahead of time) and so on depending supported or unsupported segments, supported or unsupported nodes, direction of the segment. The desirable merits of this grasshopper extension are not only ensuring the effective touch with previous horizontal layers but meanwhile eliminating the overlooked unprintable hanging lines to guarantee the continuous extrusion without stopping in spatial actions to observe continuity in 3d polylines in a way rationalizing the additive manufacturing process of multi-dimensional printing.

The fundamental shift of the proposed approach is basically trying to narrow the gap between simulation and fabrication processes which in turn catalyze the design progress as opportunities and generative stimulations from considering the fabricating constraints in physical environment.

The recent developments work on experimenting the technical constraints of obtaining essentially discretized but continuous toolpath to minimize the disruption of computing printing process using problem solving algorithms. Although having taken a lot of various constraints into account, the outcome of printed fragment also exhibited some defects concerning motions of singularities as well as unwanted interweaving. Owing to the heterogeneous directions of generated connections, it is difficult to automate the optimization simultaneously in digitallycontrol extrusion.

Moreover, we are engaging in manipulating the designated input signals to control the on-off of discrete extrusion as well as the cooling interval to be applied in the cooling system of our extruder, which could be able to solidify the vertical or diagonal lines without falling apart.

We also customized an delicated end effector with sharp bit to avoid collusion with those already-fabricated parts because of the density of multi- dimensional connections of our geometry, which ensured the final achievement of our fabrication possibilities.

T H E O R E T I C A L F R A M E W O R K | R O B O T I C FA B R I C AT I O N

Photo showing 3d-printed instances (layer by layer) of the project. The algorithm that has been developed for the purposes of the project provides abundant variations of connectivity.

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areas of research

exces si ve resolu ti on

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A R E A S O F R E S E A R C H | B I G D AT A

B I G D A TA This project emerges from redundancy and is entirely developed by algorithmic processing and computation. The idea of ‘big-data’ is the initial incentive to design high-resolution lattice structures with random input and without interference of other design tools or imported geometries to influence the conduct of the output. The use of computation in architecture proposes a new kind of synthesis based on data, overcoming the necessity of adhering to a certain ‘typology or paradigm’ 1. In an equivalent way, in this research, data management is a way of mostly dealing with analysis and evidence which is progressively collected, processed, assessed and accordingly used. For this reason, there is no specific architectural constraint that is being set in advance. Following consecutive simulations, the basic syllogism is being built on the basis of reflecting big-data to high-resolution architectural developments, linking, in a way, the notion of computational processing to architectural complexity. Dealing with computation, hence, suggests an alternative to modelling an architectural object following a certain typology or style or concept. As it will be described later, there is not only difference in the result, but also in the method and the workflow.

in this day and age, the notion that computers can handle a plethora of data – while something not new – has begun to affect computational design in various manners, since it can be more disturbing that it seems. Moreover, computational designers have the ability to use a rather simple approach driven from data in order to test different performances such as structural, light filtering, energy, and occupation, without the need to clarify them through certain causes or models 2. With the use of different algorithms data are explored on different manners and allow the designer to explore all the attributes on alternate terms. Carpo also indicates that with the abdication of simplification and the accumulation to affiliate complexity in order to achieve excessive resolution, computational design develops from limited data to abundant data.

1 P M Carranza, ‘Programs as Paradigms’, in Architectural Design, Vol 84 issue 5, September/October 2014, pp 66-73 2 M Carpo, ”Excessive Resolution”, in Architectural Design, Vol 86 Issue 6, November 2016, pp 78-83.

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1, 2, 3 Progressive instances of a simulation using Multi-Agent Systems in two-dimensional space

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A R E A S O F R E S E A R C H | C O M P U T I N G & S I M U L AT I O N

CO M P U T I N G & S I M U L AT I O N Computability is an automated procedure which requires no further efficiency to get completely integrated. However, algorithms still have certain space and time constraints, that reflect to memory usage and calculation time of computation they need, which is an aspect that should be evaluated in topics like this, where big data and processing are of high importance.

Following the involvement of architecture in computation, this has been therefore crucially addressed, considering the rapid drift to unparalleled complexity which demanded high levels of computationally expensive proceedings 1. Although this should be eventually overcome after the constant progress of technology, there are cases that will continue to be considered unsolvable in a rational given time, as for instance, the Non-Deterministic Polynomial Time problems. In a more detailed analysis the above issue remains a hardware problem; the access and speed of digital computing is being ensured by the continuous growth of the capacity of integrated circuits, named as “gates per unit area”, according to a general model known as Moore’s Law. The latest observed that the gate density doubles approximately every less than two years 2. However, this does not necessarily mean that it can as well exceed the expected physical limitations of the given circuit. It is worth referring to the computational constraints, as the necessity for simulation is becoming even more indispensable in order to secure and confirm the design decisions, to compute the performance and unveil potential problems. For many cases, the simulation is incapable of unveiling the whole spectrum of issues for a series of reasons; for instance, it is difficult or even impossible to overcome the complexities of the given conditions, to know the values of the existing variables, to define and formulate the problems, to systematize the design process or to control accuracy and tolerance problems 3.

1 F Roche, ‘Next-Door Instructions’, in Architectural Design, Vol 83 Issue 3, May/June 2013, pp 126-133 2 G E Moore, “Gramming more components onto integrated circuits”, Electronics, Vol 38, No 8: April 1965.

3 S Hanna, L Hesselgren, V Gonzalez and I Vargas, “Beyond Simulation: Designing for Uncertainty and Robust Solutions”, In: Proceedings of the 2010 Spring Simulation Multiconference (SpringSim ’10). ACM Press: New York, 2010

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1 Pavilion Aggregation in Z-Axis 2 Pavilion Data Analysis Simulation 3 Wall Connectivity Analysis (optimization process) 4 Agents simulation in a bounding box

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5 Pavilion X-Axis Simulation 6 Agents Simulation/ radial development 7 Column Z-Axis Aggregation

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AREAS OF RESEARCH | DISCRETENESS

DISCRETENESS

Architecture, until recently, was occasionally approached as a gradually developing organ-like system and different methods of computational design were based on this morphogenetic approach. Recent generative work attempts to discretise the computational design process, but there still are many constrains towards this process in order to achieve to absolve the constructive and tectonic issues into the algorithmic logic. The reason behind this is that this work is still mostly based on constant fabrication techniques, that tend to be perplexing, labour intensive and at some time structurally inefficient 1 .

Discreteness in computational design can be described as the new way to diverge from the logic. Through that approach computation differs from the continuous approach of the morphogenetic experimentation, enabling new potentialities on design. Instead of following complex processes such as mass-customisation of thousands of different elements, or 3D printing, it looks at serial repetition of discrete parts, which are able to combine into heterogenous and complex forms. Serial repetition of a very simple element is a feasible and accessible method to achieve detailed and adaptable forms. While the geometry used is pretty simple, the results while scaling up can become rather complex and discrete.

In our case, the element used is a line that connects two centers on a voxel grid and rotates on 3D space accordingly. Excessive resolution is achieved as well as discreteness and the results differ according to the algorithm. By only changing the direction on such a simple element the prospects are countless and a catalogue of numerous different connections is formed. Randomness is combined with the systematic boundaries of the algorithm in a harmonious and unique way.

1  Retsin, G., Jimenez Garcia, M. and Soler, V. (2017). Discrete Computation For Additive Manufacturing. London: The Bartlett School of Architecture, UCL, pp.178-179.

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MIKAELA PSARRA ELENI CHALKIADAKI JINGWEN HE

REFERENCES

ma rs du nes

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(previous page), 5 Martian Chiaroscuro with seasonal frost http://hirise.lpl.arizona.edu/

2 Squiggly Sand Mars Dunes http://www.uahirise.org/

1 Noachis Terra Region of Mars https://www.nasa.gov/

3 Martian ‘Morse Code’ http://www.uahirise.org/

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4 Southern Terra Cimmeria in Mars http://www.uahirise.org/

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REFERENCES | MARS DUNES

MARS DUNES Mars dunes are being created by sand-sized materials which have been trapped on the floors of many Martian craters. The High Resolution Imaging Science Experiment (HiRISE) camera on NASA’s Mars Reconnaissance Orbiter has captured stunning views of formations which unveil complexity and intricacy.

The linear dunes, are thought to be caused by shifting wind directions and blend in harmony with attractively proportioned hills and layered deposits.

In places, some dunes can be remarkably similar to adjacent ones, or slightly vary, creating in large scale a spectacular phenomenon which triggers design potential in contemporary culture 1.

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1  Information taken by NASA / JPL / University of Arizona

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REFERENCES

bone ti ssu es

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(previous page) Osteoporotic Bones http://centroradiologicodetuxtla.com/ 1 Steve Gschmeissner Scanned electron micrograph (SEM) of human cancellous (spongy) bone https://rps-science.org/

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2 Terranova L, Mallet R, Perrot R, Chappard D Scanning electron microscopy of MC3T3 cells http://atlasofscience.org/ 3 Kina Shells with large & small pores of 300-400 μm and 10-20 μm http://www.frontiersin.org/

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4 Cancellous Bone https://nos.org.uk/ 5 Osteoporotic Bones close up http://www.theaark.co.uk/osteoporosis/

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REFERENCES | BONE TISSUES

BONE TISSUES The super-performance of the osseous tissue or bone is its structural and supportive function as a connective tissue of the body. The bone matrix contains abundant collagen reinforcing fibers which impart strength, flex, and resistance to twisting or torsional forces. These are surrounded by a complex of cementlike ground substance which makes bone highly resistant to compression forces.

The complexity of its structure in micro-scale make bone one of the strongest and lightest materials known, providing valuable feedback to designers on how to structurally approach highly complex porous forms.

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REFERENCES

A RC HI T ECTU RE

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(previous page) Sou Fujimoto, Serpentine Pavilion http://www.archdaily.com/

2 Roland Snooks, NGV Pavilion http://www.rolandsnooks.com/

4 Gramazio + Kohler, The Programmed Wall https://archpaper.com/

1 Diller Scofidio + Renfro, Blur Building http://www.archdaily.com/

3 Ateliers Jean Nouvel, The Louvre Abu Dhabi, https://www.inexhibit.com/

5 Sou Fujimoto, Serpentine Pavilion http://www.archdaily.com/

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REFERENCES | ARCHITECTURE

A RC H I T ECT U R E | T H E ‘ C LO U D ’ Porous, knotty and complex geometries can be identified in several architectural paradigms, as different renditions in terms of scale, materiality and behavior.

Some of these are mentioned here as more precise instances of the way we are attempting to approach porosity, osmosis and light infiltration, throughout our project.

As it will be further analysed in the following chapters, this research led to valuable information on ways of capturing light. The computational design of a porous skin, which distillates light, delivers the essence of a ‘cloud’ architecture, shaping a light lattice structure, using Multi-Agent Systems and Perlin Noise to instantiate directionality and micro-scale intricacy.

The analogy to the cloud, is deployed to this proposal to refer to an architectural device which presents a range of comparable characteristics; the poly-scalar behavior, the absence of definite and distinct limits of the whole, the variation of density, the light infiltration and diffusion and at last, the aesthetics. Nevertheless, it should be made clear, that this is to be considered more as a metaphor rather than a model of emulation which refers to the mechanism, rather than the form, similarly to the way J. Frazer speaks about the exploration of natural behaviors 1. For that reason, synthesis is from the very beginning, a programmatic operation in a more abstract sense, rather than directly an approach of the form.

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1 J Frazer, An Evolutionary Architecture, Architectural Association: London, 1995, pp 9-21.

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ABUNDANCE

WORKFLOW

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Closeup of a physical model, as output of the suggested computational strategy. Abundant data are being translated into matter, a well-structured lattice consisting of diverse geometry.

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A B U N DA N C E | P ROJ ECT W O R K F LO W

DATA

PROCESSING

P ROJ ECT W O R K F LO W

(Java)

This research brings together unpredictable and emergent systems which are being employed in order to ensure variability and self-organization in complex, large-scale designs. Computation therefore approaches all aspects; from initial design formations and later project development to extensive analysis and digital fabrication by robotics.

Consequently, the strategy of the project has been extended and precise, in order to transit

DESIGN OUTPUT (Arnold Batch Rendering)

from computational and digital work to an effective materialised architectural object.

The concept of the cloud has been a metaphor for a lattice-like system. The idea of the lattice has been then associated to robotic spatial extrusion, which suggests a faster method of fabricating complex, computational designs. However, preparing the existing

POST VALIDITY CHECK & O PT I M I SAT I O N (Java)

structure for fabrication, expects two computationally heavy procedures; a post-validity check, as a process to structurally optimize potential deficiencies and then a pathfinding algorithm to sort the existing connections in a reasonable sequence, in order to accommodate any fabrication constraint. To reach these steps, the initial processing included the accumulation of abundant data and their effective management. This required the intuitive designer’s decisions as well as a pure computational method. The latter, consists of a program, as this has been setup in Processing (java) which used a Vector field and Voxels in accordance with a a source of entropy; in particular Multi-Agent Systems and

PERFORMANCE TESTS

(Arnold Batch Rendering)

Perlin Noise. The control and management of information has been extensively optimised, in order to condense information and allow less hardware calculations. This, resulted in better performance, which also allowed the increase of population.

VR

(Unity/ C#)

DATA S O RT I N G

(Java & Grasshopper)

The rendering and consecutive simulations have been the tool to approach and evaluate the generated outputs, which mostly used GPU Computing. The experience of our project using VR has been one more reason to effectively represent and render digitally high-resolution structures, using a procedural way of generating the geometry based on importing data containing the transformation matrices for every discrete object, trying to balance realtime and pre-computed or baked techniques, possess total control of

FA B R I C AT I O N (Robotics)

performance, and vary resolution between static and dynamic objects.

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data management

VOXELS

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Voxel based structures using 1, 2, 3 Multi-Agent Systems 4, 5 Perlin Noise Algorithm

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D ATA M A N A G E M E N T | V O X E L S

VOXELS A cube, as a Platonic solid, is one of the purest geometric forms1.

When we design a cube, we initially think the geometric form of one in our minds. Then we need to embody it. This gives us the opportunity to find out how moving towards scales can be important in demonstrating the design. Studying each scale according to the project needs is one method of bringing reality to computational design, since examining different scales enables us to visualize the details more clearly1.

Before analysing further the different scales that we work into our project, it is crucial to lay the ground for our research. In order to have the ability to move back and forth between different scales we use voxels. A voxel represents a value on a regular grid in three-dimensional space. It is, we can say, a pixel in three dimensions. And while an individual voxel has some limits on how far it can be examined, when tested based on its relation with close neighbours and according to the data it contains, it holds a significant role in surveying the whole data space. The position that each voxel has on space is relative to the other voxels of the total field. Voxels often can perform as a tank of data and not as sampled spaces that are filed with geometry2.

Voxelization has a plethora of uses in different fields, from gaming, to rendering, to creating simulations and specific visualisations. It is the

1  Miyasaka, T., 2014. Seeing and making in architecture. 1st ed ed. New York: Routledge 5

2  Kaufman, A., Cohen, D. & Yagel, R., 1993. Volume Graphics. IEEE Computer, 26(7), pp. 51-64

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alteration of geometric objects towards their consecutive geometric representation into a set of voxels that best reaches an approximation of the object2. The voxels are not handled as pixels but through voxelization a database is formed of the discrete digitation and each box contains certain data. This form of discretising geometry provides uniform subdivision and distribution of data making them easy to manage and modify. The resolution of the object is based on the size of the subdividing voxels3. Thus, the benefit of precision and depth of reality is one of the basic reasons that voxelization is used to manage data in this project.

Towards the process of this project, voxels are used to handle the abundance of data into producing certain qualities. Each voxel serves as a box of data that acquire different information. Instead of treating a voxel as a box that contains a certain geometry which is being modified and combined into a final structure, in this case the voxel contains only information. By using voxelization, we have the ability to test diverse scales according to what we want to look upon each time and transfer between themeasily, by changing the size of our voxel grid. As mentioned above, scale does not really exist on a voxel field since it depends on the subdivision that the designer chooses. By increasing or lowering the resolution of our voxel grid we get to observe our system from many aspects and notice what happens from each individual voxel and its neighbours to a quite large amount of voxels that gives high resolution.

The geometry is being generated by connecting two adjacent voxels -from centre to centre-, as displayed in the diagrams; thus, there can be 26 possible connections for each voxel. Nevertheless, there is always a limitation in the number of connections per voxel -ranging from 3 to 12, depending on the case- in order to achieve a more controlled, reasonable and porous result. Then, an invisible Vector-Field which is subscribed and adjusted to the values of the voxel space, constitutes the medium to resolve cumulative data to actual connectivity. The vector-field, has a dynamic behavior, which means it is being constantly transformed over time, and it is dependent on a synchronous variability of information. Consequently, data-field is an interactive cloud, which distils information in every single frame.

3  Fathis, Z. & Philip, S. A., 2017. Salt Project, MArch Dissertation in Emergent Technologies and

Design. 1st ed. London: Architectural Assosiation School of Architecture (AA).

D ATA M A N A G E M E N T | V O X E L S

1. The number of connections

2. Different agents can create

3. The velocity of the agent is

vary according to algorithm

multiple connections

translated into a line

MODULE

A G G R E G AT I O N 4. From the simple module to

5. The voxel grid contains the

the complete aggregation of

box of information for the

the structure

agents

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closeup of diverse connectivities on a voxel field

Current & Opposite page Connectivity Variations using Voxels/ Voxels are able to register and control every aspect of differentiation

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D ATA M A N A G E M E N T | V O X E L S

closeup of diverse connectivities on a voxel field

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NON-DETERMINISM

multi-agent systems

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Current & Opposite page Agents simulation/ The system is directed by the basic rules of swarm intelligence; cohesion, separation, alignment

N O N - D E T E R M I N I S M | M U LT I - A G E N T S Y S T E M S

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M U LT I - A G E N T S Y S T E M S The model of a swarming flock, which is known as a Multi-Agent System (MAS) operates and evolves in an emergent way; radically and unpredictably. Although the cognizance of the basic rules that constitute such a model may appear rather simple –cohesion, separation, alignment– their adaptation and control are emergent, thus producing varied outputs. Emergence is regarded, according to De Wolf and Holvoet 1, as a uniform dynamic natural behavior which arises from local interference in a such a way that cannot be degraded further to these discrete, individual characteristics. Concurrently, emergent behavior affects back the inner relationships of its separate parts. Emergence, as denoted by DeLanda

2

therefore derives from a matter,

an event which is converted into energy and restrained by possibilities, either continuous or discrete. According to Alisa Andrasek 3, architecture is a field that tries to combine complex and different departments and is closely tied to the idea of the possibility, that something will happen in the future. Despite functioning within an accelerated plan, architecture has the tendency to function within an extended conceptual one. But, as the vague and complicated landscapes that architecture must absorb increase, arises the need for better adjustment to the uncertainty of changeable areas and to circumstances around a more expanded equilibrium3.

Computation and architecture are combined in many different ways as this gives the ability to use more productive approaches in design. Even though the programming used so far was mainly the procedural one, now this is gradually being replaced with a more object-oriented one. This change can be seen into the different algorithms, one of which is the multi-agent systems(MAS) one. Object-oriented programming gives the ability to look further into the code, creating with that way different architectural results. Those computational approaches are similar to how living organisms tend to receive and edit data. Thus, the advantage of using swarm intelligentbased computing gives the opportunity to avoid operations that are just internal and straight.

This happens because a big population of small, decentralized agents within simulations and generative methods has the ability to apprehend the complications and the important synthesis within design systems 3. Accordingly, the connection between micro and macroscale in designing with code is robust; each change on one can affect the other and its results.

1 TD Wolf and T Holvoet, Emergence and Self-Organisation: Different Concepts but Promising When Combined.

ESOA 2004, LNCS 3464, S Brueckner, et al, eds. Springer-Verlag: Heidelberg, 2005, pp 1-15. (opposite page) instances of a Multi-Agent System interaction with an imported mesh geometry

2 M De Landa, Philosophy and Simulation: The Emergence of Synthetic Reason, Continuum: London, 2011, pp 7-21.

3 A Andrasek, Open Synthesis // Toward a Resilient Fabric of Architecture. Log, 25, Summer 2012, pp 45-54.

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(current & opposite page) Early studies using multi-agent systems and voxels.

Experimenting in producing 3D geometry from flocking behavior. A mesh is imported and used as an interaction field for agents with attraction forces. Other forces such as gravity are used to enhance the stability of the structure.

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E A R LY S T U D I E S

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1

Springs-based sections differentiating in connectivity 1 lengthwise springs/ enhanced population 2 lengthwise springs/ less population 3 lengthwise & cross springs/ enhanced connectivity range 4 lengthwise & cross springs/ limited connectivity range 5 lengthwise & cross springs/ enhanced population & connectivity

2

3

4

N O N - D E T E R M I N I S M | M U LT I - A G E N T S Y S T E M S

SPRINGS

Elasticity is the property of materials to return to their original size and shape after being deformed. The physics behing elasticity leads to a simple elastic system (the coil spring) and a simple law (Hooke’s law).

Hooke’s law as an equation is written like this: F = − kΔx where, F = force, spring force, elastic force, applied force, deforming force k = the spring constant, which is the constant of proportionality, needed to make the units work out right Δx = extension or compression of the spring; that is, the change in length from the spring’s relaxed, natural, or original length (x0). Use of ∆ [delta] is optional as the idea of “change” is implied.

On our case, the research on Springs led to the simulation of porous skins made by resilient fibre-like curves. The physics reflect directly the tension ability, which is mostly dependent on the length and strength of each fibre.

The behavior of the springs can be really helpful to calculate for instance, the structural ability of a faÇade. Here they have been used along with Multi-Agents Systems, and flocking generated springs, alligned to the exact trail of every agent. Consequently, linear connectivity is enhanced with cross - connectivity, leading to this complex net-structure.

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D E N S I T Y ST U DY A

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In our first experiments we experimented with springs, by testing them on creating building skins. The springs are created by following the trails of the agents. As the framecount increases the results are denser and more contensed. According to the population of the agents and the predators, the outcome leaves more or less free space.

frameCount

250

numAgents

10.000

numPredators

20

cohesionRange

10

cohesionStrength separationRange separationStrength alignmentRange alignmentStrength

0.000001 2 0.03 5 0.02

predatorRange

8

predatorStrength

5

predGravity

-0.1

maxSpeed

0.1

maxSpeedPredator numParticles particleCreationFrequency springLength springStrength springLengthBetweenLines springStrength1

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1.000

maxAgeTrailPoint

0.45 40 5 0.05 0.0001 0.05 0.0001

minSpringRange

0.05

maxSpringRange

0.5

D E N S I T Y ST U DY A

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In our first experiments we experimented with springs, by testing them on creating building skins. The springs are created by following the trails of the agents. As the framecount increases the results are denser and more contensed. According to the population of the agents and the predators, the outcome leaves more or less free space.

frameCount

500

numAgents

10.000

numPredators

20

cohesionRange

10

cohesionStrength separationRange separationStrength alignmentRange alignmentStrength

0.000001 2 0.03 5 0.02

predatorRange

8

predatorStrength

5

predGravity

-0.1

maxSpeed

0.1

maxSpeedPredator numParticles particleCreationFrequency springLength springStrength springLengthBetweenLines springStrength1

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1.000

maxAgeTrailPoint

0.45 40 5 0.05 0.0001 0.05 0.0001

minSpringRange

0.05

maxSpringRange

0.5

D E N S I T Y ST U DY A

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At a next step, vertical springs are added in addition to the horizontal ones. In that way, the results become more denser as well as porous, simulating the structure of a bone tissue.

frameCount

100

numAgents

15.000

numPredators maxAgeTrailPoint cohesionRange cohesionStrength separationRange separationStrength alignmentRange alignmentStrength

200 10 0.001 2 0.03 5 0.02

predatorRange

8

predatorStrength

5

predGravity

-0.1

maxSpeed

0.1

maxSpeedPredator numParticles particleCreationFrequency springLength springStrength

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1.500

0.45 40 5 0.05 0.0001

D E N S I T Y ST U DY B

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After voxelization, on a first approach only Multi-Agent Systems are used. Voxel Space hosts the data of the agents’ velocities and generates accordingly connections among neighbor voxels. The abundance of data is due to the boids population and to an enhanced number of connectivity which ranges between 0-15.

frameCount

100

numAgents

5.000

numPredators

100

maxAgeTrailPoint

200

cohesionRange cohesionStrength separationRange separationStrength alignmentRange alignmentStrength

0.000001 2 0.03 5 0.02

predatorRange

8

predatorStrength

3

predGravity

-1

maxSpeed

0.30

maxSpeedPredator

0.60

springLength

0.05

springStrength

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2

0.0001

D E N S I T Y ST U DY B

78

100%

80%

60%

40% By differatiating the analogy between the agents and the predators the results could be more or less dense.

frameCount 100 numAgents 5.000 numPredators 50 maxAgeTrailPoint 200

20%

cohesionRange 10 cohesionStrength 0.000001 separationRange 2 separationStrength 0.03 alignmentRange 5 alignmentStrength 0.02 predatorRange 8 predatorStrength 3 predGravity -1 maxSpeed 0.30 maxSpeedPredator 1.0 springLength 0.05 springStrength 0.0001

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0

D E N S I T Y ST U DY B

80

100%

80%

60%

40% As the frameCount increases, the results are denser and fill up more space.

frameCount 200 numAgents 5.000 numPredators 50 maxAgeTrailPoint 200

20%

cohesionRange 10 cohesionStrength 0.000001 separationRange 2 separationStrength 0.03 alignmentRange 5 alignmentStrength 0.02 predatorRange 8 predatorStrength 3 predGravity -1 maxSpeed 0.30 maxSpeedPredator 1.0 springLength 0.05 springStrength 0.0001

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0

D E N S I T Y ST U DY B

82

100%

80%

60%

40% Color

variation

is

achieved

here through mapping of the population of connectivity.

frameCount 200 numAgents 5.000 numPredators 50 maxAgeTrailPoint 200

20%

cohesionRange 10 cohesionStrength 0.000001 separationRange 2 separationStrength 0.03 alignmentRange 5 alignmentStrength 0.02 predatorRange 8 predatorStrength 3 predGravity -1 maxSpeed 0.30 maxSpeedPredator 1.0 springLength 0.05 springStrength 0.0001

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Faาชade| Interior Scene Architectural Skin using Agents & Voxels

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D E N S I T Y ST U DY B

86

100%

80%

60%

40% The density of the line can result to a more thicker structure.

frameCount 400 numAgents 5.000 numPredators 50 maxAgeTrailPoint 200

20%

cohesionRange 10 cohesionStrength 0.000001 separationRange 2 separationStrength 0.03 alignmentRange 5 alignmentStrength 0.02 predatorRange 8 predatorStrength 3 predGravity -1 maxSpeed 0.30 maxSpeedPredator 1.0 springLength 0.05 springStrength 0.0001

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0

D E N S I T Y ST U DY B

88

100%

80%

60%

40% The density of the line can result to a more thicker structure.

frameCount 500 numAgents 5.000 numPredators 50 maxAgeTrailPoint 200

20%

cohesionRange 10 cohesionStrength 0.000001 separationRange 2 separationStrength 0.03 alignmentRange 5 alignmentStrength 0.02 predatorRange 8 predatorStrength 3 predGravity -1 maxSpeed 0.30 maxSpeedPredator 1.0 springLength 0.05 springStrength 0.0001

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r andomness

P ERLI N NOI SE

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2

2

3

4

Voxel-based structures using different Noise Threholds 1 threshold = 0.40 2 threshold = 0.60 3 threshold = 0.50 4 threshold = 0.45

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RANDOMNESS | PERLIN NOISE

PERLIN NOISE Perlin Noise, first introduced by Ken Perlin in 1985 is included in the larger area of lattice gradient noises, as the most delegate example; noise is therefore produced by the interpolation or entwinement of random values, which are designated at the integer lattice nodes 1. This algorithm was initially designed for the creation of procedural textures for computer-generated 5

effects; noise gives more harmonic results, because it produces a naturally ordered sequence of random numbers which can be easily accessed and determined via the mostly known ‘seed value’, a value, initialized in the setup in order to produce a pre-arranged random sequence. Perlin Noise is most commonly implemented as a two-, three- or four-dimensional function, but can be defined for any number of dimensions. An implementation typically involves three steps: grid definition with random gradient vectors, computation of the dot product between the distance-

6

gradient vectors and interpolation between these values. Perlin Noise has been beneficial to this proposal for multiple reasons. Procedural techniques result in compact and light-sized 1

a short jump in time

a long jump in time

outputs, which is not only suitable but critical from the beginning of this study, which aims

noise value

to processing copious amounts of data. Moreover, this type of representation is not limited to an explicit resolution, but it can provide full detail in all range of scales, as well as be t += 0.01

0

extended in n-dimensional space, without any evidence of periodicity. Poly-scalar behavior

t += 0.1

has continuously remained a basic feature of this project, which rendered procedural noise

time

7

appropriate to use and research on. In addition, procedural textures can be parameterized, new max current max

width

0 current min

to obtain an extensive catalogue of potential design outputs during the research. Then, this function can be also accessed in a random way, which allows constant evaluation without

1

value

meaning that they can lead to a large spectrum of patterns, which proved valuable in order

new value

interference of other parameters and consequently provides an efficient use of multi-pipe GPU and multi-core CPU 2. As it has been already stated in this report, the use of big-data

0 new min

new value = map(value, current min, current max, new min, new max)

in computational design, casts back and emphasizes computer performance and capacity issues.

8 diagram 5 | lattice noise stores a number from the random number generator at each integer lattice point in a triple array. If the object’s point has non-integer values, the function uses trilinear interpolation to determine the returned value. (source: https://www.siggraph.org/) diagram 6 | gradient noise generates random unit vectors for each integer lattice point, and uses interpolation to find values for non-integer coordinates (source: https://www.siggraph.org/) diagram 7 | graph showing the structure of noise over time diagram 8 | noise mapping allows the reconfiguration of noise values to a larger amplitude

1 A Lagae S Lefebvre R Cook T DeRose G Drettakis D S Ebert J P Lewis K Perlin and M Zwicker, ‘A Survey of Procedural Noise Functions’, in Computer Graphics forum, Vo 29 number 8, 2010, pp 2579-2600.

2 D S Ebert, F K Musgrave, D Peachey, K Perlin, S Worley, Texturing and Modeling: A Procedural Approach (3rd edition). Morgan Kaufmann Publishers, Inc., Massachusetts, USA, 2002.

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Different qualities generated by Perlin Noise (image 1) differentiation in threshold (image 2) uniform connectivity (image 3) intricate details (image 4) spatial design

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RANDOMNESS | PERLIN NOISE

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Classification of Perlin Noise parametres as reflected in design

THRESHOLD [1]

0.50

THRESHOLD [1]

0.50

THRESHOLD [1]

0.50

THRESHOLD [1]

0.55

THRESHOLD [2]

0.40

THRESHOLD [2]

0.60

THRESHOLD [2]

0.63

THRESHOLD [2]

0.63

SCALE [1] X

0.01

SCALE [1] X

0.01

SCALE [1] X

0.01

SCALE [1] X

0.01

SCALE [1] Y

0.01

SCALE [1] Y

0.01

SCALE [1] Y

0.01

SCALE [1] Y

0.01

SCALE [1] Z

0.01

SCALE [1] Z

0.01

SCALE [1] Z

0.01

SCALE [1] Z

0.01

SCALE [2] X

0.05

SCALE [2] X

0.20

SCALE [2] X

0.15

SCALE [2] X

0.08

SCALE [2] Y

0.05

SCALE [2] Y

0.01

SCALE [2] Y

0.01

SCALE [2] Y

0.02

SCALE [2] Z

0.05

SCALE [2] Z

0.10

SCALE [2] Z

0.30

SCALE [2] Z

0.16

OFFSET [1]

0.03

OFFSET [1]

0.03

OFFSET [1]

0.03

OFFSET [1]

0.03

OFFSET [2]

0.05

OFFSET [2]

0.10

OFFSET [2]

0.12

OFFSET [2]

0.12

NOISESEED

50

NOISESEED

30

NOISESEED

50

NOISESEED

50

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RANDOMNESS | PERLIN NOISE

LIMITS

SURFACE

THRESHOLD [1]

0.45

THRESHOLD [1]

0.60

THRESHOLD [1]

0.48

THRESHOLD [1]

0.48

THRESHOLD [2]

0.45

THRESHOLD [2]

0.60

THRESHOLD [2]

0.60

THRESHOLD [2]

0.65

SCALE [1] X

0.01

SCALE [1] X

0.02

SCALE [1] X

0.02

SCALE [1] X

0.01

SCALE [1] Y

0.01

SCALE [1] Y

0.01

SCALE [1] Y

0.01

SCALE [1] Y

0.01

SCALE [1] Z

0.01

SCALE [1] Z

0.01

SCALE [1] Z

0.00

SCALE [1] Z

0.01

SCALE [2] X

0.03

SCALE [2] X

0.03

SCALE [2] X

0.02

SCALE [2] X

0.20

SCALE [2] Y

0.03

SCALE [2] Y

0.02

SCALE [2] Y

0.02

SCALE [2] Y

0.01

SCALE [2] Z

0.3

SCALE [2] Z

0.01

SCALE [2] Z

0.00

SCALE [2] Z

0.40

OFFSET [1]

0.03

OFFSET [1]

0.03

OFFSET [1]

0.03

OFFSET [1]

0.03

OFFSET [2]

0.12

OFFSET [2]

0.15

OFFSET [2]

0.15

OFFSET [2]

0.15

NOISESEED

40

NOISESEED

40

NOISESEED

40

NOISESEED

50

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APPLICATIONS

DESI GN P ROP OSA LS

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D E S I G N A P P L I C AT I O N S | C O L U M N S T U D Y

CO LU M N ST U DY Α

Column Variations Instatiating an architectural column, using different Perlin Noise values and parametres in order to achieve diverse results in terms of visibility through the lattice object as well as differentiation in light infiltration.

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D E S I G N A P P L I C AT I O N S | C O L U M N S T U D Y

CO LU M N ST U DY Α

Column Variations Culumn closeups. Perlin Noise values are evenly mapped to the three-dimensional grid of voxels, generating different types of connections among adjacent voxels.

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D E S I G N A P P L I C AT I O N S | C O L U M N S T U D Y

CO LU M N ST U DY Î’

Column Variations Areas with structural performance consist of more complex connections in terms of directionality. In contrast, areas with no structural capacity, consist of more uniform connectivity which allows light to pass through and enables visibility through the architectural element.

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D E S I G N A P P L I C AT I O N S | C O L U M N S T U D Y

CO LU M N ST U DY Î’

STRONG LIGHT Column Design Study Column geometry is here generated as Z-Axis aggregation and twists as it grows up.

Two different areas of porosity are being descritized for better performance.

Therefore, pink parts are structurally stronger which mean will contain denser and more complex connectivity. In contrast, cyan parts contain uniform connectivity, following the same directionality.

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example of a vertical-shape reinforced area

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D E S I G N A P P L I C AT I O N S | W A L L S T U D Y

W A L L ST U DY

example of a complex-shape reinforced area

The Wall Study examines the potentiality of Perlin Noise attributes as these applied in two different scales; in whole areas of the surface as well as in directionality in micro-scale. Areas which require structural performance, should consist of connection of diverse angles, in order to be sufficiently strong. For these reasons there has been a study of variant types of reinforcement. Here there are two examples mentioned, one with vertical reinforcement and the other of more complex shape. For this diversification, multiple parametres should be checked accordingly; threshold, noise scale in Z Axis and noise octaves, as well as the initial validity of the input vector field before this has been infused with Perlin Noise.

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D E S I G N A P P L I C AT I O N S | W A L L S T U D Y

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D ES I G N | CO LO R E X P E R I M E N TS

S PA T I A L S T U D Y & C O L O R E X P E R I M E N T S

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Spatial design experimentations adding color application

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FRONT VIEW

RIGHT VIEW

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LEFT VIEW

BACK VIEW

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D E S I G N A P P L I C AT I O N S | S PAT I A L D E V E L O P M E N T

pavilion top view

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IN DETAIL

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| P O LY- S C A L A R R E S O L U T I O N

CO N N E C T I V I T Y C ATA LO G U E

IN DISTANCE

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D E S I G N A P P L I C AT I O N S | S PAT I A L D E V E L O P M E N T

pavilion perspective scene

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super-PERFORMANCE

L IGHT i nf i lt r ati on

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S U P E R - P E R F O R M A N C E | L I G H T- I N F I LT R AT I O N

L I G H T F I LT E R I N G “Architecture is the masterly, correct and magnificent play of volumes brought together in light.� —Le Corbusier

The systematic management of directionality, can even more efficiently permit the divergence of porosity and it also emphasizes two important architectural qualities; the light infiltration and the visibility. As a result, areas with more elaborate patterns of connectivity incommode visibility, express high detail in design and depending on the light source, they may have an intricate and dramatic distillation of illumination. On the contrary, areas with uniform and simpler connections, enhance visibility, they are low-resolution and allow the light diffusion. According to the above observations, it is essential that the two predominant categories are consistently synthesized, in order to generate a composite of complexity, visibility and light performance into a high-resolution fabric of architecture.

Rendering instances from different angles or even rendering whole simulations with light transitions has been an essential exercise in order to evaluate resulted outputs and take critical design decisions. Effectively, after recursive tests and simulations, the effective control over the random input is suitably translated to the architectural objective of a porous, cloudlike lattice structure.

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Frame 43

Frame 275

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As the viewer wanders around the structure, he experiences a variety of light qualities that changes accordingly towards the day. Because of the different patterns towards the whole structure the light is filtered similar to a cloud. The porosity of our components allows the rays of light to pass through and absorb the daylight. By changing the parameters each time in the most noisy and complex areas the rays do not pass through blocking the light, whereas in others, more cleaner areas the rays pass though and illuminate the space.

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Azimuth: 147 Elevation: 54

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Time: 11:30 Azimuth: 154 Elevation: 57

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S U P E R - P E R F O R M A N C E | L I G H T- I N F I LT R AT I O N

spatial high-resolution lattice, interior scene

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post validity-check

OP T I MI Z ATI ON

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post validity check/ guidelines A

LAYER BY LAYER SCAN

B

( Z + X DIRECTIONS )

C

ADDITIVE PROCESS

D

COLLISION DETECTION

E

ELIMINATION OF W EAK

F

RESPECT ACTUAL GEOMETRY

O P T I M I Z AT I O N | V A L I D I T Y C H E C K & S T R U C T U R A L D E V E L O P M E N T

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RULE 01 Elimination of non-supported geometry

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RULE 02 Enhancing stability of structure - adding diagonal lines

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RULE 03 Connect disconnected parts

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RULE 04 Adding vertical lines to support diagonal ones

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RULE 05 Avoid cross connections that could cause collisions

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O P T I M I Z AT I O N | V A L I D I T Y C H E C K & S T R U C T U R A L D E V E L O P M E N T

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Research on design of a cloud-like structure by testing different parameters of noise values

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Creating connections by forming lines according to velocity inside of voxels

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Apply scans on different directions as a post process to ensure the structural performance

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The rules are created so the structure could be fabricated by the robot following a certain toolpath

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The robot starts from bottom and moves up creating a toolpath in order to fabricate on the most efficient way

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CONNECTIVITY variations

p ot ent i a li ty

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C O N N E C T I V I T Y C A TA L O G U E

In order to rationalize the process and easily test and evaluate the generated results, we examined cubes of 7x7x7 voxels with 2-3 connections per voxel. This resulted in a long connectivity catalogue due to different noise parameters.

A list of diverse areas, in terms of directionality and density, therefore arose where noisy and neat fields were being combined. This provided us with endless possibilities in order to achieve different amounts of complexity and different typologies in geometry.

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C O N N E C T I V I T Y V A R I AT I O N S | P O T E N T I A L I T Y

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construction scheme

FA BRI C ATI ON

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1st phase: connections from noise parameters

1nd phase: maintaining only horizontal lines

3rd phase: adding triangles on place of vertical and diagonal lines

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S T R A T I F I E D PA T H E X P L O R A T I O N For our early tests though, we formed a more simpler stratified path exploration algorithm that works as an easy and straightforward process:

• •

working layer by layer starting from the bottom and buiding up to the top maintaining only horizontal connections from our elements and eliminating the ones on Z-axis

•

the extrusion initiates firstly from the horizontal lines on each level creating the plan

•

afterwards new connections are added in z-axis that follow the horizontal ones

•

from each horizontal connection between two voxels, one vertical and one diagonal line is added

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1st level

2nd level

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3rd level

4th level

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FA B R I C AT I O N | PA T H - F I N D I N G A L G O R I T H M

PA T H - F I N D I N G A L G O R I T H M To rationalize the robotic fabricating process, we worked into generating the path-finding algorithm based on robotic physical constraints as well as experiences with disdrete pathprinting tests, which in turn clarified significantly our initial digital design and definitely articulated the practicalites of architectural application. The pivotal strategies are as follows.

1. Predefined the maximum connections generated from each voxel by three, and executed the primary bottom-up scanning to the initial geometry by applying algorithmic functions that check the underneath 9 nearby voxels of each voxel to determine if there are some connections exist between the checked one and the lower adjacent voxels, it would be kept temporarily in current step as an active allocation, otherwise it would be eliminated and deactivated as a flying point without supporting connections from bottom that is unprintable in physical fabrication.

2. Additional top-down scanning procedure was adopted and checked voxels that contain vertical or diagonal connections in order to generate extra necessary supporting lines. If the voxel being examined have only one spatial connection perpendicularly or diagonally, the extra vertical or diagonal line would be added connecting to the closest voxel in the lower level.

3. The third customized process essentially utilizes the cluster composing logic that incorporates the certain location data as well as the density of connections and the bottomup holistic structural analyses, attempting to ensure the stability of potentially printable computational model after the initial post-optimizations.

4.Based on the prior scanning techniques, the robot agents are programmed to crate the trajectory of robotic movements as toolpaths related to physical constraints of fabrication process together with the ability to generate response to input values pertaining to structural analysis data. Through close negotiation with constraints with regard to the searching order of active targets along with printing sequences among discrete paths in certain level, the generated toolpaths turned out to be feasible and efficient in avoid collision and singularities.

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ground level horizontal lines vertical lines added lines eliminated lines

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5th level

6th level

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FA B R I C AT I O N | R O B O T I C S PAT I A L E X T R U S I O N

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FA B R I C AT I O N | R O B O T I C S PAT I A L E X T R U S I O N

1 . DATA RES O U RCES Design Concepts Formation Computational Logic Environmental Information

2. INTERFACE

3. D IGITA L CO NTRO L SYSTE M

HARDWARE PLATFORM

SOFTWARE PLATFORM

Input Devices

Processing

CPU/GPU

Arnold

Transmission Channel

Rhino

Output Devices

Grasshopper

HEATING CONTROL Arduino

RobotStudio Arduino

MOTOR CONTROL Arduino

Motor

Digital Input Circuits

Transmitter 4. RO B OTIC END -EFFECTO R SYST E M NOZZLE Heater

Sensor 5 . FA B RICATIO N O U TCOM ES

Material

RO B OT I C W O R K F LO W From computational or digital design to real fabrication, the designated data could be inherited to construction process via information exchange sequences through interface, process controller and end effector, known as the process of physical rationalization. At the same time, the fabricating courses give feedbacks to initial design platform. This principle encourages designers to deal with concepts, further developments as well as final fabrication in holistic views.

FABR I C ATI ON

END-EF F ECTOR

P ROC ESS CONTROLLER

I NTER FAC E

DATA

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M AT E R I A L R E S E A R C H At first we studied various types of material in certain properties i.e. density, flexibility, melting point as well as strength. Based on the speciality of spatial extrusion requirement which is different from crafting, molding or layer-bylayer printing, the choice of material is an essential factor to be not only lightweight, flexible and strong but also easy to be melted as well as cooled down.

In addition, it should be self-supporting after the process of solidification.

Compared to other materials such as carbon fiber, clay and timber, the plastic filaments, especially the thermoplastic polymer conspicuously exhibit favorable characteristics that are much lighter and cost-effective. When heated to approximately 200 degrees, it turns to a state of semi-solid which obtains an inclination to be extruded as long as applying tube-like tools and pressure and could create the shapes we want. And it is easily to become solid when cooling down to room temperature and provides the self supporting property.

PLA is a kind of lightweight, durable and environmental friendly material that outweigh ABS in many aspects, e.g. lower melting point, higher flexibility. What's more, it doesn't generate intense fumes during printing. As a result, PLA could be our choice of material in final fabrication.

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BASIC ABB Connectors

Setting Frameworks

Reinforced Components

FA B R I C AT I O N | S PAT I A L E X T R U D E R D E S I G N

MOTOR Stepper Motor

Gear

Heating Wire Connector

S PA T I A L E X T R U D E R D E S I G N In order to convert our design into physical application, we are trying to develop our own spatial extruder which could accommodate efficiently to manipulate the constant input signals from digital console, i.e. grasshopper definition. It is expected to resonate correctly according to input commands, in a way turning the extrusion process on and off to rationalize the diecrete toolpath. And owing to the nonplanar connections generating in Z-Axis, the cooling system are essential appliance to solidify the vertical or diagonal lines in order to be self-supporting.

COOLING SYSTEM

Cross Connector Cooling Air Pipe Female Connector Copper Pipe

END EFFECTOR Aluminium Connector

Teflon Pipe

Aluminium Nozzle

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E N D - E F F ECTO R D ES I G N Due to the density of multi-dimensional connections, the funsize nozzle with an sharp end is required to avoid collusion with the already extruded parts. In this case, the tip angle of our first version of nozzle is set to 80 degree, which could also prevent the material from sticking to the extruder and the length of the end effector is long enough to melt the PLA filament and extrude smoothly.

Filament Filament Hole Sensor Heater Room Temperature

Isolation Tap

Melting

Compressed Air

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After indefatigable extruding tests concerning temperature consistency and motor rotating speed, the second version of end effector is developed and optimized the extruding efficiency by shorten the distance between friction buffer part and melting area to achieve much smoother extrusion quality.

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Hole for Heater Aluminium Connector 1.500

Hole for Sensor

8.500

6.000 Teflon Pipe

Hole for Srew 3.000

35.000

Hole for Filament

Sensor Heater

8.500

60

4.000 20

12

30

6.000 15

5 3.000

28

45

2.500

10

3.000 15

170

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FA B R I C AT I O N | R O B O T I C S PAT I A L E X T R U S I O N

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Gear

Motor

Teflon Pipe

Filament

Receive the Input Signal to Control the extrusion on/off

Pressure Intensity Control

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Melting Material

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WORKING PRINCIPLE OF S PA T I A L E X T R U D E R Greatly differed from the traditional makerbot or even larger 3D Printing process of layer by layer principal, the speciality of spatial extrusion is based on both structural technique and materiality to realize the self-supporting performance during the printing construction. The indispensible components are cooling system design and extruding manipulations as well as their controlling units.

Controlling the cooling System on/off

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Owing to the complex logic of digitally controlling our valid toolpaths, based on the physical constraints of robotic fabrication, it is necessary for us to customized the special robotic end effector together with the controlling system in order to practice different trials with real environment instead of computational simulation.

FA B R I C AT I O N | R O B O T I C S PAT I A L E X T R U S I O N

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PHYSICAL SETUP STRUCTURE

ROBOTIC ARM

P R I N T I N G M AT E R I A L ARDUINO BOARD

STEPPER MOTOR FRAMEWORK OF EXTRUDING SYSTEM

D I G I TA L O U T P U T S I G N A L R E C E I V E R

P O W E R S U P P LY

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2

1

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5

6

8

7

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CONTROLLING SYSTEM DESIGN

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LCD display module to monitor the changing of temperature in end effector

1

PC case fan to cooling down the whole kit of circuits

2

24V relay module manipulating digital outputs to control motor's on/off

3

Easy driver to control motor speed and rotating diections

4

Arduino Uno boards to offer commands

5

5V relay module to control heating threshold

6

Major stripboard to prototyping circuits

7

Minor stripboard to connect heaters and controlling signals

8

6mm Plywood board framework

9

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A B U N D A N C E VR

VI RT UA L REA LI TY

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VIRTUAL REALITY | ABUNDANCE

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VR

VR

The emergence of Virtual Reality applications in design and architectural developments has been one of the biggest stories of the past few years .

Progressively and radically, VR is becoming an integral part not just of presenting a project, but of the design process as well. The computational workflow and the programmatic approach of the project has offered a resilience of transforming every architectural phase into an algorithmic logic. Applying Virtual Reality, has been therefore an additional, experiential task of ‘Abundance’. Apart from using high-end technology, it has been a challenge in confronting highly-demanding problems which mostly concern issues of computability. Consequently, the use of Unity, as a Game Engine, which widely integrates VR apps has been a new chapter of ‘Abundance’. Scripting in C# in extending Monobehabior as well as Batching directly in the Editor/ System has eventually allowed to instantiate the high-resolution paradigm, in a reasonable way, in order to achieve the expected framerate for Virtual Reality runtime speed (~60 fps). For the final output we have used an HTC Vive along with SteamVR, supported by OpenVR Software Development Kit (SDK).

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Current page Unity Play-mode instances Next Page VR Stereoscopic Rendering Interaction with light-performance effects in realtime

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Mikaela Psarra has graduated from the School of Architecture, National Technical University of Athens and received funding for Postgraduate Studies from the Foundation for Education and European Culture.

During the Master in Bartlett she expanded her research in Computational Design and VR.

Eleni Chalkiadaki has graduated from the School of Architecture, National Technical University of Athens.

During the Master in Bartlett, she emphasized her research in Architectural Design and Visualisation.

Jingwen He has graduated from the School of Architecture at the Zhejiang University in China and Tokyo Institute of Technology in Japan. During the Master in Bartlett, she focused her interest in Fabrication, Robotics and Grasshopper.

Abundance Team

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A Andrasek, Open Synthesis // Toward a Resilient Fabric of Architecture. Log, 25, Summer 2012, pp 45-54.

A Andrasek, ‘Indeterminacy & Contingency’, in Architectural Design, Vol 85 Issue 3, May 2015, pp 106-111 Andrasek, A. & Andreen , D., 2015. Activating the invisible: data processing and parallel computing in Architectural Design. Intelligent Buildings International, 8(2), pp. 106-117.

Bandel, H. (2001). ‘Structure Systems/Tragsysteme’, Berlin: Hatje Cantz P Bausch, T Heinonen, P Lahti, Heisenberg’s Uncertainty Principle, Physics Reports 452, May 2007, pp 155-176 Bonabeau, E., Dorigo, M. & Theraulaz, G., 1999. Swarm intelligence. 1st ed. New York: Oxford University Press.

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Budig, M, Lim, J and Petrovic, R 2014, ‘Integrating Robotic Fabrication in the Design Process’, Architectural Design, vol. 84, no.3, pp. 20-28.

Retsin, G., Jimenez Garcia, M. and Soler, V. (2017). Discrete Computation For Additive Manufacturing. London: The Bartlett School of Architecture, UCL, pp.178-179.

P M Carranza, ‘Programs as Paradigms’, in Architectural Design, Vol 84 issue 5, September/October 2014, pp 66-73 M Carpo, Breaking the Curve, Artforum International, R Morris and K Attia, eds, February 2014, pp 169-173.

M Carpo, The Alphabet and the Algorithm, MIT Press, February 2011 M Carpo, “Parametric Notations: The Birth of the Non-Standard”, AD, February 2016 M Carpo, ”Excessive Resolution”, in Architectural Design, Vol 86 Issue 6, November 2016, pp 78-83.

J Cohen, ‘Non-Deterministic Algorithms’, Computing Surveys, Vol 11 No 2, June 1979 M De Landa, Philosophy and Simulation: The Emergence of Synthetic Reason, Continuum: London, 2011, pp 7-21.

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Gershenfeld, N., Carney, M., Jenett, B., Calisch, S. and Wilson, S. (2015a) ‘Macrofabrication with Digital Materials: Robotic Assembly’, Architectural Design: Material Synthesis: Fusing the Physical and the Computational, v.85(5): pp.122–7 Gramazio, F., Kohler, M. 2008, 'Digital Materiality in Architecture', Lars Muller Publishers, Baden.Gyurky, S. M. d., 2006. The Cognitive Dynamics of Computer Science: Cost-Effective Large Scale Software Development. 1st ed. NJ, USA: John Wiley & Sons, Inc..

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Lipson H, Kurman M (2013) Fabricated: the new world of 3D printing. Wiley, London

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G Longo, Critique of Computational Reasoning in the Natural Sciences, Fundamental Concepts in Computer Science 3, 2009.

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Shiffman, D., 2012. The Nature Of Code: Simulating Natural Systems with Processing. s.l.:s.n. TD Wolf and T Holvoet, Emergence and Self-Organisation: Stam, J., 1991. A Multi-Scale Stochastic Model for Computer Graphics, Toronto, Ontario, Canada: Department of Computer Science, University of Toronto.

Ward, J., 2010. 'Additive Assembly of Digital Materials'. PHD Thesis, Massachusetts Institute of Technology

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2016–2017 LO N D O N