MI-LUNG (MArch)

MI-LUNG Meet Dhanesha (M.Arch) Maria Falivene (M.Arch)

ARCHITECTURAL ASSOCIATION SCHOOL OF ARCHITECTURE GRADUATE SCHOOL PROGRAMMES PROGRAMME Emergent Technologies and Design YEAR 2022-2023 FOUNDING DIRECTOR: Dr. Michael Weinstock COURSE DIRECTOR Dr. Elif Erdine STUDIO MASTER Dr. Milad Showkatbakhsh STUDIO TUTORS Dr. Naina Gupta, Paris Nikitids, Felipe Oeyen, Lorenzo Santelli, Dr. Alvaro Velasco Perez, Fun Yuen COURSE TITLE Master in Architecture Dissertation.

Emergent Technologies and Design [Em-Tech]

ACKNOWLEDGMENTS

DISSERTATION TITLE MI-LUNG

We would like to express sincere gratitude to every one of those individuals in the Emergent Technologies and Design graduate (EmTech) programme at the Architectural Association School of Architecture whose assistance and support made this research project possible. This exploration would not have been possible without their incredible aid, direction, and steadfast dedication to the quest for knowledge.

STUDENT NAMES Meet Dhanesha ,

First and foremost, we would like to sincerely thank our course founder, Dr Michael Weinstock, director, Dr. Elif Erdine and co-director, Dr Milad Showkatbakhsh, who served as our research adviser and whose advice and knowledge were invaluable to us during this undertaking. Along with them, our other studio tutors were also the guides throughout this process. Their continuous support, priceless advice, and mentorship significantly improved the standard and scope of our work. We are also incredibly grateful to our digital prototyping lab staff, whose commitment to promoting cutting-edge technology for the application in the field of architecture for this research project. We would also like to thank our M.Sc group mates - Yuyu Fu and Asad Nasir Qureshi with whom this research domain was initiated.

Maria Falivene.

DECLARATION “I certify that this piece of work is entirely my/our and that my quotation or paraphrase from the published or unpublished work of other is duly acknowledged.

SIGNATURE OF THE STUDENT DATE 12 January, 2024 th

Meet Dhanesha

Maria Falivene

We thank our friends, colleagues, and families for their constant support, tolerance, and inspiration during this study adventure. Their comprehension of our efforts and confidence in them served as a continual source of inspiration. Finally, from librarians to technical support personnel (digital prototyping lab and fabrication lab team) and everyone in between, we want to thank the innumerable people who helped make this research a success, whether directly or indirectly.

This research is a testament to the power of collaboration and collective effort, and we are truly humbled by the support we have received. Although it would be impossible to thank everyone individually, please accept our sincere gratitude for your vital contributions to this research project.

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ABSTRACT Keywords: Energy consumption, Air pollution, Micro-climate (Thermal comfort), Carbon footprint, Bio-composite materials, Passive environmental design.

“Buildings breathe through MI-LUNG of passive ventilation, exhaling efficiency and inhaling a greener tomorrow.”

The escalation in population has led to increased urban density owing to the readily available resources facilitated by globalisation. This urbanisation process has solidified the natural topography, displaced indigenous green spaces, and disrupted the equilibrium of the natural ecosystem—a pivotal source of air purification. Additionally, this urbanisation has contributed to a rise in temperature due to the heat-island effect. Despite urban cities encompassing a mere 3% of the Earth’s surface, they accommodate approximately 50% of the global population and are responsible for over 75% of annual worldwide greenhouse gas emissions. Notably, energy production alone contributes at least 55% to these emissions.

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Consequently, this research focuses on developing a retrofitted passive ventilation system designed for a complex of office buildings in Milan, Italy. Milan was selected due to its designation as one of the most polluted cities, specifically in the summer months of each year, characterised by exceptionally high atmospheric pollutant levels (PM 2.5). The envisioned system aims to sequester carbon and passively ventilate the indoor office space by supplying fresh air passively to the existing building’s HVAC system, thereby ameliorating the universal thermal comfort index and reducing overall building energy consumption. This, in turn, contributes to decreasing greenhouse gas emissions from energy production. The system also enhances outdoor thermal comfort and fosters a cultural environment around the buildings, rendering the spaces functional throughout the day. The design of this system is informed by environmental factors and principles of biomimetics, drawing inspiration from termite mounds for the evolution of passive ventilation strategies. Additionally, an agrobased material composition has been formulated, promoting the growth of biofilm that can sequester carbon while emitting fewer carbon emissions compared to conventional building materials.

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Within the context of Milan, the prevailing climate change era, a pressing imperative exists to curtail energy consumption, thereby mitigating the adverse impacts of air pollution and carbon emissions on the environment. This research endeavours to devise a retrofitted passive ventilation system tailored for a conventional office building. The inclination for such an undertaking stems from energy consumption; office spaces’ universal thermal climate index is double that of residential spaces. Furthermore, the spaces surrounding office buildings are maximally utilised during office hours, which coincide with peak daytime conditions characterised by elevated humidity and solar radiation. Conversely, these spaces become dormant outside of office hours.

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MI-LUNG Comfort Index (UTCI), solar radiation, and wind movement.

Firstly, the development of porosity panels, abstracted from the bionic structure of termite mounds and guided by multiobjective optimisation and Computational Fluid Dynamics (CFD) analysis, was aimed at creating an optimal gradient for capturing and channelling filtered fresh air within the passive convection loop responsible for ventilating the building from the exterior. Simultaneously, an iterative process informed the evolution of a retrofitted passive ventilation system between two buildings, considering insights from wind and solar radiation assessments throughout the year. Multiobjective optimisation along with Kangaroo physics simulation was employed to evolve this environmentally responsive morphology of this system, which can capture maximum wind and solar radiation for efficient functioning. The resulting morphology, integrated with various experimental models, harnesses wind and solar energy year-round, enhancing internal thermal comfort and outdoor urban thermal comfort while fostering a culturally vibrant environment around the building.

Our investigative inquiry commenced with an exhaustive examination of non-conventional materials, given their potential to diminish the overall carbon footprint of developments. Milan, endowed with substantial agricultural waste owing to extensive farmlands, prompted our exploration of agro-based materials. These materials not only optimise the utilisation of agricultural waste but also foster bio-film growth, contributing to carbon sequestration and augmenting energy efficiency by improving thermal comfort, thereby addressing various concerns inherent in sustainable building practices.

To counteract the ramifications of escalating urbanisation, marked by increased energy consumption and greenhouse gas emissions, our investigation exemplifies a dedication to advancing strategies that reconcile the imperatives of environmental stewardship with those of urban growth.

Our research methodology incorporated state-of-the-art technologies such as the Ladybug and Honeybee plugin for Grasshopper and Autodesk CFD (Computational Fluid Dynamics) analysis to address the intricacies of environmental design. In tandem with system development, finite element analysis optimised the overall structural system for enhanced structural outcomes. The scope of our study extended to include the optimisation of vents supplying captured air within the building, thereby improving passive cooling and convection loops. In conclusion, our research represents a comprehensive exploration of innovative methods for sustainable building practices, underscored by advanced materials, integration of biological systems, and design concepts drawn from nature.

Inspired by nature’s ingenuity, particularly the ventilation principles observed in termite mounds, our design concepts aim to enhance both outdoor urban thermal comfort and internal thermal comfort through a retrofitted passive ventilation system, which has been developed using biomimetic principles. Rigorous environmental evaluations guided our pursuit, encompassing the Universal Thermal

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The construction industry’s introduction of concrete, steel, and glass materials has markedly accelerated urban growth and development. Termed urbanisation, this developmental trajectory has solidified the natural topography, displaced verdant spaces, and disturbed the ecological balance essential for air purification, consequently engendering a rise in temperature attributable to the heat-island effect.

Globally, the construction sector contributes approximately 30% of total greenhouse gas emissions, stemming from the cumulative impact of raw materials, including steel and cement, as well as deforestation, landfills, and energy consumption associated with building operations. In addition to energy production and carbon emissions from concrete, building systems discharge pollutants that, while contributing to thermal comfort, adversely impact the environment. Our focal point of investigation is Milan, a city emblematic of urban development grappling with environmental challenges and a signatory to the EU Green Building Pact, committing to retrofitting existing buildings for heightened energy efficiency by 2050.

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INTRODUCTION

F.

CONTENTS A. B.

INTRODUCTION

C.

D.

61

VI.

CNC – Computer numerical control

62

VII.

Fused filament fabrication - 3D print

63

RESEARCH DEVELOPMENT

65

I.

MSC phase CFD experiments

68

II.

Material Experiment.

71

a.

Material application

72

b.

Experimental stages

74

c.

Ingredient and process

77

d.

Sample performance

80

11

e.

Physical test setting

83

I.

Contemporary cities & Climate change

12

III.

Site analysis – Milan, Italy

90

II.

Context of study

16

IV.

Usability of space outside office building

96

a.

Milan, Italy

16

IV.

Enviornmental urban analysis – Milan, Italy

98

b.

Principal issues & EU context

17

V.

Calculation according for system development

VI.

CFD analysis for morphology development

110

III.

Conventional building systems

22

IV.

Biomimetic system

24

a.

Body parts & their functions

24

b.

Mechanism and performance of termite mounds

c. d.

DESIGN DEVELOPMENT

107

25

I.

MSC overview and conclusion

126

Various principles of mound by various researchers

26

II.

Morphology

135

Environmental impact on termite mound morphogenesis.

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a.

Form finding

137

Bio-composite material system

34

a.

Food waste in Milan

34

b.

Structure

152

b.

Wood Foam

35

c.

Panelling

158

c.

Biofilm

36

Porosity panels

164

a.

Bionic principles

167

b.

Computation of Type ‘A’

171

c.

Computation of Type ‘B’

181

d.

Computation of Type ‘C’

191

e.

Application process

202

CASE STUDIES

39

I.

Urban Sequoia, SOM Architects

40

II.

Melbourne city council house-2, DesignINC

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RESEARCH QUESTIONS

G.

114

III.

53

METHODOLOGY

55

I.

Evolutionary Algorithm

56

II.

Kangaroo physics simulation

57

II.

FEA – Finite Element Analysis

58

III.

CFD – Computational Fluid Dynamics

59

IV.

Shortest Path

60

H.

IV.

System working

214

V.

Column development

217

VI.

Tubular network system

219

VII.

Integrated system

222

CONCLUSION

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Robotic Arm

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V.

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U r b a n p o p u la t io n p r o je c t e d t o 2 0 5 0 , W o r ld , 2 0 1 0 t o 2 0 5 0 T o t a l u r b a n p o p u la t io n , g iv e n a s e s t im a t e s t o 2 0 1 6 , a n d U N p r o je c t io n s t o 2 0 5 0 . P r o je c t io n s a r e b a s e d o n t h e U N W o r ld U r b a n iz a t io n P r o s p e c t s a n d it s m e d ia n f e r t ilit y s c e n a r io .

6 b illio n 4 b illio n

5 ,1 7

4 ,3 8

3 ,5 9

6 ,6 9

5 ,9 4

2 b illio n 0

2010

2015

2020

2025

2030

2035

2040

2045

2050

W o r ld

The carbon emissions associated with building construction and operational phases represent critical contributors to environmental degradation. The operational carbon footprint of a building denotes the release of carbon into the environment during the processes of heating or cooling interior spaces, inherently tied to the energy production circuit.

In the year 2021, fossil fuels accounted for a staggering 80% of the global energy supply, with oil constituting nearly 30%, followed by coal (27%) and natural gas (24%)2. The building sector, in turn, claimed a 30% share of global final energy consumption, with 27% attributable to emissions in the energy sector (8% being direct emissions from buildings and

heat employed in buildings) 3 (Figure 03). Within the carbon lifecycle of a building, reliance on non-renewable sources for heating or cooling escalates energy consumption, leading to a corresponding rise in carbon emissions. The extensive use of non-renewable energy in urban domains for construction and operations has profound repercussions for the environment, contributing to urgent planetary predicaments such as climate change and subsequently giving rise to natural disasters including ozone layer depletion, sea level rise, and glacier melting, thereby affecting both rural and urban topographies.

Globally, the construction industry is culpable for approximately 30% of total greenhouse gas emissions4, encompassing cumulative emissions from raw materials such as steel, cement, deforestation, landfills, and energy production deployed within the building sector. Furthermore, energy production for buildings accounts for 17.5% of carbon emissions, surpassing the contribution of any other resource used in buildings, thereby intensifying greenhouse gas emissions.

Fig. 01.

Population in Urban Cities,2021 Source: UN population division via world bank.

Fig. 02. Population in Urban Cities. Source: Our World in Data, June 13, 2018. https://ourworldindata.org/urbanization.

The carbon emissions pervading the environment adversely impact the air quality index, thereby detrimentally affecting

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S o u r c e : O W ID b a s e d o n U N W o r ld U r b a n iz a t io n P r o s p e c t s 2 0 1 8 a n d h is t o r ic a l s o u r c e s (s e e S o u r c e s ) 19% indirect emissions from the production of electricity and O u r W o r ld In D a t a .o r g /u r b a n iz a t io n• C C B Y

Urban centres, though comprising a mere 3% of the Earth’s surface, bear the weight of approximately 50% (Figure 01) of the global population, concurrently contributing over 75% of annual greenhouse gas emissions worldwide. Projections

by WHO indicate that 2050 urban residents will number 7.5 billion, mirroring the current global population (Figure 02). To accommodate this burgeoning populace, a twofold expansion of current urban surfaces would be imperative, exacerbating issues associated with urban densification and amplifying environmental challenges linked to greenhouse gas emissions.

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CONTEMPORARY CITIES & CLIMATE CHANGE

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Main emission sectors

NH3

NOX

NMVOC

SO2

PM2.5

BC

PM10

C0

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20%

30%

40%

50%

60%

70%

80%

90%

100%

Agriculture

Residential, commercial and institutional

Waste

Road transport

Manufacturing and extractive industry

Non-road transport

Energy supply

Other

Fig. 03.

Global greenhouse gas emissions by various sectors. Source: Climate Watch, the World Resources Institute (2020).(Left) Fig. 04. Contributions to EU Member States’ emissions of NH3, NO2, NMVOCs, SO2, primary[5] PM2.5, primary PM10, BC, and CO from the main source sectors in 2021.

Source: EEA Air Pollution Report 2023.(Righ Top)

Fig. 05. Exposure to air pollution with fine particulate matter,2017 Source: Brauer et al. (2017) via world bank.

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the quality of life for humans and other biological ecosystems.

Despite a significant concentration of the global population in urban areas, the air quality index in these locales is notably low due to the elevated presence of diverse contaminants, culminating in multi-graded detrimental effects on living systems. Airborne pollutants consist of solid particles (particulate matter), liquid droplets, and gases emanating from diverse sources, constituting a principal causative factor for numerous vascular and respiratory ailments in urban settings. To gauge and monitor worldwide air pollution rates, the World Health Organization continually updates guidelines, expressed in micrograms per cubic meter (µg/ m3). Notably, PM2.5, PM10, NO2, and SO2 (Figure 04) are paramount pollutants considered for policymaking due to their release into the atmosphere as by-products of energy consumption and mobility. Particularly noteworthy is PM2.5, which frequently exceeds WHO guidelines by two to eight times in various urban centres (Figure 05). These particulate contaminants, colloquially known as particulate matter, are emitted into the environment during the energy production process derived from non-renewable sources. 2023 report from EEA showcases that the maximum harful gasses in Europe is released in the enviornment is through eighter agriculture related activities or through usage of resources by residential, commercial or industrial usage. (Figure 04)

10%

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0%

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Cold air

Warm air

Smog *phenonemon that usually happens in winter

Cold air

CONTEXT OF STUDY MILAN, ITALY

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A 2020 European Environment Agency (EEA) study reveals that Milan is grappling with a severe air quality crisis, ranking 349th out of 375 cities for inadequate air quality.6 This alarming assessment underscores the city’s significant hurdles in addressing air pollution, a critical environmental and public health concern. World Health Organization (WHO) standards stipulate that the average 24-hour exposure to PM2.5 should not exceed 15 g/m3, with an optimal annual average concentration below 5 g/m3.7 However, Milan’s air quality falls starkly short of these benchmarks, with PM2.5 levels approximately six times higher than WHO recommendations, necessitating urgent and comprehensive interventions. (Figure 08) Recent research from the EEA in 2023 identifies energy use in residential, commercial, and institutional sectors as the primary source of PM2.5 pollutants in Milan.8 This undesirable distinction places Milan among the top 10 most polluted cities globally, ranking third on the list as of March 21 of the 2023.

Fig. 06.

Satellite view of Milan’s geographical context. Milan is highlighted in yellow.

Source: Google Earth.

Fig. 07. Diagram of thermal inversion that contributes to the formation of smog.

Source: Author.

In response to these environmental challenges, the European Commission launched the EU Green Pact in 2020, a groundbreaking proposal to steer Europe towards carbon neutrality by 2050.

The EU Green Buildings Accord, an integral component of this comprehensive initiative, stands out for its commitment to revolutionize energy consumption dynamics in public and residential structures. Buildings’ substantial influence

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The intricacies of Milan’s environmental conditions are compounded by its high population density and extensive human activities, including transportation, industrialisation, and urbanisation. These factors collectively contribute to elevated emissions of greenhouse gases and pollutants, exacerbating air quality degradation and hastening the impacts of climate change.

The city’s challenges are further compounded by the ineffective dispersion of emissions caused by unfavourable wind patterns, rendering Milan particularly susceptible to the thermal inversion phenomenon. (Figure 07) This atmospheric condition traps pollutants above the city, culminating in the persistent smog visible on the skyline5. The humidity level, averaging 82% throughout the year, coupled with variable wind flow from all directions, (Figure 10) underscores the complexity of Milan’s environmental dynamics, as illustrated in Figure 08.

PRINCIPAL ISSUES & EU CONTEXT

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Nestled within the Po River valley in northern Italy, Milan grapples with a distinctive set of challenges that are significantly influenced by its geographical location, which the Apennines hems into the south and the imposing Alps to the north. (Figure 06) The resultant topographical features of the valley engender a microclimate that gives rise to temperature extremes and fosters temperature inversions due to the thermal inversion-related effects on airflow emanating from the surrounding mountainous terrain. (Figure 07)

SURROUNDED BY MOUNTAINS

Humid & hot summer

No air circulation

THERMAL INVERSION

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HUMID SUB-TROPICAL CLIMATE

Humid & hot winter

POLLUTION WHO Guidelines

PM 2.5

NO2

<5 μg/m³

<10 μg/m³

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GEOGRAPHICS

MILAN Third most polluted city in the world in March 2023

MILAN, ITALY

28.6 μg/m³

44 μg/m³

> x6

> x4.5

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HIGH ENERGY USAGE

EU GREEN BUILDING PACT Atleast 15% of buildings in Italy should be retrofitted or replaced by new ones with zero emission.

B

C

F>E by 2030 G>E by 2027

D

E

F

60%

of total EU greenhouse emissions come from buildings

of buildings in Italy are F or G

However, Milan confronts a pressing challenge, with 60% of its buildings categorised under the lowest energy performance ratings, F or G.10 The majority of these structures were erected before the implementation of thermal regulations in the 1970s, marking the historical origins of the city’s energy inefficiency crisis and emphasising the profound need for comprehensive intervention.

G

Fig. 08. Main issues of Milan. Source: WHO, EEC, Source (left) Fig. 09. Bird’s eye view of Milan.Source : Google maps (right)

To carry forward the research extreme enviornmental conditions of Milan needs to be analysed. The enviornmental analysis using Ladybug plugin on Rhinoceros 3D-Grasshopper was done to understand the extreme wind speed, humidity and temperature for further system development. (Figure 10)

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36%

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on the surrounding environment propels this strategic approach, with data revealing that 40% of the energy consumed by structures in the European Union is significant and contributes to 36% of the region’s carbon dioxide emissions. The EU Green Buildings Pact delineates a compelling directive: to renovate and enhance at least 15% of the most environmentally detrimental buildings falling under the F to G energy efficiency ratings.9 (Figure 08) This challenging yet imperative target, encompassing factors such as building materials, insulation, heating and cooling systems, and energy consumption efficiency, aims to eliminate emissions from existing buildings by 2030. The EU’s steadfast support for this initiative underscores its commitment to fostering a sustainable future.

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CONCLUSION

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Climate analysis of Milan, Italy. Source: Ladybug, Grasshopper.

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Fig. 10.

The city of Milan, situated geographically in a locale susceptible to thermal inversion and characterised by high humidity, falls under the purview of the EU Green Building Pact. This pact mandates the upgrading of buildings with higher energy efficiency ratings to upper-efficiency levels by 2050, with 60% of Milan’s buildings currently falling within this ambit. The pressing need to diminish energy consumption and mitigate environmental impacts propels the urgency to retrofit these antiquated structures in Milan. In light of this imperative, adapting existing edifices and strategically planning for new constructions becomes imperative. Future architectural designs must possess the capability to absorb external pollutants while minimising energy utilisation by enhancing the thermal comfort indoors to outdoors. Consequently, this research endeavours to develop a passive ventilation system capable of diminishing energy consumption by enhancing thermal comfort within a space. The integration of this passive ventilation system aims not only to reduce greenhouse gas emissions but also to sequester carbon, thereby mitigating particulate matter levels in the surrounding context.

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Globalisation has precipitated a surge in the proliferation of urban centres, primarily driven by the accessibility of resources.

However, this accelerated urban development is intricately linked to environmental repercussions, chiefly manifesting in increased greenhouse gas emissions. Of noteworthy concern is the substantial contribution of energy consumption by buildings in urban locales, constituting a significant proportion of the global energy production responsible for greenhouse gas emissions. A pivotal imperative emerges to effectively curtail overall greenhouse gas emissions: reducing energy consumption in buildings within urban environments.

1% HVAC system utilizes 38%

39%

HVAC

of the total energy used by building

residential

commercial

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4%

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9%

commerical to residential energy consumption ratio 2:1

Lighting

UTCI value of residential room

Equipment

22%

Lifts Domestic Hot Water Other

25% Effecient passive ventilation system to reduce energy consumption and air pollution

UTCI value of office space

Addressing the requisite airflow within interior spaces, crucial for achieving thermal comfort, aligns with broader objectives of mitigating air pollution and enhancing energy efficiency.

As depicted in Figure 03, the maximum energy consumption in an office building attributed to HVAC systems amounts to 39%. Furthermore, as elucidated in the preceding chapter, the preeminent source of greenhouse gas emissions arises from energy production for building usage, surpassing other resources employed within the building.

Distinctly, the energy consumption in residential buildings is comparatively lower than that in office buildings, attributable to factors such as occupancy rates and electronic loads.

Statistical data underscores this contrast, revealing that commercial buildings utilise twice the energy their residential counterparts consume. Also, we see in Figure 12 that the UTCI values, which is the Universal thermal comfort index of a commercial unit, are relatively higher than that of the residential unit almost by 2.5 degree celsius.

The passive ventilation system holds the potential to curtail overall energy consumption, consequently reducing greenhouse gas emissions by mitigating the necessity for energy production to absorb carbon from the environment.

Fig. 11.

Energy consumption breakdown in the office building. Source: PEOPLE PRACTICES SYSTEMS,” September 2013.

Fig. 12. Inter-relation of commercial buildings for the passive ventilation system and UTCI calculations. Source: Author.

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Operational systems within buildings currently command a substantial share of energy consumption, concurrently emitting a spectrum of pollutants that exert adverse effects on the environment. This environmental impact is intricately tied to the building’s functional efficiency. While integral to building functionality, traditional Heating, Ventilation, and Air Conditioning (HVAC) techniques engender a pronounced ecological strain due to their high energy demands11. In the present era of climate change, the imperative for a building system capable of establishing a symbiotic relationship with the urban environment, concurrently enhancing thermal comfort, and mitigating energy use and air pollution is of paramount significance.

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CONVENTIONAL BUILDING SYSTEMS

strong wind-driven forced convection high-frequency components of turbulent spectrum negligible natural convection Collecting Chamber

Egress Complex

+

local pO2 local pCO2 local humidity local air movement metabolic energy

FLOW OF GASSES

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Egress tunnels and surface conduits

Central Chimney Shaft

mound surface

-

+

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excavate

wind energy

+ forced convection zone

+

+ +

mixing zone natural convection zone

+

colony pO2 -

colony metabolism recruitment

Reticulum (Lateral Connective Tunnels) Lateral Connective Tunnels

mixed forced convection/natural convection medium frequency components of turbulent spectrum

Surface Conduits forced convection zone CO2 H2O heat O2

Galleries

mixing zone

natural convection zone

Cellars Radial Tunnels

Nest, chimney and subterranean tunnels natural convection dominates low frequency components of turbulent spectrum negligible wind-driven forced convection

A

B

BIOMIMETIC SYSTEM

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The termite mounds function as a thermosiphon model for ventilation and consequent thermoregulation, relying on a convection loop created through temperature differentials.

Various scientists’ theories on termite mounds were scrutinised to formulate biomimetic principles considering multiple parameters.

MARTIN LURCHER’S THEORY ON TERMITE MOUNDS OF MACROTERMES NATALENSIS (1961)

The cyclic thermal looping process involves utilising termite metabolism for thermal ventilation. Metabolic heat generated by termites in the nest produces rising heat that ascends through the chimney. The absence of an opening at the mound’s summit results in buoyant forces that propel air through the surface conduits, exhausting carbon dioxide while drawing in oxygen, thus increasing air density.15 Gravitational forces then guide the denser air back to the nest, perpetuating the cycle. Micropores on the egress façade of surface conduits allow for the transfer of oxygen and carbon dioxide while preventing forceful air escape, ensuring the continuous replacement of stale air with fresh air. (Figure 14)

BODY PARTS & THEIR FUNCTIONS

Fig. 13.

Body parts of termite mound. Source : Turner, J. Scott. “Beyond biomimicry: What termites can tell us about realising the living building”

Fig. 14. (A) Luscher’s model of convection loop of termite mounds. (B) Scott Turner’s model of convection loop in Termite mounds. Source : Beyond biomimicry.

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The primary objective of termite mounds is to establish a thermoregulated internal environment conducive to the well-being of termites. Within these mounds, termites inhabit the ground-level nest region, encompassing galleries where termites coexist with fungi 14 (Figure 13), which also serve as a nutritional source for the termites. A central chimney shaft above the nest connects to surface conduits in the summit collection chamber. These conduits extend beneath the envelope and interconnect below ground level through radial tunnels, ultimately completing the loop back to the nest.

Lateral connective tunnels within the mound centre link the exterior environment to the chimney shaft. The semi-porous external surface exhibits a complex geometry, facilitating enhanced airflow and ventilation by inducing turbulence without wind flow.

MECHANISM AND PERFORMANCE OF TERMITE MOUNDS

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To emulate passive ventilation principles observed in nature, termite mounds stand out as exemplars of animal architecture, employing passive mechanisms to efficiently ventilate and shield their interiors from the adverse external conditions prevalent in hot and humid environments 13.

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morning

B

midday

afternoon

cold

day

A

B

night

hot

J. SCOTT TURNER’S THEORY ON TERMITE MOUNDS OF MACROTERMES NATALENSIS (2002)

Contrary to J. Scott Turner’s theory, Mahadevan’s experiment suggested that termite metabolic heat was not a significant heat source in the convection loop of termite mounds.

Observations during the experiment indicated varying flow rates, with positive flow during the day and a somewhat more substantial flow at night. The nest consistently maintained the coolest temperature along the mound’s central axis throughout the day and night, as illustrated in Figure 15, which depicts the motion of the convection loop in conical mounds.17

(II-C) LAKSHMINARAYANAN MAHADEVAN’S THEORY ON TERMITE MOUNDS (2015)

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Turner’s hypothesis, akin to Lurcher’s theory, considered wind flow movements outside the mound. Wind turbulence induces gas exchange through the porous mound walls and alters wind velocity within the mound. 16

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Fig. 15. Lakshminarayana Mahadevan model of convection loop in various types of mounds. (A) Conical, (B) Cathedral. Source : Termite Mounds Harness Diurnal Temperature Oscillations for Ventilation

EGRESS COMPLEX INVESTIGATION BY DAVID ANDREEN AND RUPERT SOAR

Fig. 16. Egress complex. Source : Solar-Powered Ventilation of African Termite Mounds.” The Journal of Experimental Biology 220

The egress complex, characterised by a dense lattice-like network of tunnels between 3mm and 5mm wide, connects wider conduits inside and outside.18 This complex adapts to different temperatures by enabling excess moisture evaporation while ensuring adequate ventilation (Figure 16). Dr. David Andréen, a senior lecturer at Lund University, highlighted the significance of the egress complex in promoting novel flows of air, heat, and moisture in human architecture. The tunnels in this complex interact with wind oscillations to enhance mass transfer for ventilation.19

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ENVIRONMENTAL IMPACT ON TERMITE MOUND MORPHOGENESIS

Fig. 17.

Types of mounds (left-cathedral mound, right-conical mound) outside Otjiwarongo, Namibia. Source: Andrea Surovek.

Fig. 18. Temperature distribution in the best configurations obtained in the multi-objective optimisation for average environmental conditions of Namibia, India, and Brazil. Source: Korb, Judith. 2010. “Termite Mound Architecture, from Function to Construction.”

Morphological differences between cathedral and conical mounds are outlined in Figure 17, revealing the presence of undulations on the cathedral mound’s exterior to accommodate surface conduits. The conical mound in cooler regions embeds surface conduits within a smoother exterior, reflecting reduced reliance on solar-powered and windinduced cooling. Figure 18 illustrates that mounds exposed to stronger solar irradiances exhibit taller and slimmer structures, mirroring natural mound sizes at different latitudes.21

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The architecture of termite mounds, constructed by termites, The architecture of termite mounds, constructed by termites, plays a pivotal role in achieving passive cooling objectives.

Mounds up to 6-8m tall exhibit a conical form with a broader base and a narrower top, ensuring structural stability and responsiveness to higher wind speeds.20 Construction factors consider external temperature, wind speed direction, and humidity. The study focuses on cathedral and conical mounds in Milan due to their existence in similar environmental conditions. (Figure 17)

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CONCLUSION

Solar radiation and wind turbulence emerge as central topics unifying the mound’s ventilation dynamics. Solar radiation, acting as the principal conductor, influences temperature differences within the mound. The mound’s exterior, especially the bulge, is illuminated by solar radiation, causing temperature changes that trigger ventilation. Concurrently, wind turbulence within the depression utilizes the energy produced by temperature differences, directing this power through the mound’s internal tubes. This initiates a cycle of air exchange, effectively serving as the termite nest’s internal thermostat. These intricate interactions successfully control the temperature, fostering a harmonious atmosphere conducive to the survival and well-being of the termite colony. (Figure 20) These abstracted principles were further analysed for architectural transformation.

BIO-MIMETIC PRINCIPLE ABSTRACTIONS

Fig. 19.

Convection loop of termite mound during various time of the day. Source: Author (Right Top) Fig. 20. Principle extraction of termite mound. Source: Author (Right Bottom)

RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

Frequently overlooked in its architectural significance, the termite mound serves as a cultivated natural passive ventilation system. This intricate mechanism, guided by underlying principles, positions the termite mound as an exemplary source of sustainable architectural inspiration. Central to this system is an elaborate network of tubular and channel structures, embodying conventional principles to establish a continuous loop (Figure 19) that facilitates heat exchange, thereby intricately regulating the internal temperature.

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This research endeavors to delve deeper into the nuanced design of termite mounds, seeking to extract valuable insights applicable to architectural contexts. The study meticulously examines how these principles may inform the development of façades, material exploration, environmental design systems, and system’s morphology. By drawing inspiration from nature, a sustainable and energy-efficient system that responsively interact with their surroundings, thereby minimizing ecological footprints.

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DOMAIN

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Others

13% Grassland

11%

Wood-based material from food waste

47%

Wooden foam panels

Corn

29% Artifical areas

corn cobs

Rice

Agricultural areas Forest and semi-natural areas

agriculture waste

corn husks

Agriculture area in Milan

blender yeast/strach

rice straw

Production & processing waste

processing 3.1%

rice husks

distribution 13.5%

corn cobs

corn husks

Bio-based forming material

catering 3.8%

mix

Properties 1 Fire resistance 2 thermal insulation 3 absorb humidity

yeast

rice straw

production 36.6%

rice husks

consumption 43%

starch forming

Food waste in Milan

FOOD WASTER IN MILAN

WOOD FOAM

The primary constituents of the agricultural waste encompass essential elements for crop growth and harvesting, such as straw, rice bran, husks, cobs, and maise leaves. These remnants are poignant reminders of frequently overlooked resources that could be repurposed and innovatively utilised.

Recognising the multifaceted challenges posed by agricultural waste in Milan, implementing initiatives focused on reusing agricultural byproducts emerges as a crucial strategy. Such initiatives curtail waste and foster sustainability and resilience in the face of these challenges.

Fig. 21.

Food Waste in Milan and use for bio-materials. Source: Author.

Fig. 22.

Wood foam. Source: Author.

In the intricate tapestry of the natural world, foam-like structures manifest in diverse and captivating forms, possessing an inherent ability to balance high stability with low weight—attributes pivotal in applications where toughness and durability are paramount. Amid this intricate dance of nature’s brilliance, wood foam emerges as a captivating example of sustainable innovation. This material, derived from agricultural waste, signifies a harmonious synthesis of natural endowments and human ingenuity, presenting an appealing solution to various challenges. Wood foam materialises within the region’s humid atmosphere, symbolising adaptability and environmental friendliness.24 Remarkable properties, including innate fire resistance, moisture absorption, and heat insulation, make wood foam a versatile option that contributes significantly to carbon emission reduction and addresses food waste. This intersection of environmental stewardship and material innovation signals a transformative era in conscientious construction (Figure 22). With a density ranging between 250 and 300 kg/m3, the lightweight nature of wood foam positions it at the forefront of materials designed for efficacy and sustainability in a world where every gram bears significance.and 300 kg/m3.25 This lightweight property places wood foam at the forefront of materials designed for effectiveness and sustainability.

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In Milan, an astonishing 5.1 million metric tonnes of food are annually discarded without consumption or recovery, constituting a distressing 15.4% of total annual consumption and escalating to an alarming 91.4% of surplus food. This translates into a staggering financial loss of 12.6 billion euros annually, equating to an impressive 210 euros per individual.

22 Beyond the financial implications, this massive food wastage has profound environmental repercussions, as the discarded food contributes to a carbon footprint of approximately 13 million metric tonnes of CO2 emissions as illustrated in Figure 21. 23

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BIO-COMPOSITE MATERIAL SYSTEM

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sunlight

Electricity O2

Biomass organic waste as “fuel”

Biofilm (algae) ANODE

CATHODE pump

MFC

CO2

CONCLUSION

BIOFILM

Fig. 23.

Biofilm. Source: Author.

The landscape of materials employed across diverse industries has undergone significant transformation in response to the global paradigm shift towards sustainability and environmental stewardship. Agro-based materials, derived from agricultural resources, have emerged as substitutes for conventional materials in various applications owing to their superior environmental friendliness. The imperative propels this transition to mitigate our environmental impact, preserve natural resources, and ameliorate the deleterious consequences of climate change. This study scrutinises the superior environmental performance of agro-based products compared to traditional materials, delineating their appeal as pivotal components for a sustainable future. The attributes rendering agro-based materials environmentally advantageous include renewability, a diminished carbon footprint, biodegradability, reduced chemical usage, and enhanced energy efficiency. Incorporating agro-based products holds promise in alleviating environmental challenges and mitigating the cumulative impact of human activities on the ecosystem. Adopting such materials constitutes a proactive stride towards a greener and more ecologically conscious future, aligning with the ongoing global prioritisation of sustainability. Within this research, a diverse array of agro-based materials has undergone evaluation for the development of our façade porosity panel, demonstrating commendable thermal conductivity and strength conducive to external applications.

DOMAIN

DOMAIN

A biofilm, a three-dimensional microbial structure, is a battleground for microbial life. Formed when bacteria detect and adhere to a surface, subsequent colonisation and the production of an extracellular polysaccharide matrix (EPS) solidify its structure. In damp environments, algae and mosses readily form biofilms, absorbing carbon dioxide, releasing oxygen, and absorbing moisture when growing on surfaces.

This feature can be leveraged to create materials applicable to building surfaces that foster biofilm development. Notably, biofilms can be recycled in a new material cycle at the end of their life cycle, showcasing the potential for significant carbon emission reduction.26 Additionally, biofilms play a pivotal role in biofuel cells, generating trace amounts of energy when algae biofilms envelop electrodes in microbial fuel cells.27 (Figure 23)

Biofilm (algae)

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bio-composite material

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CASE STUDIES

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CASE STUDIES

CASE STUDIES

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Urban Sequoia synthesises diverse strands of sustainable design thinking, incorporating the latest innovations and emerging technologies at the building scale. Urban Sequoia achieves significantly greater carbon reductions than traditional approaches that address these aspects separately through a holistic optimisation of building design, reduction of materials, integration of biomaterials, advanced biomass,

and carbon capture technologies. The central emphasis of this proposal lies in carbon sequestration and mitigating air pollution. However, it is noteworthy that the proposal has yet to comprehensively address thermal heating and cooling considerations, which are pivotal elements impacting a building’s energy consumption efficiency. Given their substantial contributions to operational costs and carbon emissions, optimising thermal comfort in a building holds transformative potential.

Fig. 24.

Render view of Urban Sequoia. Source: SOM Architects.

Fig. 25.

Energy network between building and city. Source: SOM Architects.

In contrast to the prevalent model of net-zero buildings, the Urban Sequoia proposal envisions a departure towards structures actively collecting carbon from the atmosphere, thereby enhancing their capacity to reduce carbon emissions over time. Grounded in innovative carbon-sequestering technologies such as algae applications and carbon capture, utilisation, and storage (CCUS) technologies, the proposal envisions a built environment that not only absorbs carbon but also contributes to biofuel production and the supply of bioprotein to various sectors. Incorporating biomass and algae systems into the building’s façade epitomises this transformative approach. The potential applications of these carbon sequestration technologies hold significant promise, particularly in addressing the escalating concerns surrounding air pollution in Milan’s metropolitan surroundings.

CASE STUDIES

CASE STUDIES

SOM (Skidmore, Owings, and Merrill) unveiled the “Urban Sequoia” proposal at COP26, the 2021 UN Climate Change conference in Glasgow, UK. This visionary concept aims to revolutionise the built environment, specifically within urban contexts, by enabling buildings to absorb carbon at an unprecedente d rate. The core tenet of Urban Sequoia posits that urban structures can play a pivotal role in carbon absorption, thereby presenting a paradigm shift in how buildings and cities are conceptualised and constructed.

Given the prevailing global climate change challenges, this proposal offers a pragmatic solution with far-reaching implications, potentially fostering a circular economy centred around carbon absorption. Kent Jackson, a partner at SOM, underscores the adaptability and applicability of the proposal, asserting its potential positive impact on diverse cities across the globe and buildings of varying scales.

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URBAN SEQUOIA, SOM

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Informed decisions

Module of systems

CASE STUDIES

All the factual data which is reflected in in this case-study has been learnt from the official SOM Architects website of which conclusion was drawn out for further investigation Source: https://www.som.com/research/urban-sequoia/ Fig. 26.

Design, structural and technical details for Urban Sequoia. Source: SOM Architects.

Modularity and prefabrication

Holistic integration of components

CASE STUDIES

This project reveals a discernible gap in addressing thermal comfort, a crucial aspect of building design. The pursuit of optimal thermal comfort, as expounded in the preceding chapter, significantly escalates energy consumption by the heating and cooling system. While the project acknowledges the principles of cross-ventilation and stack effect, it posits that tower frameworks, characterized by cumulative heat generation from human metabolism and environmental factors, may only derive partial benefits from these strategies. The demographic composition of a building can undergo substantial transformation if a system can effectively enhance thermal comfort, given that the heating and cooling system represents a primary consumer of energy. In response to this challenge, the research recognizes the imperative for a dual-purpose system—one that not only consumes less energy compared to conventional systems but also elevates thermal comfort. The integration of these objectives, coupled with the incorporation of recommended carbon-sequestering technology, culminates in a comprehensive strategy that aligns ideally with the broader urban milieu. By concurrently addressing air pollution within the same system, this approach establishes a holistic framework for an urban context, thereby contributing to the desired domain.

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CONCLUSION

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CH2 manifests both literal and metaphorical expressions of environmental intentions within its architectural composition.

Nature serves as an inspirational touchstone for façades that effectively moderate the climate. Tapered ventilation ducts seamlessly integrate with daylighting strategies, and an evocative undulating concrete floor structure assumes a central role in the building’s heating and cooling dynamics.

Notably, CH2 achieved the distinction of being the first new commercial office building in Australia to not only meet but surpass the six-star rating system administered by the Green

Building Council of Australia.

Integral to its environmental features is CH2’s capability to provide 100% fresh air to all occupants, ensuring one complete air change every half hour. Beyond the environmental merits, the building is positioned to yield substantial benefits, including superior indoor air quality and conservative energy cost estimates that anticipate the building’s innovative features paying for themselves within the relatively short timeframe of five to ten years.

CH2 employs a strategic cooling approach through the judicious management of the temperature differential between nocturnal and diurnal air. A significant facet of this strategy involves the deliberate exposure of an entire façade, facilitating the intake of air through automated shutters crafted from recycled wood. The passive treatment of air exchange and convection loop is adept at maintaining a comfortable ambient temperature within the spaces for a considerable portion of the day. Furthermore, cooled fresh air ascends through strategically positioned floor registers, perpetuating the cooling effect throughout the day. (Figure 29) Fig. 27.

External passive ventilation system. Source: Archdaily.

Fig. 28.

System application at upper floor level of the building. Source: Archdaily.

CASE STUDIES

CASE STUDIES

The Council House 2 (CH2) office building was collaboratively designed with the City of Melbourne to function as a holistic system, wherein occupants actively participate. The design paradigm embodies a model fostering enhanced interaction between the urban landscape and nature, emphasizing mutual interdependence. Aligned with the City of Melbourne’s objective to achieve zero emissions for the municipality by 2020, a pivotal aspect of this strategy involves a 50% reduction in energy consumption in commercial buildings. CH2, serving as a pilot initiative, seeks to provide a tangible exemplar for the local development market. The project brief necessitated a building predominantly reliant on passive energy systems while concurrently delivering a structure of premium quality.

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CH-2 MELBOURNE CITY COUNCIL HOUSE-2, DESIGNINC.

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“Productivity studies show that the two full air changes per day have led to a 10.9 per cent increase in staff health and productivity.

That’s a saving of around $2 million a year. When people ask about the cost of this technology, I say, the real question should be: how can you afford not to design this way?”

Rob Adams Director of City Design and Projects, City of Melbourne

CASE STUDIES

All the factual data which is reflected in in this case-study has been learnt from the official DesignINC and archdaily website of which conclusion was drawn out for further investigation. Source: https://www.designinc.com.au/project/council-house-2-ch2 Fig. 29.

Passive ventilation system working during various time of the day. Source: Archdaily (Right Top) Fig. 30. Interior of office space of CH2 buildng. Source: Archdaily (Right Bottom)

CASE STUDIES

In conclusion, the Council House 2 (CH2) office building stands as a groundbreaking testament to sustainable design and environmental stewardship. Collaboratively envisioned with the City of Melbourne, CH2 transcends traditional architectural paradigms, functioning as a holistic system that actively engages its occupants in a symbiotic relationship with the urban and natural environment. CH2 had exemplified a forward-thinking approach, achieving a remarkable 50% reduction in energy consumption within commercial buildings. The architectural embodiment of CH2 reflects a harmonious fusion of literal and metaphorical expressions of environmental intent. The innovative “night purge” cooling system, along with the strategic use of water gradients, showcases CH2’s commitment to passive energy systems. By providing 100% fresh air circulation and achieving one complete air change every half hour, CH2 not only prioritizes environmental sustainability but also ensures superior indoor air quality. As showed in Figure 29,30 that how the external system passively regulates the temperature of the interior space of the building during the different times of the day.

This system eventhough pasively ventilates the internal space of the building, it still uses the mechanical force to generate the passive ventilation with the wind turbines on top of the building.

Also there lack of organisation in the system to capture wind from the atmosphere.

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CONCLUSION

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In conclusion, the phenomenon of globalization has unequivocally propelled the expansion of urban centers worldwide, driven by heightened resource accessibility and economic opportunities. However, this urbanization has incurred a substantial environmental toll, principally attributable to escalating levels of greenhouse gas emissions stemming from the energy consumption of urban edifices. The imperative to ameliorate this critical issue by mitigating energy consumption in urban locales, thereby advancing toward a more sustainable trajectory, is imperative.

Our research focalizes on Milan, an exemplary case of urban proliferation fraught with environmental challenges such as thermal inversion and excessive humidity. Milan, cognizant of these issues, has committed to the EU Green Building Pact, aiming to retrofit extant structures by 2050 to augment energy efficiency. This exigency underscores the urgency for innovative approaches to retrofit existing structures and conceive novel ones that minimize environmental impact and energy consumption.

DOMAIN CONCLUSION

Our investigation delves into distinctive design principles conducive to passive ventilation systems, drawing inspiration from natural paradigms, particularly the intricate architectural configurations of termite mounds. The termite mound’s innate ventilation mechanism offers insights into morphology, building ventilation dynamics, and intricate geometric configurations that optimize airflow. Notably, this system is characterized by adept utilization of geometric principles and convection-based airflow.

In summation, our research endeavors to address pressing issues of urban sustainability by synthesizing insights from the natural realm, exemplified by the convection loop derived from termite mounds, and leveraging cutting-edge bio-materials for façade systems capable of sequestering environmental carbon while infusing fresh, filtered air into the convection loop. The bifurcated nature of our investigation involves the development of a passive ventilation system for an office tower, applicable to prospective developments in Milan, Italy, and a retrofittable passive ventilation system positioned between existing structures to ameliorate thermal comfort within internal spaces and outdoor dead spaces alike.

RESEARCH QUESTIONS

RESEARCH QUESTIONS

Finally, we contemplate the integration of biological systems, exemplified by algae through bio-film, into architectural blueprints to abate air pollution and contribute to environmental well-being. Due to its unique attributes, algae emerges as a promising candidate for bio-film façade systems capable of generating power and sequestering carbon.

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Furthermore, our study explores the prospect of sustainable deployment of agro-based materials as substitutes for conventional construction materials. These materials confer advantages such as renewability, a diminished carbon footprint, biodegradability, and enhanced energy efficiency, aligning with global initiatives to combat climate change and preserve the environment.

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1.

Ritchie, Hannah, and Max Roser. “Emissions by Sector.” Our World in Data, 2020. https://ourworldindata.org/emissions-by-sector.

19.

Claggett, Nicholas, Andrea Surovek, William Capehart, and Khosro Shahbazi. 2018. “Termite Mounds: Bioinspired Examination of the Role of Material and Environment in Multifunctional Structural Forms.” Journal of Structural Engineering 144 (7): 02518001. https://doi.org/10.1061/(asce)st.1943-541x.0002043.

2.

Iea. “Greenhouse Gas Emissions from Energy Data Explorer – Data Tools.” IEA. https://www.iea.org/data-and-statistics/ data-tools/greenhouse-gas-emissions-from-energy-data-explorer.

3.

“Buildings – Analysis.” IEA, accessed April 28, 2023, https://www.iea.org/reports/buildings.

4.

Ritchie, Hannah, and Max Roser. “Emissions by Sector.” Our World in Data, 2020. https://ourworldindata.org/emissions-by-sector.

5.

Wu, Ling. (2021). “Modelling the Temporal and Spatial Relationship among Air Quality, Urban Morphology, and Urban Ventilation.”

6.

“Europe’s Urban Air Quality -Re-Assessing Implementation Challenges in Cities,” March 20, 2019.

7.

“Particulate Matter (PM 2.5 and PM 10,” September 22, 2021.

8.

KPMG. “European Green Deal Policy Guide - KPMG Global,” September 30, 2022. https://kpmg.com/xx/en/home/insights/2021/11/european-green-deal-policy-guide.html.

9.

KPMG. “European Green Deal Policy Guide - KPMG Global,” September 30, 2022. https://kpmg.com/xx/en/home/insights/2021/11/european-green-deal-policy-guide.html.

10.

PEOPLE PRACTICES SYSTEMS,” September 2013.

11.

Ferrando, Davide Tommaso. “Back in Business: Superlab in Milan, Italy by Balance Architettura.” Architectural Review, February 16, 2023. https://www.architectural-review.com/buildings/back-in-business-superlab-in-milan-italy-by-balance-architettura.

RESEARCH QUESTIONS

Turner, J. Scott. “Beyond biomimicry: What termites can tell us about realising the living building.” (2008).

14.

Turner, J. Scott. “Beyond biomimicry: What termites can tell us about realizing the living building.” (2008).

15.

Turner, J. Scott. “Beyond biomimicry: What termites can tell us about realizing the living building.” (2008).

21.

“Food Waste.” n.d. Food Safety. https://food.ec.europa.eu/safety/food-waste_en.

22.

Climate Watch, the World Resources Institute (2020).

23.

Sauer, Christiane. It is made of--: New materials sourcebook for architecture and design. Berlin: Gestalten, 2010.

24.

Sauer, Christiane. It is made of--: New materials sourcebook for architecture and design. Berlin: Gestalten, 2010.

25.

Dade-Robertson, Martyn, Alona Keren-Paz, Meng Zhang, and Ilana Kolodkin-Gal. 2017. “Architects of Nature: Growing Buildings with Bacterial Biofilms.” Microbial Biotechnology 10 (5): 1157–63. https://doi.org/10.1111/1751-7915.12833.

26.

Nagendranatha Reddy, C., Hai T. H. Nguyen, Md T. Noori, and Booki Min. 2019. “Potential Applications of Algae in the Cathode of Microbial Fuel Cells for Enhanced Electricity Generation with Simultaneous Nutrient Removal and Algae Biorefinery: Current Status and Future Perspectives.” Bioresource Technology 292 (122010): 122010. https://doi.org/10.1016/j.biortech.2019.122010.

27.

“Urban Sequoia.” n.d. SOM. https://www.som.com/research/urban-sequoia/.

28.

“Urban Sequoia: Building a Carbon Negative Future | Kent Jackson | TEDxGoodenoughCollege.” n.d. Www.youtube. com. Accessed January 3, 2024. https://www.youtube.com/watch?v=DLmikzs-DuU. 29.

“At COP27, SOM Presents Urban Sequoia, a Building Proposal That Absorbs Carbon from the Atmosphere.” 2022.

ArchDaily. November 9, 2022. https://www.archdaily.com/991930/at-cop27-som-presents-urban-sequoia-a-building-proposal-that-absorbs-carbon-from-the-atmosphere.

30.

“Council House 2 (CH2).” n.d. Hansen Yuncken. https://www.hansenyuncken.com.au/projects/commercial/52-councilhouse-2-ch2.

31.

“CH2 Melbourne City Council House 2 | DesignInc - Arch2O.com.” n.d. https://www.arch2o.com/ch2-melbourne-city-council-house-2-designinc/#google_vignette.

32.

ArchDaily. 2013. “CH2 Melbourne City Council House 2 / DesignInc.” ArchDaily. June 30, 2013. https://www.archdaily. com/395131/ch2-melbourne-city-council-house-2-designinc.

16.

King, Hunter, Samuel Ocko, and L. Mahadevan. 2015. “Termite Mounds Harness Diurnal Temperature Oscillations for Ventilation.” Proceedings of the National Academy of Sciences 112 (37): 11589–93. https://doi.org/10.1073/pnas.1423242112.

33.

“Council House 2 (CH2) - Projects - DesignInc.” n.d. Www.designinc.com.au. https://www.designinc.com.au/project/ council-house-2-ch2.

17.

Ocko, Samuel A., Hunter King, David Andreen, Paul Bardunias, J. Scott Turner, Rupert Soar, and L. Mahadevan. 2017.

“Solar-Powered Ventilation of African Termite Mounds.” The Journal of Experimental Biology 220 (18): 3260–69. https://doi. org/10.1242/jeb.160895.

34.

https://climate-pact.europa.eu/about/priority-topics/green-buildings_en

RESEARCH QUESTIONS

13.

20.

Korb, Judith. 2010. “Termite Mound Architecture, from Function to Construction.” Biology of Termites: A Modern Synthesis, 349–73. https://doi.org/10.1007/978-90-481-3977-4_13.

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12.

Claggett, Nicholas, Andrea Surovek, William Capehart, and Khosro Shahbazi. 2018. “Termite Mounds: Bioinspired Examination of the Role of Material and Environment in Multifunctional Structural Forms.” Journal of Structural Engineering 144 (7): 02518001. https://doi.org/10.1061/(asce)st.1943-541x.0002043.

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FOOTNOTES

18.

Andréen, David, and Rupert Soar. 2023. “Termite-Inspired Metamaterials for Flow-Active Building Envelopes” 10 (May). https://doi.org/10.3389/fmats.2023.1126974.

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I. How can termite mound morphology design principles be abstracted for passive ventilation system design?

The architectural configuration of termite mounds strategically adapts to environmental conditions, featuring ridges (bulge) to harness solar radiation and valleys (depression) to channel wind flow. These principles drive the convection loop within termite mounds, facilitating ventilation. How can these principles inform the design of passive systems to optimize solar radiation absorption and induce turbulence for enhanced wind capture?

II. What impact would the incorporation of convection-based ventilation principles derived from termite mound structures have on the performance of a passive ventilation system, particularly concerning improved thermal comfort?

Termite mounds utilize a convection loop that leverages solar radiation for temperature regulation, challenging conventional building design approaches. How might the performance of a space be altered if solar gain is embraced rather than mitigated?

III. How can the egress complex geometry which is bionic structure be incorporated of a termite mound structure into the system organisation (porosity panels/exhaust vents) to aid in improving ventilation within the space?

RESEARCH QUESTIONS

Termite mound egress complexes enhance wind flow through the creation of micro-turbulence for nest ventilation.

Does this geometric arrangement facilitate ventilation in the context of architectural construction applications, contributing to improved flow within buildings?

IV. How can this passive ventilation system will help in achieveing air exchange rate desired for a each individual space?

To minimise the amount of carbon emissions and energy consumed while producing building construction materials, these conventional materials can be replaced with agro-based materials, which can help to utilise the food waste generated within the city of Milan as well as minimise the carbon footprint of the building. This material can also be the source of absorbing humidity from the environment because of its porous nature.

VI. Can the integration of living systems such as algae into the architectural framework contribute to the absorption of environmental air pollutants?

Given algae’s properties in carbon sequestration and electricity generation in humid conditions, can the application of algae as a bio-film serve as a living system for façade construction, effectively aiding in the absorption of air pollutants?

RESEARCH QUESTIONS

RESEARCH QUESTIONS

V. Can agro-based building materials effectively confer thermal insulation properties and sequester carbon, surpassing the capabilities of conventional materials?

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Considering each space’s distinct functionality and volume, achieving desired air exchange rates is critical. Can this biomimetic passive ventilation system fulfill the specific air exchange requirements of individual spaces, in contrast to conventional systems?

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METHODOLOGY

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METHODOLOGY

The inherent efficacy of the generative evolutionary process is evident in its manifestation within the natural world. The dynamism of environmental design is perpetually in flux, with each succeeding generation of every species stemming from novel mutation strategies implemented by their immediate progenitors.

This research paper delves into the sophisticated realm of computational design through an in-depth examination of the Kangaroo Physics simulation framework integrated into Rhino and Grasshopper. Kangaroo Physics, a robust physics engine, has become integral to the field of architectural design, particularly in simulating the dynamic behavior of flexible structures. The study aims to elucidate the multifaceted capabilities of Kangaroo Physics, shedding light on its applicability, computational intricacies, and transformative impact on parametric architectural modeling. Kangaroo allows for responsive and parametric design, where the form and structure of a model can adapt dynamically based on changing parameters or external forces.

This research delves into the utilization of an evolutionary process centered on generative design principles for architectural endeavors, employing the Wallacei X plugin with Grasshopper on Rhinoceros 3D. The evolutionary design methodologies espoused herein are poised to reconcile divergent objectives by meticulously accounting for localized environmental variables, thereby augmenting the overall performance of architectural structures. Throughout the course of this study, the evolutionary algorithm implemented has served as a fundamental tool, iteratively optimizing designs that align seamlessly with the predefined parameters within the environmental and structural domains. This method was used at various stages of investigation while developing the passive ventilation system. The process was employed while form finding of morphology of passive system and various types of porosity panels.

Kangaroo physics simulatation was employed for form finding the double arch curvature, as the desired objective was to capture maximum solar radiation while capturing maximum wind.

Fig. 31.

Evolutionary algorithm for facade pannels.

Fig. 32.

CFD for air-flow false ceiling.

METHODOLOGY SECTION TITLE

SECTION TITLE METHODOLOGY

KANGAROO PHYSICS SIMULATION

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EVOLUTIONARY ALGORITHM

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COMPUTATIONAL FLUID DYNAMICS (CFD)

Finite Element Analysis (FEA), referred to as FEA, is a potent numerical method used in engineering and computational mechanics. Through the modeling and simulation of intricate behaviors inherent in structures and systems, engineers and researchers can glean insights that are frequently elusive or cost-prohibitive when relying solely on conventional experimental methodologies. At the core of FEA lies discretization, a fundamental technique that subdivides a sizable structure into numerous smaller, more manageable components. Each of these components is distinctly characterized by its shape, material properties, and imposed boundary conditions.

The study of fluid flow and heat transfer processes using numerical methods and computer simulations is known as computational fluid dynamics (CFD), a complex and vital area of study in fluid mechanics. This intricate and pivotal domain within fluid mechanics employs specialized algorithms and numerical techniques to solve the governing mathematical equations that delineate the dynamics of fluid flow and heat transfer.

Within the framework of this project FEA assumes a pivotal role, given the imperative that the passive ventilation system must be self-supporting, situated between extant architectural scenarios. FEA proves instrumental in delineating the primary exoskeletal framework of the morphology, taking into meticulous account the structural integrity of the central core and other constituent structural elements. The incorporation of FEA into the design methodology serves the purpose of extending the efficacy of the structural system, thereby contributing to the reduction of the carbon footprint associated with the envisioned architectural intervention.

The transformative impact of CFD on decision-making processes within projects pertaining to fluid and heat transfer phenomena is unequivocal. The optimization of fluid flow, encompassing mediums such as air, and the elucidation of heat transfer mechanisms are imperative prerequisites for ensuring the efficiency of a project. CFD serves as a tool to scrutinize diverse design scenarios through the utilization of computational models, facilitating the assessment of their efficacy and the formulation of defensible conclusions based on the resultant data.

Fig. 33.

Shortest path for tubular network system.

Fig. 34.

FEA for facade panel.

A comprehensive comprehension of fluid dynamics assumes paramount significance in this context, particularly with regard to the intricate patterns of air movement within these geometric configurations for this specific research project is crucial.

METHODOLOGY SECTION TITLE

SECTION TITLE METHODOLOGY

FINITE ELEMENT ANALYSIS (FEA)

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B

C

A

A - Porosity component material

of agro based

B - Extruder for 3D print.

C - Robotic Arm.

SHORTEST PATH.

ROBOTIC ARM.

The widely employed “shortest path” algorithm is pivotal for determining optimal connections between locations by systematically minimizing metrics like distance, time, and cost.

Applicable across academic domains such as technology and logistics, this foundational concept is particularly relevant to our research initiative, focusing on creating an efficient tubular network system for airflow.

A mechanical apparatus denoted as a robotic arm has been engineered to replicate the movements and functionalities inherent in a human arm. Typically comprised of interconnected segments or links articulated by joints, this robotic arm exhibits the capacity for precise movements along multiple axes (6-axes). Positioned at the terminus of the robotic arm, one commonly encounters a gripper, specialized tool, or attachment tailored for specific purposes.

This algorithm, adept at identifying effective pathways for air or fluid movement within enclosed spaces, enhances ventilation and thermal comfort by establishing direct, uncomplicated routes between zones. The optimization of airflow is crucial for energy-efficient design, demanding the reduction of energy loss and the establishment of optimal routes for effective heat transfer. In this context, the shortest path algorithm is utilized to design a tubular network system strategically guiding airflow while concurrently minimizing turbulence and energy dissipation. The broader implications of this research extend to diverse academic fields, reflecting the algorithm’s versatility in addressing complex challenges beyond the realm of airflow optimization.

Fig. 35.

Robotic arm application for facade panel fabrication.

Fig. 36.

CNC for facade panels fabrication.

In the context of the current research project, the utilization of a robotic arm assumes paramount importance in the fabrication of double-curved façade porosity panels employing agriculturally derived materials. The underlying concept of these panels revolves around harnessing wind dynamics by inducing controlled turbulence through their distinctive geometric configuration, thereby facilitating a passive ventilation system within the architectural structure.

The efficacy of employing a robotic arm in this context is underscored by the intricate geometrical design of the panels and the integration of modularization. This application enables expeditious panel production while concurrently minimizing material waste. For this research, these porosity panels can be directly 3D printed from the agro-based material which would be supplied directly to the extruder.

METHODOLOGY SECTION TITLE

SECTION TITLE METHODOLOGY

AXONOMETRIC

61

60

PLAN

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MI-LUNG

B C

A

C

B A

A - Porosity component material

of agro based

A - Porosity component B - Extruder

B - CNC 6mm. diameter bit.

C - 3D printer for producing moulds for panel formation.

FUSED FILAMENT FABRICATION - 3D PRINT.

Computer Numerical Control (CNC) stands as a consequential technological advancement in the realm of manufacturing and industrial processes, relying on the utilization of computers to oversee and automate intricate machine operations.

This technology finds extensive application across diverse industries.

3D printing, also referred to as manufacturing is a technology that converts digital designs into physical objects. It works by building up layers of materials, like plastics, metals or ceramics to create three structures. The process starts with a model made using computer aided design (CAD) software.

Different 3D printing techniques, such as Fused Deposition Modeling (FDM) and Stereolithography (SLA) offer a range of material choices and printing resolutions. The applications of 3D printing span industries from prototyping and developing products, to creating customized medical implants and aerospace components.

In the current milieu, characterized by the production of porous façade panels crafted from agro-based materials necessitating processes such as baking, CNC technology emerges as a compelling alternative to robotics. In this context, CNC foam models play a pivotal role in the meticulous creation of silicon molds, serving as foundational elements for the efficient production of façade modules. The CNCdriven methodology, adopted in this instance, streamlines the production process, ensuring the precise and seamless fabrication of intricate components. Simultaneously, this approach optimizes resource utilization and enhances overall process effectiveness. The discerning application of CNC technology in this intricate manufacturing context underscores its efficacy as a sophisticated and versatile tool within the broader landscape of industrial and manufacturing technologies. This process can be used to produce porosity compnent from the block made out of wooden foam material.

Along this research investigation plastic extrusion FFF 3D prinitng system was used for fabrication process to understand the outcome of the digitial design by translating it to physical prototype using this system at micro-level. This also helps to create the mould for fabricating the porosity component by pouring method which will be further explained in detail.

Fig. 37.

Robotic arm application for facade panel fabrication.

Fig. 38.

CNC for facade panels fabrication.

METHODOLOGY SECTION TITLE

SECTION TITLE METHODOLOGY

COMPUTER NUMERICAL CONTROL (CNC)

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62

C - CNC machine

64

65

RESEARCH DEVELOPMENT

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RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

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MI-LUNG

OVERVIEW

This chapter provides insights into how biomimetic principles facilitated the development of a morphology, pivotal for the evolution of the passive ventilation system during the Master of Architecture (M.Arch) phase. Moreover, the material advancements achieved in the MSC phase seamlessly transitioned into the M.Arch phase, contributing to the fabrication process of the designated component.

The research culminates in an urban-scale analysis, aiming to comprehend the Universal Thermal Climate Index (UTCI) values across diverse zones within the city of Milan. Given the city’s notably hot and humid climate, this analysis seeks to identify the most challenging scenarios for testing the application of the developed systems.

RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

Throughout both research phases, the underlying biomimetic principles derived from termite mounds remained constant. These principles underwent rigorous testing across various domains, with a primary focus on Computational Fluid Dynamics (CFD) tests to evaluate their architectural transformations.

67

66

The comprehensive investigation unfolded in two distinct phases, commencing with the formulation of a passive ventilation system tailored for a new office tower building in the (MSC) phase. Subsequently, recognizing the challenges and constraints inherent in the initial system, a refined passive ventilation system was devised, strategically designed for retrofitting onto an existing office building within an urban fabric. This innovative system not only elevates indoor thermal comfort but also augments outdoor urban thermal comfort, fostering heightened cultural interactions within the urban fabric.

Fig. 39.

MSC overview.

windflow direction

windflow direction

windflow direction

windflow direction

MI-LUNG

MI-LUNG

maximum wind capture

average wind capture

0

55°C

45°C

8 (m/s)

INITIAL MORPHOLOGY CFD ANALYSIS (ABSTRACTED PRINCIPLES)

INITIAL TUBULAR NETWORK CFD ANALYSIS

The complex interaction between solar radiation and wind turbulence in termite mounds helps to regulate the symphony of conventional loops by maintaining a delicate temperature equilibrium essential to the termites’ survival.

This section is dedicated to scrutinizing the hypothesis of the diurnal convection loop observed in termite mounds and assessing its feasibility when applied to a building scale.

To investigate the hypothesis associated with termite mounds, particularly the diurnal convection loop from surface conduits to the chimney, a Computational Fluid Dynamics (CFD) simulation was executed. The model was configured with exterior tubes and a central chimney shaft, establishing interconnections between them. The model’s height was extended to 100 meters, intending to evaluate the hypothesis on a building scale. The surfaces of the tubes facing the South, representing an afternoon solar scenario, were assigned temperatures of 65°C, 55°C, and 45°C, while the central shaft, considered a shaded interior, maintained a temperature of 25°C (figure 41).

The CFD analyse on the basic morphology primitive which was abstracted from the principles of termite mound was teested on using CFD to understand the morphological behaviour and its architectural scalability. The CFD also aids us to understand the significance of morphological adaptation of buldge and depressions and its impact on wind flow. The depression on the morphology will help to capture the wind flow which is important for convection loop.

This part of the research helped in the MSC phase helped us to derive the formation of the morphology which performed best for wind catchment. The one’s showed in figure 40 are only a few from a series of experiments carried out in MSC phase same can be referred is the previous book to understand in detail. But the outcomes from these experiment setup helped to understand the relation between the depression and buldge with the wind flow. The outcome from this analysis was used to develop the morphology at M.Arch phase.

Fig. 40.

Morphology CFD analysis with buldge and depression

Fig. 41.

Adapted tubular system CFD analysis

After running the series of simulation with various parameters of the model the one with narrower tubes diamters (0.6m diamter) and group of tubes instead of one tube at each side to improve surface area to volume ratio in terms of solar gain.

This entire experiment setup was explianed in detail in MSC phase so the same can be referred in Mi-lung research book of MSC phase to understand the entire eexperiment in detail.

RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

12 (m/s)

65°C

69

68

0

average wind capture

MI-LUNG

MI-LUNG

DESIGN DEVELOPMENT

Agro-based one-forth of porosity panel - type A

DESIGN DEVELOPMENT

Fig. 42.

In Milan, an astounding volume of food, quantified in metric tonnes, is annually discarded without consumption or recovery, as detailed in the preceding domain chapter (refer to page 34). The primary constituents of the agricultural waste which is also extensively found in Milan encompass essential elements for crop growth and harvesting, such as straw, rice bran, husks, cobs, and maise leaves. The pervasive issue of food waste and agricultural waste demands comprehensive consideration. The utilization of innovative recycled materials holds substantial potential to significantly advance objectives related to the reduction of carbon emissions and mitigation of pollutants associated with construction materials, deviating from conventional building facade materials such as steel and concrete. However, a thorough evaluation of the practical application of these materials is imperative, particularly concerning aspects such as insulation, wind resistance, tensile strength, fire resistance, and related factors. In this context, the paramount importance of material experimentation becomes evident. This section delves into the exploration of leveraging agricultural food waste for the synthesis of building materials, offering a promising avenue for waste reduction and the implementation of environmentally sustainable building techniques.

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MATERIAL EXPERIMENT

MI-LUNG

MI-LUNG

corn husk

rice straw

Wood foam

rice husk

moss

Bio-film Fig. 44.

Fig. 43. Matrial application of porosity panel. (Left) Raw material for porosity panel fabrication (Right)

algae

RESEARCH DEVELOPMENT

Subsequent to this, a biofilm is applied onto the substrate of wood foam to engage in carbon sequestration through photosynthetic processes, thereby aligning with the sustainability objectives of the project. Given the humid climatic conditions prevalent in Milan, the exposed surface is susceptible to sustaining lichen, moss and algae growth (Figure 43). Both wood foam and biofilm materials manifest commendable physical properties, concomitantly contributing to the amelioration of air quality.

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73

RESEARCH DEVELOPMENT

lichen

LIVING SYSTEM IN HUMID ENVIROMENT

MATERIAL APPLICATION The primary constituents of the facade system investigated in this research initiative are wood foam and biofilm. The foundational element, wooden foam,in Milan is derived from agricultural waste materials such as rice husk, straw, corn cobs, and analogous byproducts (Figure 46). As expounded upon in the preceding chapter (refer to pages 35-36), wood foam exhibits distinctive attributes, particularly excelling in the domains of fire protection, thermal insulation, and moisture absorption. The porosity and lightweight nature of wood foam result from a fermentation process, thereby augmenting its appropriateness for the envisaged application.

AGRICULTURE WASTE IN MILAN

corn corb

MI-LUNG

STAGE II - APPLICATION

COMPUTATION AND MATERIAL PROPERTIES

FABRICATION

MI-LUNG

STAGE I - SAMPLES

THERMAL INSULATION TEST LOAD TEST

Subsequently, the identified mixing ratio of the most efficacious sample was employed in the fabrication of a wooden foam porosity panel module, an elaboration of which is presented in the ensuing chapter. The physical tests encompassed thermal insulation assessments and load evaluations, serving as pivotal benchmarks in the empirical examination of the materials.

The eventual phase of the research entails the manufacturing of building-scale components, involving the replication of modular units for scalability and practical application.

This process is integral to the translational potential of the materials and findings derived from the earlier phases of experimentation.

Fig. 45.

Stages for material experiment.

RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

The material experimentation proceeded through a bifurcated process, comprising two distinct stages. Initially, the development of samples involved the utilization of agro-based raw materials and other binders in varying proportions, with the objective of discerning the most optimal ratio for subsequent exploration. The select material samples demonstrating superior performance underwent comprehensive physical testing to ascertain the optimal configuration, with a focus on foam characteristics to capture humidity and density considerations for structural stability.

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74

MATERIAL EXPERIMENTS

MI-LUNG

Screen high density powder

Rice husk

Wood chips

MI-LUNG

BLEND

Straw

MIX & MELLOW Mellow 1 hour in 35 °celsius

Rice husk powder

Sawdust

Straw powder

BAKE Bake 1 hour in 200 °celsius

INGREDIENT AND PROCESS Corn flour

RESEARCH DEVELOPMENT

yeast

oil

Fig. 47.

Fig. 46. Ingridients for material test.(Left) Process for mixture formation for facade component development (Right)

DRY

This experimental undertaking corresponds to the discourse within the research paper pertaining to the genesis and fermentation of bio-material. The elemental production procedure encompassed four distinct stages: blending (involving the filtration of high-density powder), mixing and mellowing (conducted for one hour at 35⁰C), baking (carried out for one hour at 200⁰C), and solar drying.

RESEARCH DEVELOPMENT

sugar

77

76

Wheat flour

Preceding the experimental phase, the procedural commencement involved the comminution of three primary raw materials—namely rice husk, straw, and wood chips— as depicted in Figure 46. These materials functioned as the control substances for the ensuing experimentation.

Subsequently, rice husk powder, straw powder, and sawdust were derived through a grinding process. In tandem, wheat flour, maize flour, yeast, and sugar were enlisted as raw materials for the foaming aspect, while the wood-derived materials served as the reinforcing constituents. Essential components for foaming included sugar, yeast, and wheat (comprising corn flour). The ensuing step involved the subdivision of the various ratios into smaller aliquots, facilitating the creation of material samples.

MI-LUNG

MI-LUNG

Selected

A : rice husk powder B : sawdust C : straw powder D : wheat flour E : corn flour F : sugar G : yeast

79

78

H : oil

RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

Fig. 48.

Breakdown of percentages of ingridients of sample tests.

MI-LUNG

BRITTLE

Selected

MI-LUNG

FOAM

HARD

The values attributed to these 12 samples were charted on an axes diagram, employing a classification system based on two parameters: Foam-No Foam and Hard-Brittle.

Subsequent to this classification, A_02 (comprising 35.09% rice husk powder, 52.63% wheat flour, 5.26% sugar, 3.51% yeast, and 5.26% oil), B_02 (comprising 35.09% sawdust, 52.63% wheat flour, 5.26% sugar, and 5.26% oil), and C_01 (comprising 17.54% straw powder, 70.18% wheat flour, 5.26% sugar, 3.51% yeast, and 5.26% oil) emerged as the three ratios exhibiting superior performance, as discerned through rigorous analysis of the charts.

UNFOAM Fig. 49.

Sumamry diagram of performance of all samples.

RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

Sample selection was guided by the imperative considerations of foam density for humidity resilience, structural strength, and thermal insulation, recognized as robust criteria for this investigation. Following a meticulous assessment of diverse combinations and ratios involving raw components and binders, three samples characterized by elevated foaminess and hardness were discerned from a cohort of 12 sets representing varied proportions.

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80

SAMPLE PERFORMANCE

MI-LUNG

MI-LUNG

A_02

SAMPLE

Rice husk: 35.09% Flour: 52.63%

LOAD B_02 Sawdust: 35.09% Flour: 52.63%

LOAD TEST C_01 Straw: 17.54% Flour: 70.18%

ICEBAG

PHYSICAL TESTS SETTING

82

SAMPLE

THERMAL INSULATION TEST Fig. 50.

Fig. 51.

Diagram of physical tests. (Left) Diagram of physical tests. Material samples. (Right)

In the load test experiment, a strip of samples was subjected to a suspended weight applied centrally, with point support on the extremities, until the samples reached fracture. The quantification of the maximum force applied to each sample was accomplished through the measurement of the weight of the load. Concurrently, the thermal test involved the placement of an ice bag, set at a temperature of -5 degrees Celsius, between the test specimen and a heating pad. Subsequent to the application of this thermal stimulus, surface temperature readings of the ice bag were systematically recorded at regular intervals, constituting the thermal insulation test.

RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

HEATING

83

Subsequently, the three meticulously chosen material samples underwent further scrutiny through material testing, encompassing assessments of weight and resistance to thermal conduction. For the purpose of these evaluations, novel samples were synthesized, measuring 8cm by 16cm— half the dimensions conventionally employed by material scientists in calculating Young’s modulus (15cm by 30cm).

The selected sample formulations (A_02, B_02, C_01) were employed in triplicate for these comprehensive analyses.

MI-LUNG

MI-LUNG

A_02 Max weight: 32.5 Ibs

B_02 Max weight: 25.0 lbs

C_01

LOAD TEST Silica sand served as the experimental loading agent in the conducted load test. The experimental outcomes reveal that the application of this loading mechanism engendered an augmented force exerted on the crossbar situated on the surface of the material samples. Notably, the material sample denoted as A_02 exhibited the highest stress, manifesting a maximum peak force of 32.5 pounds. In comparison, samples B_02 and C_01 exhibited lower stress levels, registering peak forces of 25.0 pounds and 16.2 pounds, respectively

Fig. 52.

Pictures of load tests (Left/Right)

RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

85

84

Max weight : 16.2 Ibs

MI-LUNG

MI-LUNG

TEMP (°C) 10

B_02

5

MAX TEMP: 9.4 °C 0 TIME (min)

-5 0

5

10

15

20

TEMP (°C) 10

5

A_02 Maximum temp: 2.2 °C

0 TIME (min)

-5 0

5

10

15

20

TEMP (°C)

86

5

Maximum temp: 9.0 °C

0 TIME (min)

-5 0

5

10

15

20

Fig. 53.

Fig. 54.

Pictures of thermal insulation tests (Right) Graphics of each thermal insulation test. (Left)

While all three samples demonstrated commendable thermal insulation, A_02 emerged as the preeminent performer, exhibiting the most favorable thermal characteristics among the tested specimens.

RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

C_01

To elucidate the thermal insulating characteristics of the samples, a series of measurements were conducted, recording the center temperature of the ice pack’s surface at regular intervals spanning a duration of 20 minutes.

Subsequently, three distinct temperature line graph tables were derived, illustrating the thermal performance of the samples. Specifically, the final temperature readings for samples A_01, B_02, and C_01 were recorded as 2.2, 9.4, and 9.0, respectively.

87

THERMAL INSULATION TEST

10

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MI-LUNG

A_02

B_02

C_01

LOAD (lbs)

A_02 emerged as the formulation that excelled in both categories of investigations, as evidenced by a holistic analysis of the combined experimental charts. Consequently, A_02 was selected as the formulation for the ensuing application.

Notably, the primary component of A_02 is rice husk, aligning with a deliberate objective. Given the substantial annual production of rice husk waste from the predominant crop in Milan, utilizing this agro-waste minimizes carbon emissions associated with its incineration. The component and binding ratio of this material sample would be further used for fabrication of porosity component module.

TEMP (°C)

Fig. 55.

Physical test selection.

RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

The comprehensive material experimentation commenced with the development of 12 samples, incorporating agro-based raw components and various binders in distinct proportions.

The objective was to discern the most optimal ratio for subsequent exploration. Three selected material samples, demonstrating superior performance, underwent exhaustive physical testing to ascertain the optimal configuration. The evaluation focused on foam characteristics, addressing humidity retention and density considerations for structural stability.

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CONCLUSION OF PHYSICAL EXPERIMENTS

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Sensor 03: Viale Marche

ensor 01: Pascal Cittá Studi Sensor 01:

Sensor 03: Viale Marche

03: Sensor 03:Sensor Viale Marche

Sensor 03: Viale Marche

Sensor 04: Via Verziere Sensor 04: Sensor 04: Via Verziere

Sensor 04: Via Verziere

Viale Marche

Pascal Citta Studi

Sensor 01: Pascal Cittá Studi

Sensor 04: Via Verziere

Sensor ensor 02: Via Senato02: Via Senato

Via Verziere

00

300 300

SITE ANALYSIS: MILAN,ITALY

90

0

300 300

specifically chosen for monitoring vital PM2.5 emissions in the primary Lombardian city-center. Subsequent to an initial filtration process, it is observed that only these four sensors scattered across Milan city persistently record PM2.5 emissions. (figure 56) The designated sensors include: 1 : Citta Studi.

2 : Via Senato.

3 : Viale Marche.

The adoption of standards by the World Health Organization (WHO) assumes paramount importance. These standards are instrumental in ensuring that air quality levels remain reasonable and acceptable. According to the latest WHO recommendations, the average exposure to PM2.5—fine particulate matter with a diameter of 2.5 microns or less— over a 24-hour period should not exceed 15 g/m3. Hence, the identification of the critical zone exhibiting the highest PM2.5 concentrations becomes imperative for testing the design module.

4 : Via Verziere.

To gain a comprehensive understanding of the emissions context, a land-use study was conducted using the Grasshopper program (ELK), which facilitates the accessibility of data from OpenStreetMap. The outcomes of the analyses contribute to contextualizing the sources and trends of emissions.

the selection of the site, analysis of data obtained from ensor 04: ViaFor Verziere

four out of the 12 sensors strategically positioned across Milan during a specific time frame (June 22–June 23 of the preceding year) is indispensable. These four sensors were

00

Fig. 56.

Identified four sites with sensors to identify the PM 2.5 value of each.

Source: Author.

Sensor 03: Viale Marche

Ultimately, it was determined that sensor 04, located in Via Verziere, occupies Milan’s business center. The distinctive urban landscape of this area is characterized by the presence of universities, government buildings, and cultural landmarks.

Consequently, the primary users of this location are visitors and students, significantly influencing the dynamics of the neighborhood.

RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

Sensor 02: Via Senato

300

91

As per a 2020 research conducted by the European Environment Agency (EEA), Milan, a prominent city in northern ensor 03: Viale Italy,Marche grapples with a severe air quality crisis, accentuating the city’s struggle against adverse atmospheric conditions. This urgent issue not only disrupts environmental equilibrium but also poses a substantial threat to human health.

0

Fig. 57.

Comparison of PM 2.5 values of each site. Source: Author

In summation, the rigorous site selection process, incorporating both land-use assessment and meticulous analysis of sensor data, converges upon the conclusion that sensor 04 situated at Via Verziere manifests elevated levels of PM2.5 emissions. The heightened emission levels are intrinsic to the urban composition of the city center, characterized by administrative centers, educational institutions, commercial hubs, and prominent tourist attractions. This synthesis of land-use considerations and empirical sensor data substantiates the rationale behind the selection of Via Verziere as an exemplar site for scrutinizing PM2.5 emissions in a densely urbanized context.

RESEARCH DEVELOPMENT

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93

92

MI-LUNG

MI-LUNG

The selection of Via Verziere, specifically sensor 04, as the designated site for PM2.5 emissions testing is underpinned by multiple considerations. The dearth of expansive green spaces within the city center significantly curtails natural air purification processes, leading to an augmented concentration of pollutants. Consequently, the emissions observed at this location exhibit a notable magnitude, registering at levels nine times higher than the World Health Organization’s (WHO) recommended annual average PM2.5 guideline of 5 g/ m3. This disparity underscores the heightened atmospheric pollution in this urban milieu.

0

300

MI-LUNG

MI-LUNG

CONCLUSION

Sensor 04: Via Verziere

Sensor 03: Viale Marche

94 RESEARCH DEVELOPMENT

0

300 300

Fig. 58.

Site selection. Site highlited with black frame.

RESEARCH DEVELOPMENT

0

The city center, notably Via Verziere, predominantly comprises commercial offices, retail establishments, cultural and tourist landmarks, and public administration entities, presenting a stark dearth of green spaces. This particular urban characteristic has contributed to heightened levels of vehicular movement, which, in turn, has substantially elevated PM2.5 concentrations. For the purposes of the current research experiment, a site characterized by a land-use designation for a garage building was deliberately selected.

This decision was guided by both legal regulations pertaining to public areas and the site’s proximity to one of the tallest structures within the Milan city center. The chosen site offers a distinctive vantage point for investigating PM2.5 emissions within the context of urban dynamics, where regulatory considerations and architectural prominence intersect.

95

Following a comprehensive analysis of particle matter (PM2.5) data obtained from all four city centers situated across Milan, a meticulous screening process identified Via Verziere, located in the city center, as the focal point for further investigation. This particular urban locus exhibited the highest PM2.5 values, a phenomenon attributed to the conspicuous absence of green spaces, particularly in areas densely populated with public and tourist amenities, consequently intensifying vehicular activity.

MI-LUNG

MI-LUNG

URBAN SPACE around office building.

URBAN SPACE around office building.

9 a.m.

9 p.m.

MAXIMUM USABILITY during office hours.

DEAD URBAN SPACE outside office hours.

PEAK DAYTIME conditions with high HUMIDITY & maximum SOLAR RADIATION.

introduction of PASSIVE SYSTEM to enhance cultural enviornment

introduction of PASSIVE VENTILATION SYSTEM

interaction of system with the BUILDING & SURROUNDING

passive system enhancing the OUTDOOR THERMAL COMFORT

rendering the space FUNCTIONAL throughout the day

USABILITY OF SPACE OUTSIDE OFFICE BUILDING The urban areas adjacent to office buildings experience optimal utilization during office hours between 9.00am to 6.00pm, characterized by peak daytime conditions marked by elevated humidity levels and intense solar radiation.

Conversely, these spaces remain inactive during periods outside of regular office hours between 9.00pm to 6.00am (figure 59). The incorporation of the passive ventilation system, developed through the course of this research, is poised to not only ameliorate the internal thermal conditions of neighboring buildings but also enhance the overall outdoor thermal comfort. Consequently, this system stands to cultivate a more dynamic cultural environment, ensuring the functionality of these spaces throughout the entirety of the day.

RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

6 a.m.

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96

6 p.m.

Fig. 59.

Outdoor space usability of office building throughout the day

MI-LUNG

Sensor 01: Pascal Cittá Studi Sensor 02: Via Senato SITE : 2 Sensor 03: Viale Marche

Sensor 04: Via Verziere

Sensor 04: Via Verziere

PM10

C0

Sensor 01: Pascal Cittá Studi Sensor 02: Via Senato SITE : 3 C0Sensor 03: Viale Marche Sensor 0% 04: Via 10% Verziere 20%

0

Sensor 02: Via Senato Sensor 03: Viale Marche Sensor 04: Via Verziere

PM10

30%

40%

50%

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70% 80% Industrial Agriculture

20%

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50%

60%

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100%

8

Residential, c

Green spaces Waste

Road transpo

Agriculture

Commercial Manufacturing and extractive industry Residential, commercial and institutional

Non-road tra

Waste

Residential Energy supply Road transport

Other

Manufacturing and extractive industry

Office Non-road transport

Energy supply

Other buildings Other

300

Sensor 02: Via Senato Sensor 03: Viale Marche Sensor 04: Via Verziere

0%

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Sensor 01: Pascal Cittá Studi Sensor 02: Via Senato SITE : 1 Sensor 03: Viale Marche

BC

0

300

0

300

0

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Sensor 02: Via Senato Sensor 03: Viale Marche Sensor 04: Via Verziere

Dry bulb temperature (°C)

0 UTCI comfort - Summer - August - Period 6.00 to 18.00.

Hottest Day & Hour : 06 August 16.00.

Dry Bulb Temperature 33.6 °C.

Sensor 03: Viale Marche Sensor 04: Via Verziere

UTCI comfort - Summer - August - Period 6.00 to 18.00.

Hottest Day & Hour : 06 August 16.00.

Dry Bulb Temperature 33.6 °C.

Sensor 03: Viale Marche Sensor 04: Via Verziere

98

UTCI comfort - Summer - July - Period 6.00 to 18.00.

Hottest Day & Hour : 11 July 16.00.

Dry Bulb Temperature 32.6 °C.

UTCI comfort - Summer - June - Period 6.00 to 18.00.

Coldest Day & Hour : 21 June 17.00.

Dry Bulb Temperature 32.1 °C.

0

300

UTCI comfort - Summer - July - Period 6.00 to 18.00.

Hottest Day & Hour : 11 July 16.00.

Dry Bulb Temperature 32.6 °C.

Sensor 04: Via Verziere

Sensor 04: Via Verziere

300

28.59

0

300

UTCI comfort - Summer - June - Period 6.00 to 18.00.

Coldest Day & Hour : 21 June 17.00.

Dry Bulb Temperature 32.1 °C.

After the selection of a singular city center from the four under consideration, the one exhibiting the highest PM2.5 values was subjected to a detailed micro-scale analysis.

This involved the identification of three distinct zones across the city, considering land use as a pivotal criterion, 0 with a deliberate focus on areas characterized by maximal commercial and industrial activities.

The analysis extended to the assessment of the Universal Thermal Climate Index (UTCI) in these three selected zones.

UTCI serves as a comprehensive metric designed to evaluate the thermal comfort or discomfort experienced by individuals in outdoor environments. Its nuanced approach considers various meteorological elements, such as air temperature, humidity, wind speed, and radiation, transcending conventional measures reliant solely on air temperature.

The application of UTCI proves invaluable in discerning the intricate interplay of weather conditions on human thermal sensation and well-being. This index finds widespread application in fields such as climatology, urban planning, and0 public health, offering insights into potential thermal stress Fig. 60.

Land-use and outdoor thermal comfort map for summer months.

300

RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

300

34.97

UTCI VALUE FOR EXTREME SUMMER MONTHS.

Sensor 03: Viale Marche Sensor 04: Via Verziere

0 UTCI comfort - Summer - June - Period 6.00 to 18.00.

Coldest Day & Hour : 21 June 17.00.

Dry Bulb Temperature 32.1 °C.

300

99

Sensor 04: Via Verziere

0

UTCI comfort - Summer - August - Period 6.00 to 18.00.

Hottest Day & Hour : 06 August 16.00.

Dry Bulb Temperature 33.6 °C.

0 UTCI comfort - Summer - July - Period 6.00 to 18.00.

Hottest Day & Hour : 11 July 16.00.

Dry Bulb Temperature 32.6 °C.

300

300

MI-LUNG

Sensor 01: Pascal Cittá Studi Sensor 02: Via Senato SITE : 2 Sensor 03: Viale Marche

Sensor 01: Pascal Cittá Studi Sensor 02: Via Senato SITE : 3 Sensor 03: Viale Marche

Sensor 04: Via Verziere

Sensor 04: Via Verziere

Sensor 04: Via Verziere

0

Sensor 02: Via Senato Sensor 03: Viale Marche Sensor 04: Via Verziere

Sensor 02: Via Senato Sensor 03: Viale Marche Sensor 04: Via Verziere

Dry bulb temperature (°C)

-3.26

-9.43

Sensor 03: Viale Marche Sensor 04: Via Verziere

0

300

0

300

0

300

Sensor 02: Via Senato Sensor 03: Viale Marche Sensor 04: Via Verziere

0 UTCI comfort - Winter - December - Period 6.00 to 18.00.

Coldest Day & Hour : 20 December 07.00.

Dry Bulb Temperature -5.2 °C.

300

MI-LUNG

Sensor 01: Pascal Cittá Studi Sensor 02: Via Senato SITE : 1 Sensor 03: Viale Marche

300

UTCI comfort - Winter - December - Period 6.00 to 18.00.

Coldest Day & Hour : 20 December 07.00.

Dry Bulb Temperature -5.2 °C.

Sensor 03: Viale Marche Sensor 04: Via Verziere

UTCI comfort - Winter - December - Period 6.00 to 18.00.

Coldest Day & Hour : 20 December 07.00.

Dry Bulb Temperature -5.2 °C.

Sensor 03: Viale Marche Sensor 04: Via Verziere

UTCI VALUE FOR EXTREME WINTER MONTHS.

Fig. 61.

Land-use and outdoor thermal comfort map for winter months.

RESEARCH DEVELOPMENT

0 UTCI comfort - Winter - February - Period 6.00 to 18.00.

Coldest Day & Hour : 08 February 07.00.

Dry Bulb Temperature -4.6 °C.

Sensor 04: Via Verziere

UTCI comfort - Winter - February - Period 6.00 to 18.00.

Coldest Day & Hour : 08 February 07.00.

Dry Bulb Temperature -4.6 °C.

Sensor 04: Via Verziere

UTCI comfort - Winter - February - Period 6.00 to 18.00.

Coldest Day & Hour : 08 February 07.00.

Dry Bulb Temperature -4.6 °C.

Sensor 04: Via Verziere

0 UTCI comfort - Winter - January - Period 6.00 to 18.00.

Coldest Day & Hour : 19 January 08.00.

Dry Bulb Temperature -8.7 °C.

300

300

UTCI comfort - Winter - January - Period 6.00 to 18.00.

Coldest Day & Hour : 19 January 08.00.

Dry Bulb Temperature -8.7 °C.

0 UTCI comfort - Winter - January - Period 6.00 to 18.00.

Coldest Day & Hour : 19 January 08.00.

Dry Bulb Temperature -8.7 °C.

RESEARCH DEVELOPMENT

The primary objective of this research endeavor is to ameliorate the thermal comfort of urban spaces. To achieve this, UTCI values were computed using Honeybee plugin on Rhinoceros 3D, Grasshopper for extreme summer and winter months, specifically June, July, August, December, January, and February, as depicted in Figures 59 and 60, respectively. The analysis facilitated the extraction of outdoor dry bulb temperatures for each site during various months, revealing minimal disparities among the three sites. To refine the investigation further and identify the site with the most challenging thermal conditions, a micro-analysis was undertaken. This involved the selection of a commercial complex or a grouping of adjacent commercial buildings, as their collective impact was anticipated to exert a more pronounced influence on thermal dynamics.

101

100

on individuals across diverse climates and seasons.

300

MI-LUNG

SITE : 2

SITE : 3

Sensor 02: Senato 03: Via Viale Marche

Sensor 02: Senato 03: Via Viale Marche

Sensor 04: Via Verziere

Sensor 04: Via Verziere

Sensor 04: Via Verziere

MI-LUNG

SITE : 1

Sensor 02: Senato 03: Via Viale Marche

0

300

0

300

0

300

300

0

300

0

300

Sensor 03: Marche 04: Viale Via Verziere

Sensor 03: Marche 04: Viale Via Verziere

Sensor 03: Marche 04: Viale Via Verziere

Dry bulb temperature (°C)

35.51

0

UTCI comfort - Summer - June to Auguest Period 6.00 to 18.00.

Average Dry Bulb Temperature 32.8 °C.

UTCI comfort - Summer - June to Auguest Period 6.00 to 18.00.

Average Dry Bulb Temperature 28.3 °C.

UTCI comfort - Summer - June to Auguest Period 6.00 to 18.00.

Average Dry Bulb Temperature 32.6 °C.

33.43

ENVIORNMENTAL ANALYSIS (MICRO-SCALE)

Sensor 04: Via Verziere

Sensor 04: Via Verziere

0

UTCI comfort Winter-January to February Period 6.00 to 18.00.

Average Dry Bulb Temperature WW.

UTCI comfort Winter-January to February Period 6.00 to 18.00.

Average Dry Bulb Temperature -6.3 °C.

UTCI comfort Winter-January to February Period 6.00 to 18.00.

Average Dry Bulb Temperature -5.6 °C.

-10.14

300

0

300

0

Radiation (KW/H) Maximum incident radiation : 1927.31 kWh/msq.

Maximum incident radiation : 1651 kWh/msq.

Maximum incident radiation : 1046.55 kWh/msq.

920

Fig. 62.

Micro site analysis (Landuse, outdoor thermal comfort and solar radiation)

To explore worst-case scenarios, an analysis of solar radiation and wind patterns was conducted. The findings indicated significantly higher solar radiation on Site 1 compared to the other two sites. With the elevated UTCI at Site 1, it can be inferred that wind movement within this zone would be comparatively subdued. This inference aligns with the CFD-based wind analysis, revealing diminished wind movement near Site 1. These insights contribute to a nuanced understanding of micro-environmental conditions, facilitating a more precise evaluation of thermal and climatic dynamics in the targeted urban spaces.

RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

2200

After identifying minimal differences among the three sites at a macro scale, a detailed micro-scale environmental analysis focused on selected commercial complexes. This investigation included a comprehensive assessment of the Universal Thermal Comfort Index (UTCI), sunlight duration, and wind dynamics, utilizing Computational Fluid Dynamics (CFD). Figure 62 illustrates that the average UTCI, value 300 during summer months was slightly higher for the selected commercial complex compared to the other two sites.

Conversely, during winter months, Site 1 exhibited a more favorable UTCI, considering Milan’s prevailing concerns of heat and humidity over extensive cold climates.

103

102

Sensor 04: Via Verziere

Dry bulb temperature (°C)

-4.88

Vi a Ve ia :V 04

:V

ia

Ve

windflow direction

rz windflow direction ie re iteration : 20

ns 04

04

:V

ia

Ve

Se

rz windflow direction ie re iteration : 20

or

Wind-speed - 8.4 (m/s) ; Direction - Southeast Temperature 28.6 °C

Wind-speed - 8.4 (m/s) ; Direction - Southeast Temperature 28.6 °C

Wind-speed - 8.4 (m/s) ; Direction - Southeast Temperature 28.6 °C

iteration : 10

windflow direction

Se ns

iteration : 10

windflow direction

or

Wind-speed - 8.4 (m/s) ; Direction - Southeast Temperature 28.6 °C

or

Se ns

windflow direction

rz windflow direction ie re iteration : 30

Se n

rz windflow direction ch ie re iteration : 30e

Ve ia

:V 04

or

ns Se

rz ie Ve

windflow direction

rz windflow direction ie re Ve

so r0

4:

Vi

a

Ve M ar

le ia :V 03 or ns Se

re iteration : 20

Se n

M ar e a Vi

4:

so r0

ie rz

Ve ia

:V 04

:V

ia

Wind-speed - 8.4 (m/s) ; Direction - Southeast Temperature 28.6 °C

Wind-speed - 8.4 (m/s) ; Direction - Southeast Temperature 28.6 °C

Wind-speed - 8.4 (m/s) ; Direction - Southeast Temperature 28.6 °C

Wind-speed - 8.4 (m/s) ; Direction - Southeast Temperature 28.6 °C

or Wind analysis (macro/micro) of various site scenarios

rz windflow direction ch ie e re

so r0

4: al Vi 3:

so r0 Se n

Se n

re iteration : 30e

ch M ar le ia :V

03 04 or ns Se Se ns

Fig. 63.

4:

Vi a

o

so r0

al

4:

so r0

or ns Se

Wind-speed - 8.4 (m/s) ; Direction - Southeast Temperature 28.6 °C

iteration : 10

Figure 63, delineating wind velocity distributions, aligns with this deduction, illustrating lower wind velocities at Site 1. It is noteworthy that the Computational Fluid Dynamics (CFD) analysis conducted on Autodesk CFD employed SouthEast as the designated wind direction, and the average temperature considered for simulation was 28.6 °C. Despite the simplification of considering only contextual factors for wind diversion in this CFD analysis, the results are in concurrence with the anticipated wind patterns, contributing valuable insights into the micro-scale wind dynamics influencing thermal comfort in the examined urban spaces.

Wind-speed - 8.4 (m/s) ; Direction - Southeast Temperature 28.6 °C

Wind-speed - 8.4 (m/s) ; Direction - Southeast Temperature 28.6 °C

RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

The mean wind speed in Milan registers at 8.4 m/s, a marginally low figure. Given Milan’s geographical setting, encircled by mountainous terrain, wind patterns in the region exhibit variability from multiple directions. To discern wind flow dynamics at distinct sites, particular emphasis was placed on the prevailing wind direction, notably from the South-East, in the investigative process. Considering the relatively higher Universal Thermal Comfort Index (UTCI) observed at Site 1 in comparison to the other two sites, it is deduced that the wind movement at Site 1 is diminished, considering wind speed as a contributing factor in UTCI calculations.

SITE : 3

105

104

CFD ANALYSIS OF VARIOUS SITES MICRO-SCALE

Se n

Vi a

Vi

3:

so r0

Se n

e ch

Wind-speed - 8.4 (m/s) ; Direction - Southeast Temperature 28.6 °C

M ar le ia :V 03 or ns Se

7.5 (m/s)

Se ns

4: S Vi Vi en e a a at M Ve o ar rz c ie he re

o

2:

Se n

so r0 Se n

Se n

so r0

3:

Se n

Vi

al

e

M ar

ch

e

so r0

to Se na Vi a

2: so r0 Se n

Se ns

o Se ns

to Se na Se n

so r0

2:

Vi a

MI-LUNG 0

SITE : 2

MI-LUNG

velocity magnitude

SITE : 1

Se n

4: or 0 er :V ia e V h

rz windflow direction ie re Ve a 4:

4 0 r o S ns en Se

windflow direction

a

i V :

windflow direction

le

windflow direction

windflow direction

o

4 0 r

ia V :

r e V

e r zie o

: 4 r0

a Vi

V

er

e r zie

windflow direction

S

o

4 0 r

so r0

Ve windflow direction

M

c ar windflow direction

windflow direction

04

or

ns Se so n e

2 0 r ia V :

Wind analysis of various months on a site

Following the prioritization of Site 1 based on a comprehensive understanding of environmental variables such as Universal Thermal Comfort Index (UTCI), solar radiation, and wind patterns, a pivotal aspect of the analysis involved an indepth examination of the wind flow on the site across various months throughout the day. To accomplish this, wind directions and speeds for different months in Milan were extracted utilizing the Ladybug plugin within the Rhinoceros 3D and Grasshopper framework. Subsequently, this data was employed to conduct a micro-level Computational Fluid Dynamics (CFD) analysis specifically tailored for Site 1, utilizing Autodesk CFD. In configuring the parameters for the Autodesk CFD simulations, the windspeed, wind direction, and temperature inputs were tailored in accordance with the unique characteristics of each analyzed month. The CFD outputs revealed a predominant wind flow emanating from the East or Southeast direction, with a minimum windspeed of 5.2 m/s and a maximum windspeed of 8.8 m/s. These findings contribute crucial insights into the intricate wind dynamics at the micro-scale, augmenting the comprehensive understanding of environmental conditions at Site 1 and informing potential design considerations for enhancing thermal comfort and overall livability.

RESEARCH DEVELOPMENT

Fig. 64.

9.7 (m/s)

107

ia V :

Vi

i rz

ia V : 3 Sensor 04: Sensor 04: Via Verziere 0 Via Verziere r o s n e S

so r0

Se n

ziewindflow direction re

re ie rz

106

e

ch

Ve

n e S

ar M

ia

:V

04

r Ve

e r e

rz windflow direction ie re 4:

Vi

a

Ve

windflow direction

Se ns

ch 0 MI-LUNGe

M ar

to

e

or

Month - September ; Temperature 25 °C Wind-speed - 6.4 (m/s) ; Direction - Northeast Calm for 78.99% of the time = 545 hours.

Month - December ; Temperature 7 °C Wind-speed - 5.8 (m/s) ; Direction - Southwest Calm for 60% of the time = 414 hours.

velocity magnitude

CFD ANALYSIS OF SITE FOR VARIOUS MONTHS

ns

Month - August ; Temperature 19 °C Wind-speed - 7 (m/s) ; Direction - South Calm for 62.03% of the time = 428 hours.

Month - November ; Temperature 12 °C Wind-speed - 6 (m/s) ; Direction - Southwest Calm for 68.7% of the time = 474 hours.

MI-LUNG

na Se

al

Month - June ; Temperature 29 °C Wind-speed - 8.6 (m/s) ; Direction - Southeast Calm for 54.78% of the time = 378 hours.

Se

RESEARCH DEVELOPMENT

ia :V

Vi 0

Month - May ; Temperature 25 °C Wind-speed - 9 (m/s) ; Direction - South Calm for 39.57% of the time = 273 hours.

Month - July; Temperature 31 °C Wind-speed - 8.2 (m/s) ; Direction - South Calm for 47.54% of the time = 328 hours.

Month - October ; Temperature 19 °C Wind-speed - 5.2 (m/s) ; Direction - East Calm for 80.72% of the time = 557 hours.

3:

Month - April ; Temperature 19 °C Wind-speed - 8.8 (m/s) ; Direction - Southeast Calm for 56.81% of the time = 392 hours.

Month - March ; Temperature 16 °C Wind-speed - 8.6 (m/s) ; Direction - East Calm for 44.06% of the time = 304 hours.

r0 so

Month - February ; Temperature 11 °C Wind-speed - 6.6 (m/s) ; Direction - East Calm for 70.96% of the time = 457 hours.

0

n Se

Month - January ; Temperature 7 °C Wind-speed - 5.8 (m/s) ; Direction - Southeast Calm for 65.51% of the time = 452 hours.

r iz e

02

Sensor 04: Via Verziere

MI-LUNG

BUILDING : 2

BUILDING : 3

BUILDING COMPLEX

MI-LUNG

BUILDING : 1

JULY

JULY

AUGUST Dry bulb temperature (°C)

26.38

19.31

Fig. 65.

Indoor thermal comfort of the buildings

RESEARCH DEVELOPMENT

DECEMBER

JANUARY

RESEARCH DEVELOPMENT

Subsequent to the examination of outdoor Universal Thermal Climate Index (UTCI) values, a pivotal aspect of the research also involved an in-depth exploration of internal UTCI values within buildings. This phase is integral to the investigation as it informs the delineation of domains of sizes for the development of a passive ventilation system considering the air exchange values. Understanding the requisite degree of space ventilation is paramount, particularly considering the air exchange values characteristic of office environments.

The inherent enhancement of thermal comfort through adequate air exchange, as exemplified in Case Study 2 featuring Council House 2 in Melbourne, underscores the significance of internal thermal conditions. The discernment of internal thermal comfort profiles further aids in pinpointing optimal locations for the application of passive ventilation systems, ensuring efficacious outcomes within the studied urban context.

NOVEMBER

109

108

INTERNAL UTCI VALUE OF EACH BUILDING.

MI-LUNG

MI-LUNG

ASHRAE 62.1 office spaces 4 to 6 air exchange per hour

1

D 2

AIR EXCHANGE =

1

volume of air in CFM x 60 volume of room

A

2

C 1

2

1

B 2

Calculating volume of air supply required to a building on each floor considering the volume of space and air exchange rate required.

RESEARCH DEVELOPMENT

VOLUME OF AIR IN CFM = air exchange X volume of room 60 (per hour)

A

2 = 759.43 m2 ; TOTAL = 1,616.63 m2 1 = 777.92 m2 ;

B

2 = 703.97 m2 ; TOTAL = 1,481.89 m2

BUILDING ‘A’

BUILDING ‘B’

1 = 566.37 m2 ;

C

2 = 506.18 m2 ; TOTAL = 1072.56 m2

8.80 sq.m.

Fig. 66.

Vents area calculation

area of vents per floor

4.83 sq.m.

1 = 425.03 m2 ;

D

2 = 461.47 m2 ; TOTAL = 886.5 m2

Fig. 67. Passive ventilation system column calculation considering the area calculation and airexhange values.

5 columns Ø > 2.7 m 2 columns Ø > 2.0 m 1 column Ø > 2.5 m 2 columns Ø > 1.0 m

RESEARCH DEVELOPMENT

As elucidated in the preceding chapters, specifically in Case Study-2 of Council House 2, Melbourne, the role of air exchange emerges as a pivotal factor in augmenting the thermal comfort of the spatial environment. The determination of the requisite volume of air for a system to supply to the space was accomplished through the utilization of universally acknowledged air exchange rates outlined in ASHRAE clause 62.1, tailored for office spaces ranging from 4 to 6 individuals. Figure 66 illustrates the application of the air exchange rate formula, elucidating the calculation of the volume of air necessary for each floor. This methodology was subsequently employed to ascertain the areas of ventilation required for each floor across both buildings. Subsequently, these calculations facilitated the delineation of experimental domains, specifically pertaining to the determination of the requisite number of columns and their respective radii. For an experiment point of view, the system application has been tested at various locations so for the same it was important to extract out the number of columns required for each part for system application so the same can be seen in figure 67.

1 = 857.20 m2 ;

111

110

CALCULATIONS TO DEFINE PARAMETERS.

MI-LUNG

MI-LUNG

POROSITY PANEL

COLUMN SYSTEM

AIR

RELATIVE

TEMPERATURE

HUMIDITY

MRT : MEAN RADIANT TEMPERATURE

1. Constant air circulation.

1. Disipate humidity.

1. Maximize shadowing.

- porosity organisation & density.

- porosity organisation & density.

- Surface area of morphology.

-wood foam material gradient.

-effecient convection looping.

carbon

from

the

- maximize surface area for bio-film application.

Relation of characteristics of UTCI to experiments

2. De-humidifier.

- wood foam material gradient.

EXPERIMENTAL LINKAGE WITH UTCI PARAMETERS. The Universal Thermal Comfort Index (UTCI) functions as a comprehensive metric meticulously crafted for the assessment of thermal comfort or discomfort encountered by individuals within both outdoor and indoor environments. Its intricate methodology incorporates a range of parameters, including air temperature, humidity, wind speed, and radiation. These distinct UTCI parameters were systematically interlinked with the objectives of passive ventilation system experiments.

The specified objectives served as the criteria governing the execution of a generative algorithm, subsequently ensuring the satisfaction of the universal thermal comfort index (UTCI) parameters. This orchestrated approach aimed at optimizing both indoor and outdoor thermal comfort conditions. Thats how the linkage between the experiments as well as the experiements with the thermal comfort parameters has been formulated to have a holistic passive ventilation system for an urban fabric to enhance indoor as well as outdoor thermal comfort.

RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

2. Extract enviornment

Fig. 68.

CONVECTION LOOP

113

112

MORPHOLOGY

COLUMN TYPE: 3

(SINGLE DEPRESSION) plan

CFD plane

plan

CFD plane

windflow direction

windflow direction

windflow direction

CFD plane

windflow direction

windflow direction

plan

(DOUBLE DEPRESSION)

windflow direction

MI-LUNG

COLUMN TYPE: 2

(NO DEPRESSION)

MI-LUNG

COLUMN TYPE: 1

velocity magnitude

Fig. 69.

CFD analysis on various column profiles

iteration : 30

iteration : 50

windflow direction

windflow direction

iteration : 20

iteration : 30

windflow direction

windflow direction windflow direction

iteration : 20

windflow direction

Distinct column profiles were subjected to testing (Figure 69), encompassing one without depressions, a second with a single depression, and a third with two depressions. The CFD outputs from these simulations were meticulously analyzed, revealing that the column profile featuring double depressions (Column type:3) distinctly induces turbulence, thereby exhibiting a heightened capacity for wind capture.

This empirical finding holds significant implications for the optimal design and implementation of passive ventilation systems, as it underscores the potential efficacy of specific column profiles in maximizing wind utilization for enhanced thermal comfort within the designated urban spaces.

iteration : 20

iteration : 30

iteration : 50

iteration : 50

RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

Following the determination of the requisite number of columns and the corresponding vent sizes for each floor, a critical consideration involved comprehending the structural profile of these columns, which plays a crucial role in inducing turbulence and enhancing wind capture efficiency for the passive ventilation system. This wind analysis was conducted through Computational Fluid Dynamics (CFD) simulations, utilizing Autodesk CFD. The simulation environment was configured to reflect the highest recorded wind speed of 8.8m/s and an average temperature of 28.6 °C.

iteration : 10

115

114

CFD ANALYSIS OF COLUMN PROFILES

iteration : 10

windflow direction

iteration : 10

windflow direction

6 (m/s)

windflow direction

0

(36 meters total height)

plan

plan CFD plane

plan

CFD plane

windflow direction

windflow direction

CFD plane

windflow direction

winflow direction

windflow direction

windflow direction

windflow direction

windflow direction

MORPHOLOGY TYPE: 3

(32 meters total height)

windflow direction

MI-LUNG

MORPHOLOGY TYPE: 2

(28 meters total height)

MI-LUNG

MORPHOLOGY TYPE: 1

velocity magnitude

0 iteration : 10

iteration : 20

windflow direction windflow direction

windflow direction iteration : 50

windflow direction

windflow direction

windflow direction

116 RESEARCH DEVELOPMENT

iteration : 30

iteration : 50

Subsequent to the analysis of various column profiles, a Kangaroo simulation methodology was employed for the intricate development of a morphological structure, to be subsequently elucidated in comprehensive detail.

In this process, the identification of the height of each segment within the morphology became imperative, as these dimensions delineated the domains for the ensuing simulation. The experimentation involving the testing of different heights assumed a pivotal role in the morphological development, as the efficacy of wind capture is known to increase with elevation.

Consistent environmental parameters were employed for both the Kangaroo physics simulation and the Computational Fluid Dynamics (CFD) analysis conducted on Autodesk CFD (Figure 70). The findings revealed that the morphology of type 3, with specific domains defined by Kangaroo simulation, exhibited a greater exposed surface area to wind compared to the other two morphologies. Furthermore, it induced turbulence, a characteristic that enhances wind capture efficiency. Consequently, the domains derived from the morphology of type 3 were subsequently employed in executing the Kangaroo simulation, constituting a critical phase in the iterative refinement of the passive ventilation system.

Fig. 70.

CFD analysis on various morphology heights without voids

RESEARCH DEVELOPMENT

iteration : 30

iteration : 30

iteration : 50

iteration : 20

CFD ANALYSIS OF MORPHOLOGY HEIGHTS

117

windflow direction

iteration : 20

6 (m/s)

iteration : 10

windflow direction

windflow direction

iteration : 10

MI-LUNG

MORPHOLOGY TYPE: 5

(28 meters total height)

MORPHOLOGY TYPE: 6

(32 meters total height)

plan

(36 meters total height)

plan

CFD plane

plan

CFD plane

windflow direction

windflow direction

MI-LUNG

MORPHOLOGY TYPE: 4

CFD plane

windflow direction

windflow direction

windflow direction

CFD analysis on various morphology heights with voids

iteration : 50

windflow direction

windflow direction

windflow direction iteration : 20

iteration : 30

windflow direction

windflow direction windflow direction iteration : 30

Similar to the preceding experiment, a spectrum of morphology heights was tested, and it was observed that the morphology of Type 6, characterized by the highest height, exhibited superior performance in wind capture efficiency when voids were integrated. These findings contribute significant insights into the optimization of the morphology design for the passive ventilation system, emphasizing the importance of voids in enhancing wind flow dynamics.

Fig. 71.

iteration : 20

windflow direction

RESEARCH DEVELOPMENT

iteration : 20

iteration : 30

iteration : 50

iteration : 50

RESEARCH DEVELOPMENT

To address this limitation, voids were subsequently introduced between each segment, and a parallel CFD analysis in figure 71 was conducted within the same environmental context to gauge the ramifications of this modification. The outcomes of the CFD analysis underscored that the introduction of voids facilitated the channeling of wind between each segment, concurrently inducing turbulence. This behavior of wind circulation proves advantageous in augmenting wind capture efficiency in contrast to the morphology lacking voids. These voids also helps in directing the wind for the urban public space between the buildings which help to enhance the outdoor thermal comfort.

iteration : 10

119

118

The preceding experiment involving the exploration of morphology heights lacked the incorporation of voids, as discernible in the plan depicted atop each Computational Fluid Dynamics (CFD) simulation in Figure 70. The absence of these voids constrained the wind flow to channel through only two pathways, namely the top and bottom of the morphology. Consequently, no inter-segmental movement of wind was observed within the morphology.

iteration : 10

windflow direction

iteration : 10

windflow direction

CONCLUSION

windflow direction

6 (m/s)

windflow direction

0

windflow direction

velocity magnitude

LEVEL : 3

iteration : 10

iteration : 20

iteration : 20

windflow direction

iteration : 10

windflow direction

windflow direction

iteration : 10

windflow direction

(HEIGHT : )

windflow direction

(HEIGHT : )

windflow direction

MI-LUNG

LEVEL : 2

(HEIGHT : )

MI-LUNG

LEVEL : 1

3 2

iteration : 20

iteration : 30

windflow direction windflow direction

windflow direction

windflow direction

windflow direction

iteration : 50

iteration : 60

iteration : 60

6 (m/s)

CFD ANALYSIS OF MORPHOLOGY(AT VARIOUS LEVELS) Following the selection of the morphology characterized by voids and possessing the greatest height, an additional investigation was conducted within the same Computational Fluid Dynamics (CFD) environment. This exploration focused on testing the morphology at different vertical levels within the column, aiming to comprehend the influence of the tapered column design. The CFD results at each level consistently exhibited analogous outcomes, showcasing the morphology’s capacity to induce wind turbulence and effectively capture the wind. Subsequently, these observations played a crucial role in delineating the domains for the Kangaroo physics simulation, specifically employed for the iterative process of form finding within the morphological structure.

Fig. 72.

iteration : 60

velocity magnitude

CFD analysis on morphology at various heights

RESEARCH DEVELOPMENT

iteration : 50

iteration : 50

windflow direction

120

iteration : 30

0

121

windflow direction

iteration : 30

RESEARCH DEVELOPMENT

windflow direction

windflow direction

1

MI-LUNG

MI-LUNG

FOOTNOTES 35.

“Europe’s Urban Air Quality -Re-Assessing Implementation Challenges in Cities,” March 20, 2019.

36.

“Food Waste.” n.d. Food Safety. https://food.ec.europa.eu/safety/food-waste_en.

37.

Climate Watch, the World Resources Institute (2020).

38.

Sauer, Christiane. It is made of--: New materials sourcebook for architecture and design. Berlin: Gestalten, 2010.

39.

Sauer, Christiane. It is made of--: New materials sourcebook for architecture and design. Berlin: Gestalten, 2010.

40.

Dade-Robertson, Martyn, Alona Keren-Paz, Meng Zhang, and Ilana Kolodkin-Gal. 2017. “Architects of Nature: Growing Buildings with Bacterial Biofilms.” Microbial Biotechnology 10 (5): 1157–63. https://doi.org/10.1111/1751-7915.12833.

41.

Nagendranatha Reddy, C., Hai T. H. Nguyen, Md T. Noori, and Booki Min. 2019. “Potential Applications of Algae in the Cathode of Microbial Fuel Cells for Enhanced Electricity Generation with Simultaneous Nutrient Removal and Algae Biorefinery: Current Status and Future Perspectives.” Bioresource Technology 292 (122010): 122010. https://doi.org/10.1016/j.biortech.2019.122010.

42.

“Thermal Comfort Indices - Universal Thermal Climate Index, 1979-2020 — English.” n.d. Climate-Adapt.eea.europa. eu. Accessed January 3, 2024. https://climate-adapt.eea.europa.eu/en/metadata/indicators/thermal-comfort-indices-universal-thermal-climate-index-1979-2019#:~:text=The%20categories%20relate%20to%20UTCI.

RESEARCH DEVELOPMENT

RESEARCH DEVELOPMENT

44.

“Copernicus Climate Data Store | Copernicus Climate Data Store.” n.d. Cds.climate.copernicus.eu. https://cds.climate.copernicus.eu/cdsapp.

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Gerd Jendritzky, Abdel Maarouf, Dusan Fiala, Henning Staiger, Deutscher Wetterdienst, Freiburg, Germany Environment Canada, Toronto, Canada, De Montfort University, Leicester, U.K.

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The investigative journey commenced with the development of a morphological structure, heavily influenced by environmental conditions and guided by principles derived from a termite mound nest. Utilizing Karamba within the Grasshopper platform, stress lines were extracted to establish the morphology’s structural system. Subsequent steps involved the integration of volume porosity, considering environmental factors and adhering to rules defined by Cellular Automata. This integration created green voids, enhancing wind flow within the structure.

Concurrently, a tubular network and façade system were designed to regulate the convection loop and implement a passive ventilation system. Throughout this experimental trajectory, unwavering attention was dedicated to environmental factors and the integration of primary bio-mimetic principles, as depicted in Figure 73 and 74.

WIND CAPTURE

Fig. 74.

Fig. 73. Workflow of MSC phase experiments. (Left) Passive ventilation system for new office building. (Right)

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PASSIVE CIRCULATION

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The diagram illustrates the comprehensive workflow of the (MSC) phase experiments. Initiated by the meticulous site selection, environmental factors such as solar radiation and wind dynamics intricately shaped the morphology’s organization, contributing to its holistic evolution. This methodological approach facilitated the convergence of diverse experimental domains, culminating in an integrated system tailored for a new office building during the MSC phase.

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This section comprehensively outlines the passive system development during the Master of Science (MSC) phase for a new office tower, emphasizing the integral role of environmental analysis and biomimetic principles, particularly the bulge and depression concepts. The optimized building morphology aimed to capture solar radiation effectively, incorporating a sinusoidal bulge for outward heat absorption and parabolic depressions for enhanced inward windcatching capabilities. The foundational geometry adopted was a cylindrical structure with a 22.5-meter radius and 100-meter height.

BUILDING MORPHOLOGY

values n/2

WINTER PERIOD

MSC CONCLUSION

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Seasonal analyses for solar radiation (summer and winter) and wind (year-round) were conducted, contributing to a well-balanced approach for finalizing the morphology.

This approach prioritized design features, particularly wind capture depressions, achieving equilibrium between the requirements of different seasons (Figure 75). Post-analysis of the morphology informed the development of a passive ventilation system, providing insights for the subsequent M.Arch phase.

TUBULAR NETWORK SYSTEM

FEA - FINITE ELEMENT ANALYSIS

The façade’s exoskeleton incorporated a tubular network system for passive ventilation, strategically positioned based on solar radiation patterns. This design strategy leveraged readily available heat for convection cycles and increased solar energy absorption through building bulging. (tubular network system in figure 75) The hierarchical arrangement of tubes, categorized as hottest, hotter, and hotter, optimized passive ventilation and heat absorption.

Fig. 75.

Passive ventilation system for new tower building.

The final experiment in the MSC phase focused on the porosity panel system and ceiling vents, adapting bionic principles from termite mounds. While two types of porosity

DESIGN DEVELOPMENT

DESIGN DEVELOPMENT

Volumetric porosity was introduced within the building to optimize wind flow and establish internal connections.

Vertical green voids were strategically placed on the east, north, and west sides, enhancing wind movement. This feature, though not taken forward to the M.Arch phase, played a crucial role in optimizing wind circulation.

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The structural analysis (FEA in figure 75) envisioned the building as a shell with a structural exoskeleton connecting to the core through beams. Initial considerations included a 10 cm thick façade morphology, slabs with pores and a 30 cm thickness, and a 20 cm thick central core. Gravity and biocrete were the primary load considerations. Subsequent stress line analysis guided the formulation of the structural exoskeleton, consisting of assemblies of columns and additional diagonal structures.

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panels were initially developed, limitations in matching patterns prompted reformulations for stitchable modules.

Fabrication used agro-based raw materials, and limitations in structural stability necessitate further exploration for practical application. Also the fall-ceiling vents application to tubulate and enhance the wind speed was vital for further development of retrofitten passive ventilation system. This aspect was pivotal and carried forward to the M.Arch phase of research as well while developing a passive ventilation system for already existing buildings.

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This chapter is dedicated to a pivotal realization emphasizing the imperative need for establishing intricate interconnections among diverse experiments spanning environmental, physical organization, and structural domains. This profound insight served as the foundation for the meticulous development of a hierarchical framework, facilitating the seamless formulation of an retrofitted passive ventilation system for an existing building in Milan. The intricate network of relationships between various experiments emerged as a fundamental principle in comprehending the intricacy inherent in each experimental facet.

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The primary considerations in this study revolved around significant parameters derived from the termite mound, serving as the guiding framework for the entire project. These considerations included climate conditions (environmental factors), the ventilation system, structure, and volume porosity. These parameter domains were delineated subsequent to Computational Fluid Dynamics (CFD) studies discussed in the preceding chapter, and they are grounded in abstracted biomimetic principles. These characteristics lay the groundwork for the formulation of the fundamental passive system morphology development, porosity components, vents, and convection loop to facilitate the passive ventilation for the immediate buildings.

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Evolutionary Algorithm

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Panelling Fig. 76.

Diagram of the morphology development process.

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Structure

The generative process comprises six steps as seen in figure 76: 01) identification of areas of application on the site; 02) delineation of the base mesh along with the column profile; 03) demarcation of parameters for Kangaroo Physics simulation; 04) application of an evolutionary algorithm to optimize the morphology using Wallacei X; 05) generation of structural elements, hierarchy, and FE analysis; 06) panelization of the resulting morphology surface for panel application.

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Kangaroo Physics

The morphological development focuses on generating a sequence of interconnected arches forming a vault, characterized by surface curvature derived from adapted bio-mimetic principles and domains abstracted through CFD experiments in the research development phase. The primary objective of this research segment is to induce turbulence and capture maximum wind for the passive system, subsequently supplying it to adjacent buildings.

Additionally, the morphology is paneled with porosity panels designed to capture wind for the system, a concept to be further elaborated. Following the determination of optimal column numbers and corresponding vent sizes for each floor, the study delves into comprehending the structural characteristics of these columns, essential for inducing turbulence and optimizing wind capture in the passive ventilation system. Employing Kangaroo simulation methodology, a detailed morphological structure is intricately developed, with specific emphasis on segment heights dictating the domains for subsequent simulation phases.

The critical factor in morphological development of different heights along with column profiles were experimented in research development chapter, as elevated segments and undulation of buldge and depression are recognized in MSC phase for enhancing wind capture efficiency.

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The fundamental guidelines for each structure are established, and the air exchange formula introduced in the

research development chapter is employed to calculate the required number of chimney columns for each structure.

Accordingly, the floor area coverage for each building is calculated. The subsequent discussion will focus on Area A, representing the most significant area within this context.

Fig. 77.

Top view of site with identified areas and their air exchange values.

The form-finding optimization process employs Kangaroo Physics and Wallacei X, integrated into Grasshopper 3D. These tools are applied to attain the requisite formal complexity for the vault. Given that the primary objective of the morphology is to guide wind for the passive ventilation system within the structure, achieving an optimized curvature profile for both arches and columns is imperative.

Fig. 78.

Area A .Base mesh and anchor points for Kangaroo Physics simulation.

Area A, measuring 38.35m x 17.29m, necessitates five chimney columns with a diameter exceeding 2.7m for the air exchange of each building. The morphology is segmented into two parts: bottom and top in order to channelize the

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To delineate areas of application on the designated site, the primary strategy involves identifying passages between two buildings with a domain width ranging from 14 m to 20 m.

Within this site, four distinct areas have been identified. Each area’s definition is determined by the extension of common passage areas between two buildings, thereby establishing spaces for morphology application. Consequently, buildings are segmented, allowing for multiple passive ventilation applications within each building.

FORM FINDING

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Generate bottom side of the morphology

OPTIMISE the morphology for maximise wind capture through maximising curvature on the surface while maximising the shadow on the ground to enhance UTCI values on ground level

FC 01: Minimum Surface Area FC 02: Maximise Shadows on Ground FC 03: Maximise Curvature FC 04: Maximise Wind Catchment

PHENOTYPE

Generated using Kangaroo Physics plug-in on Grasshopper

GENEPOOL

Column width and depth Column profile control points selector, displacement in normal direction from edge Anchor points selector and displacement in -z direction from roof edge for connective arches.

Selection of points and amplitude of load application on arch area between columns Selection of points, angle and amplitude of load application on sides arch area between columns Voids between arches width and depth

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The evolutionary algorithm’s objective is to optimize the morphology by maximizing wind capture through increased surface curvature. Simultaneously, it seeks to maximize the shadow cast on the ground to elevate Universal Thermal Climate Index (UTCI) values at ground level while minimizing the overall surface area.

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Fig. 79. Diagram of parts of the geometry and oad applications for Kangaroo Physics simulation.

To govern the curvature of the surface, various loads with distinct directions are applied to the central section of the arch. The primary objective is to attain a valley-shaped structure that effectively channels the wind. Voids strategically introduced between arches serve the dual purpose of allowing increased wind penetration while permitting sunlight to filter through.

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wind and allow indirect light reach the ground. Commencing with the bottom part, parameters for the Kangaroo simulation are specified. The parameters for the Kangaroo Physics simulation and the evolutionary algorithm are meticulously defined as follows: (i) Mesh density is set to 0.25m x 0.25m to ensure enhanced resolution. (ii) The axes of each column are established, and anchor points for the column profile are chosen with a radius of 1.75m. (iii) These anchor points are then displaced along a normal vector from the building façade.

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2024.29 141.43 0.231

1487.54 Fitness Criteria :1 4484.3 Fitness Criteria :2 0.103 Fitness Criteria :3 0.753 Fitness Criteria :4 Generative algorithm analysis of morphology.

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FC 4: MAX. WIND CATCHMENT Last Generation

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Fitness Criteria : 2

increasing fitness

Last individual

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Fig. 81.

Bottom. Simulation selected one.

Utilizing Wallacei X, generations 0, 15, 30, and 49 were extracted, along with eight Pareto front solutions. Among these, individual G42 I9 was singled out for further analysis. The selection methodology predominantly emphasized prioritizing curvature over other fitness criteria, alongside minimizing the surface area. Notably, the most stable genes were identified within the column profile genes and the dimensions of the voids.

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GENERATIVE ALGORITHM

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OBJECTIVES

FITNESS CRITERIA

Generate top side of the morphology

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GOAL

OPTIMISE the morphology for maximum wind capture by maximising solar radiation on the surface while maximising the shadow on the ground to enhance UTCI values on ground level

FC 01: Minimum Surface Area FC 02: Maximise Shadows on Ground FC 03: Maximise Wind Catchment FC 04: Maximise Solar Radiation

PHENOTYPE

Generated using Kangaroo Physics plug-in on Grasshopper

GENEPOOL

Spring strength of x mesh wires Maximum load application on z direction on arches vertices to create different height.

Maximum load application on -z direction on arches vertices to flatten the surface.

TOP PART EXPERIMENT

building line

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Fig. 82. Diagram of parts of the geometry and oad applications for Kangaroo Physics simulation.

Concurrently, the evolutionary algorithm aims to optimize the morphology for maximal wind capture by enhancing solar radiation on the surface while concurrently maximizing shadowing on the ground. This approach is geared towards improving UTCI values at ground level.

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Similar to the bottom part of the morephology, the top part parameters for the Kangaroo Physics simulation and the evolutionary algorithm are meticulously defined: 01) Mesh density is configured at 0.25m x 0.25m to ensure enhanced resolution; 02) Anchor points are strategically positioned on the edge of the roof of each building and the exposed edges of the bottom part; 03) Spring strength is selectively applied solely on x-direction mesh wires; 04) Loads are reintroduced to the central part of the arch to govern the curvature of the surface in the z-direction. The overarching objective is to attain a valley shape with minimal height on the first and last arches and maximum height on the central arch to efficiently capture wind from all directions.

3322.26 0.526 20833.33

1005.36 Fitness Criteria :1 3787.88 Fitness Criteria :2 3.62 Fitness Criteria :3 29411.76 Fitness Criteria :4 Generative algorithm analysis of morphology.

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increasing fitness

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Fig. 84.

Top. Simulation selected one.

From the Wallacei X, generations 0, 15, 30, and 49 were systematically extracted, along with the compilation of eight Pareto front solutions. Consequently, the individual G0 I1 emerged as the optimal selection. The selection strategy primarily emphasized prioritizing the average rank of all fitness objectives, along with a consideration for minimizing the surface area. It is noteworthy to observe that the genetic traits exhibited a relatively lower stability compared to those observed in the bottom part of the morphology.

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GENERATIVE ALGORITHM

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The derived morphological configuration advances to the specification of the structural framework and subsequent panelization. In light of the complex nature of the form, a rationalization procedure is executed, entailing the subdivision of the mesh into finer segments to refine the assessment of surface topology. The zenith of the elevation reaches 32 meters, whereas the nadir of the arches is positioned at 14 meters above ground level. The lower height was kept constant considering the modularity of the form, where the bottom part along with columns remain modular and on the part has different heights.

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FORM FINDING RESULT

Fig. 85.

Bottom and top. Final phenotype.

The structure comprises both external and internal frameworks that interconnect within the central region of each arch. The partitioning process commences by segmenting the resultant mesh into distinct sections. Initially, the morphology undergoes a division along the X-direction, resulting in the creation of side A and side B. Subsequently, regions with unsuitable curvature for panel application are identified, determining the surface area for each arch. The final partitioning stage involves an octagonal subdivision of each arch on both sides. Additionally, on the top section, areas with the highest solar radiation values are excluded

Fig. 86.

Bottom and top. Final phenotype.

b

e

g lin

in uild

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The mesh partitioning process is instrumental in defining the vertical rods of the structure. Horizontal structure determination involves considering the central axis of each column, dividing it at one-meter intervals to identify intersecting planes with the structure. The internal structure comprises the chimney and the internal structural connection between side A and side B, affixed to the chimney. The hierarchical order of the structure prioritizes the chimney as the principal element, followed by vertical rods, horizontal rods, and the connection between vertical rods and the chimney. Custom nodes are crafted to connect vertical rods to the chimney, tailored uniquely for each arch.

Fig. 87.

Left. Diagram of horizontal and vertical divisions on a column.

Fig. 88. RIght. Diagram of the whole structure on a column.

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from the panelization process, as they are designated for the tubular network.

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Fig. 89.

Axonometry model for FEA (page 156/157)

Fig. 90.

Fig. 91.

Displacement results (page 156/157) (Left) (Utilisation results)

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To validate the structural conjecture, Finite Element (FE) analysis is conducted. The primary structure incorporates steel s355 with various profiles. Conversely, all surfaces are treated as shells, utilizing the material properties of wood foam previously tested in the MSc phase. Gravity is the sole load taken into consideration, with a total of 34 supports, of which 24 are anchored to the buildings, distributed evenly on each side. The resultant displacement is measured at 0.50 cm, and the utilization factor indicates a higher magnitude for the beams compared to the shell components.

In the conclusive phase, the process of panelization is implemented on the resultant mesh, incorporating panels of types A, B, and C. In alignment with the earlier structural partitioning, individual panels are delineated between vertical and horizontal rods, predominantly possessing four edges.

Panels with three edges are excluded from the paneling process, while those with more than four edges undergo further subdivision to eliminate triangular sections, as such configurations are deemed unsuitable for panel application.

Subsequently, all single panels are further subdivided into 0.25m x 0.25m surfaces to accommodate the panels. To designate surfaces for types A, B, and C, machine learning,

specifically the K-means clustering algorithm, is employed.

Inputs considered for the algorithm encompass area, perimeter, angle, and planarity. Initially, the algorithm is utilized to identify optimal surfaces capable of hosting the panels due to the non-square shape of some surfaces.

Subsequently, an additional K-means clustering is employed to categorize surfaces for types A and B, incorporating an extra input, i.e., their height from ground level. Type C, being a panel designed for smoothing the transition between types A and B, is manually selected from the boundaries of both type A and type B areas.

building line Fig. 92.

Fig. 93.

Left. Diagram of horizontal and vertical divisions on a column.

Right. Diagram of the whole structure on a column.

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PANELLING

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Subsequently, a methodical process was employed to delineate a structural framework conducive to the application of panels, predominantly types A, B, and C. During the formfinding phase, the evolutionary algorithm implemented for the bottom segment exhibited superior efficacy compared to its counterpart for the top segment. Following the form-finding stage, subsequent efforts were dedicated to the refinement of an optimized structure capable of accommodating the panels. The central challenge revolved around determining an optimal panelization strategy that seamlessly conformed to the intricate curvature of the structure’s surface. Future investigations could benefit from a more nuanced assessment of panelization methodologies to enhance the overall system efficacy.

Fig. 94.

Final Axonometric view of morphology

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The developmental trajectory of the morphology underwent meticulous exploration, commencing with the foundational abstraction inspired by termite mounds. An in-depth analysis ensued, delving into the curvature and height of columns to optimize wind turbulence and capture, meticulously scrutinized through Computational Fluid Dynamics (CFD).

The form-finding process was then propelled by generative optimization tools, specifically Wallacei and Kangaroo Physics, orchestrated within the Grasshopper environment. Notably, the morphology was dichotomized into two distinct segments, namely the bottom and top, strategically designed to channel wind effectively and permit the ingress of indirect light to the ground. The lower height was intentionally maintained at a constant level, adhering to the modular design principles, where the bottom part, along with columns, retained modularity while the top part exhibited varied heights.

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CONCLUSION

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TYPE - ‘A’

High altitude - more porosity

POROSITY PANELS

TYPE - ‘C’ TYPE - ‘B’

The pursuit of these three interconnected objectives necessitated a comprehensive research strategy incorporating both digital simulations and empirical testing.

The research methodology unfolded in four distinct stages, delineated by four primary developmental objectives—wind speed, biofilm coverage, porosity, and deformation rate.

The initial phase entailed employing computer simulations to ascertain the optimal façade shape. Subsequently, the outcomes derived from the developmental phase of the material experimentation endeavor were methodically translated into tangible material components, utilizing the most efficacious material composition identified through these studies.

Low altitude - Less porosity

Fig. 95.

Types of porosity panel

DESIGN DEVELOPMENT

DESIGN DEVELOPMENT

In the second dimension, the integration of biofilm into the façade becomes paramount, as it substantially sequesters carbon from the ambient environment through biofilm photosynthesis. Additionally, these biofilm-filled macroplants serve a critical role in air filtration, specifically targeting pollutants such as PM2.5 and NO2. The third facet centers on the composition of the façade, emphasizing the use of agrocomposite materials primarily derived from residual crops such as maize and rice husks. The intentional repurposing of agricultural waste not only mitigates air pollution resulting from incineration but also contributes to a reduction in carbon emissions.

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The porosity panels integrated into the passive ventilation system play a pivotal role in orchestrating wind currents and facilitating the application of biofilm. As an indispensable functional element within the comprehensive passive ventilation framework, the system primarily endeavors to channel wind in a convection loop, simultaneously sequestering carbon and augmenting air quality. The panels function in tandem with the overarching objectives of enhancing wind velocity for more efficient wind collection within the immediate building system, thereby ameliorating thermal comfort through passive ventilation and consequently reducing reliance on electrical power consumption.

wind turblence wind turblence + capture (connectivity)

wind capture

TYPE - ‘A’ TYPE - ‘B’ TYPE - ‘C’

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TURBULENCE GENERATION

BIONIC PRINCIPLES

Fig. 96.

Fig. 97.

3d scanning termite mound model. (Left) Types of facade component and thier objectives (Right)

The first component, designated as Type-A, is designed to maximize wind capture efficacy. It exhibits a higher degree of porosity and adheres to a linear orientation (figure 96). This specific configuration is engineered to optimize the capture

DESIGN DEVELOPMENT

Milan exhibits a persistent trend of relatively low wind speeds throughout the year, seldom surpassing 5 meters per second. To address this climatic characteristic, the research endeavors to extrapolate pertinent insights from the aperture bionic structure observed in termite mounds, discerning two distinct component ideas for the purpose of orchestrating a passive circulation system.

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Conversely, the second component, denoted as Type-B, is characterized by lower porosity and a shifted orientation.

This configuration is strategically intended to augment wind turbulence, thereby enhancing wind speed for more effective capture. The deliberate shift in orientation serves to induce turbulence, contributing to an overall increase in wind speed.

This can be panelled at lower altitudes as it can help in capturing more wind by turbulence.

windflow direction

wind turblence + capture (connectivity)

TYPE - ‘B’

A third component, referred to as Type-C, serves as a transitional element between Type-A and Type-B. It embodies a combined feature set, incorporating both less and more porosity, facilitating a smoother transition in the wind circulation dynamics from Type-A to Type-B. The nuanced integration of these component types aims to optimize the passive circulation system’s performance in mitigating the challenges posed by Milan’s predominantly low wind speeds. currents within the system which can be panelled at higher altitude to capture maximum wind. (Figure 96)

8 (m/s)

0 Fig. 98.

Types of porosity panel

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of wind currents within the system.This specifically can be panelled at lower altitude as wind reaching at the lower point of the morphology would be comparatively quite less. (Figure 96)

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Generate component ‘TYPE A’ for maximum wind capture zone for the passive ventilation system

OBJECTIVES

OPTIMISE the component for maximum wind capture through maximum porosity and maximum surface area for biofilm application

FITNESS CRITERIA

FC 1: maximum porosity for wind capture FC 2: maximum surface area for bio-film application FC 3: maximum wind capture facing surface volume (front surface) FC 4: minimum total volume FC 5: minimum displacement

PHENOTYPE

Generated using JELLYFISH and WEAVERBIRD plug-in on grasshopper

GENEPOOL

Size of the component Grids on U/V axes, Radius of outer and inner spheres for voids, Radius of connection, Thickness of component

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COMPUTATION OF TYPE A

DESIGN DEVELOPMENT

4. THICKNESS

3. SPLIT WITH BOUNDING BOX

Fig. 99.

Type-A facade formation computational sequence.

The optimization process was undertaken with the overarching objective of refining Component Type-A for the wind collection segment of the passive system. Central to this optimization endeavor was the dual focus on enhancing both wind collection efficiency and the attachment of biofilm.

Multiple gene pools were deployed to elicit diverse phenotypic variations for subsequent optimization and screening processes. These gene pools included configurations related to the grids of u/v, the radius of volumes, the radius of the connection part, and the overall thickness of the component.

DESIGN DEVELOPMENT

The generation of primitives encompassed four key procedural stages (figure 98): setting the thickness, employing a split operation with the bounding box, configuring input and output parameters, and establishing inter-volume connections. The linking of volumes predominantly relied on Grasshopper components such as “jellyfish” and “weaverbird.”

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The principal experimental tool employed in this research was the Wallacei X optimization tool integrated within the Grasshopper environment. Within this framework, the input and output volumes for Component Type-A were meticulously configured to delineate the points of wind ingress and egress, respectively. These volumes were strategically spread in a linear fashion, enabling the component to effectively capture wind by facilitating its unimpeded flow through the system.

FC 5: MIN. DISPLACEMENT -230724 -1147063 386012 165208 105093 77055

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Fig. 100. Fitness Criteria of porosity panel type -A -128261.9

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Fitness Criteria : 4

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FC 4: MIN. TOTAL VOLUME

173

SECTIONDEVELOPMENT DESIGN TITLE

FC 3: MAX. WIND CAPTURE SURFACE 2.02e-006

2.26e-006

2.56e-006

2.95e-006

3.49e-006

4.26e-006

5.48e-006

7.67e-006

1.28e-005

3.81e-005

-3.88e-005

-1.28e-005

-7.70e-006

Fitness Criteria : 3

39164

43616

49210

56450

66187

79984

101048

137173

213498

481299

-1892282

-319016

-174191

Fitness Criteria : 2

FC 2: MAX. BIO-FILM SURFACE 1137837

1283486

1471898

1725142

2083639

2630217

3565519

5533073

12345879

-53378851

-8441190

-4582964

-3145325

Fitness Criteria : 1

MI-LUNG

MI-LUNG

FC 1: MAX. POROSITY

Fig. 101. SD Graphs and pareto fronts of porosity panel type - A

MI-LUNG

1.80e-005

347295.82

2.40e-005

MI-LUNG

1675154.11

608820000000 Last individual

MODULE SIZE

CFD ANALYSIS

windflow direction

boundary : X

increasing fitness

8 (m/s)

branch width : Y 0 First individual 17159722.7

3.72e-006

75051.99

5.14e-006

21290000000

Fitness Criteria :1

Fitness Criteria :2

Fitness Criteria :3

Fitness Criteria :4

Fitness Criteria :5

OPTIMIZATION STRATEGY

In the pursuit of optimizing the morphology for the ideal porosity component, we employed five distinct training objectives: (Figure 99)

The optimization process sought to identify the most effective phenotype through the utilization of a highly porous generative algorithm. Following the optimization of these training objectives, the initial phase of selection (Figure 102) volved applying two conditions to filter the obtained phenotypic pareto fronts of the entire generative algorithm: a variable length ranging from more than 225 cm to less than 250 cm and a width of the connecting component ranging from more than 20.0 cm to less than 30.0 cm. This selection criterion yielded a refined Component Type-A characterized by increased lightness and structural robustness.

Fitness Objective 1: Maximization of porosity Fitness Objective 2: Maximization of biofilm application

174

Fitness Objective 3: Maximization of the surface area susceptible to wind capture (front) Fitness Objective 4: Minimization of total volume to reduce overall material consumption

SECTIONDEVELOPMENT DESIGN TITLE

After running the generative algorithm standard deviation graphs (SD) and parallel coordinate points (PCP) were extracted using Wallecei X platform on grasshopper to analyse for further selection of phenotype.

A standard deviation graph, also known as a deviation chart or variability chart, is a graphical representation of the spread or dispersion of a dataset. Typically, it includes the mean (average) and one or more standard deviations above and below the mean.

A Parallel Coordinates Plot (PCP) is a data visualization technique used to represent multivariate data in a two-dimensional space.

It is particularly useful for exploring relationships and patterns within high-dimensional datasets.

Fig. 102. PCP chart of GA for porosity panel type-A

Fig. 103. Optimisation stratergy for selection of phenotype

DESIGN DEVELOPMENT SECTION TITLE

The subsequent phase of the selection process entailed a meticulous wind analysis conducted through Computational Fluid Dynamics (CFD) on Autodesk CFD. The parameters for this CFD analysis mirrored those utilized in the morphology examination, maintaining consistency with the highest recorded wind speed of 8.8 meters per second and an average ambient temperature of 28.6 °C.

Fitness Objective 5: Minimization of displacement

175

GENERATIVE ALGORITHM

MI-LUNG

MI-LUNG

GENERATION : 23 INDIVIDUAL : 02

8 (m/s) x windflow direction

z

y

w

0

GEN : 23 IND : 08

Front isometric view

FITNESS CRITERIA VALUES

8 (m/s)

windflow direction

FC2 FC1

FC3

FC5 0

GEN : 19 IND : 15

x : 250.0 mm y : 250.0 mm z : 80.00 mm w : 25.00 mm

FC4

FC 1: Maximum porosity for wind capture - 1.1673e-7 Rank : 164/500 FC 2: Maximum surface area for bio-film application - 5.941e-6 Rank : 163/500 FC 3: maximum wind turbulence - 218494.022365 Rank : 334/500 FC 4: minimum total volume - 8.1882e-6 Rank : 164/500 FC 5: minimum displacement - 2.4797e+11 Rank : 338/500

Front view

SELECTION CRITERIA

0

GEN : 12 IND : 16

Back isometric view

windflow direction

GEN : 23 IND : 02

0 Fig. 104. CFD analysis of various phenotypes to shortlist the best performing one.

Back view Fig. 105. Best performing phenotype of Type-A

Simultaneously to shortlist the the four phenotypes depicted in Figure 103 additional scrutiny through the application of K-means clustering, a machine learning technique facilitated on the Wallacei X platform was applied on pareto fronts as they are the best performing phenotypes. This clustering methodology aids in grouping phenotypes with similar Fitness Criteria (FC) values. Subsequently, these four phenotypes underwent wind analysis via CFD simulations conducted on Autodesk CFD, ultimately culminating in the identification of the phenotype with optimal wind capture capabilities.

Detailed specifications, encompassing sizes and FC values, are elucidated in Figure 104.

DESIGN DEVELOPMENT SECTION TITLE

SECTIONDEVELOPMENT DESIGN TITLE

8 (m/s)

Following the iterative filtration process employing multiple selection phases, individual 02 from the 23rd generation (Figure 104) emerged as the most proficient within the cohort of 500 individuals. This determination was based on the predefined fitness objectives set by the generative algorithm.

The selection of this particular phenotype involved a multitiered approach, initially assessing the dimensions of the component and its branches.

177

176

windflow direction

8 (m/s)

Z

Z

iteration : 20

iteration : 10

15 (m/s) X

Y

Y

15 (m/s)

15 (m/s)

15 (m/s)

15 (m/s)

Z

X

iteration : 40

0

iteration : 30

0

iteration : 40

0

X

Y

Y

Y

0

15 (m/s)

15 (m/s)

15 (m/s)

15 (m/s)

Y

Z

X

Y

iteration : 60

0 iteration : 50

0 iteration : 60

0

X

Y

Y

0

0

0

179

REGIONAL SCALE TYPE-A Highest performing section

Z

Y

length of combined porosity panel

Y

length of wind turbulence

Average performing section

Fig. 106. Regional scale CFD tests for Type-A

X length of combined porosity panel

DESIGN DEVELOPMENT SECTION TITLE

The chosen individual was amalgamated with others to form a regional-scale model, and this composite was subjected to a comprehensive Computational Fluid Dynamics (CFD) simulation (figure 105). The simulation replicated the prior environmental conditions, and sectional analyses were conducted at various intervals. The resulting data, mapped on XY axes, facilitated an in-depth comprehension of wind capture efficacy and turbulence generation across different segments. This invaluable information was subsequently not everaged in the meticulous paneling of components onto the morphology but can be further investigated for effecient passive ventilation system development.

length of wind turbulence

178

X

Y

0

SECTIONDEVELOPMENT DESIGN TITLE

15 (m/s)

0

Y

iteration : 50

Z

iteration : 20

iteration : 10

Y

iteration : 30

Z

15 (m/s)

MI-LUNG

MI-LUNG

15 (m/s)

MI-LUNG

Generate component ‘TYPE B’ for maximum wind turbulence zone to enhance the wind speed of the captured wind for the passive ventilation system

OBJECTIVES

OPTIMISE the component for maximum wind turbulence to enhance the speed of the capture wind through minimum porosity and maximum surface area for biofilm application

FITNESS CRITERIA

1. SET INPUT AND OUT PUT VOLUMES

2. CONNECT VOLUMES

MI-LUNG

GOAL

FC 1: minimum porosity for wind capture FC 2: maximum surface area for bio-film application FC 3: maximum wind turbulence FC 4: minimum total volume FC 5: minimum displacement

PHENOTYPE

Generated using JELLYFISH and WEAVERBIRD plug-in on grasshopper

GENEPOOL

Size of the component Grids on U/V axes, Radius of outer and inner spheres for voids, Radius of connection, Thickness of component

COMPUTATION OF TYPE B

180 DESIGN DEVELOPMENT

3. SPLIT WITH BOUNDING BOX

Fig. 107. Type-A facade formation computational sequence.

The optimization process was undertaken with the overarching objective of refining Component Type-B for the wind turbulence while collection segment of the passive ventilation system. All other configuration for the formation of porosity panel type-B stays similar to type-A.

DESIGN DEVELOPMENT

4. THICKNESS

The generation of primitives encompassed four key procedural stages (figure 106): which were identical to porosity panel type-A component. The linking of volumes predominantly relied on Grasshopper components such as “jellyfish” and “weaverbird.”

181

Similar to Type A porosity component Wallacei X optimization tool integrated within the Grasshopper environment was employed for type B and type C as well. Within this framework, the input and output volumes for Component Type-B were meticulously configured similarly to type A. These volumes were strategically spread in a staggered fashion, enabling the component to effectively turbulate to enhance the speed while capturing the wind by facilitating its unimpeded flow through the system.

FC 5: MIN. DISPLACEMENT -222469.7 -845025.0 469880.73 183830.42 114267.53 82898.19

65042.4

53515.48 45459.13

39511.05 34939.41 31315.99

-2.55e-013 -5.54e-013 3.24e-012 4.13e-013 2.20e-013

1.50e-013

1.14e-013 9.19e-014

7.69e-014 6.62e-014 5.81e-014

DESIGN DEVELOPMENT SECTION TITLE

-1.66e-013

Fig. 108. Fitness Criteria of porosity panel type -B -128096.9

EXPERIMENT TRANSFORMATION

-1.23e-013

Fitness Criteria : 5

Fitness Criteria : 4

182

FC 4: MIN. TOTAL VOLUME

183

SECTIONDEVELOPMENT DESIGN TITLE

FC 3: MAX. WIND TURBULENCE 1.46e-006

1.64e-006

42113.03

47254

53824.68

1.87e-006

74559.78

2.58e-006

62517.79

92347.49

3.19e-006

2.17e-006

121281.61

176619.73

324834.57

2019799.88

-478860.0

-214055.5

-137834.5

4.18e-006

6.05e-006

1.09e-005

5.69e-005

-1.78e-005

-7.68e-006

-4.90e-006

Fitness Criteria : 3

Fitness Criteria : 2

FC 2: MAX. BIO-FILM SURFACE 1316963

1490318

1716231

2022871

2462924

3147662

4359754

7089917

18968118

-28085716

-8069031

-4711293

-3326887

Fitness Criteria : 1

MI-LUNG

MI-LUNG

FC 1: MIN. POROSITY

Fig. 109. SD Graphs and pareto fronts of porosity panel type - B

MI-LUNG

1.80e-005

475719.66

2.10e-005

MI-LUNG

1774245.06

12215000000000 Last individual

MODULE SIZE

CFD ANALYSIS

windflow direction

boundary : X

increasing fitness

8 (m/s)

branch width : Y 0

23213705.37

2.68e-006

73924.67

4.50e-006

406200000000

Fitness Criteria :1

Fitness Criteria :2

Fitness Criteria :3

Fitness Criteria :4

Fitness Criteria :5

OPTIMIZATION STRATEGY

In the pursuit of optimizing the morphology for the ideal porosity component, we employed five distinct training objectives (figure 107):

The optimization process sought to identify the most effective phenotype through the utilization of a low porous generative algorithm which is contradict to type A experiment. Following the optimization of these training objectives, the initial phase of selection involved applying two conditions to filter (figure 110) the obtained phenotypic pareto fronts of the entire generative algorithm same as previous type A porosity component.

This selection criterion yielded a refined Component Type-B characterized by increased stiffness and structural robustness.

Fitness Objective 1: Minimization of porosity

SECTIONDEVELOPMENT DESIGN TITLE

Fitness Objective 2: Maximization of biofilm application Fitness Objective 3: Maximization of wind turbulence Fitness Objective 4: Minimization of total volume to reduce overall material consumption

Same as it was done for previous experiment for porosity component type-A the subsequent phase of the selection process entailed a meticulous wind analysis conducted through Computational Fluid Dynamics (CFD) on Autodesk CFD.

Fitness Objective 5: Minimization of displacement After running the generative algorithm standard deviation graphs (SD) and parallel coordinate points (PCP) were extracted using Wallecei X platform on grasshopper to analyse for further selection of phenotype same as it was done for type-A porosiy component.

Fig. 110. PCP chart of GA for porosity panel type-B

Fig. 111. Optimisation stratergy for selection of phenotype

DESIGN DEVELOPMENT SECTION TITLE

GENERATIVE ALGORITHM

185

184

First individual

MI-LUNG

MI-LUNG

GENERATION : 11 INDIVIDUAL : 02

8 (m/s) x windflow direction

z

y

w

x : 250.0 mm y : 250.0 mm z : 80.00 mm w : 25.00 mm

0

GEN : 09 IND : 00

Front isometric view

FITNESS CRITERIA VALUES

8 (m/s)

FC2 windflow direction

FC1

FC3

FC5 FC4

0

GEN : 10 IND : 11

FC 1: Minimum porosity for wind capture - 1.5164e-7 Rank : 246/500 FC 2: Maximum surface area for bio-film application - 5.7816e-6 Rank : 202/500 FC 3: maximum wind turbulence - 213119.595522 Rank : 283/500 FC 4: minimum total volume - 9.8442e-6 Rank : 246/500 FC 5: minimum displacement - 2.7178e+12 Rank : 283/500

Front view

SELECTION CRITERIA

0

GEN : 21 IND : 16

Back isometric view

windflow direction

GEN : 11 IND : 02

0 Fig. 112. CFD analysis of various phenotypes to shortlist the best performing one.

Back view Fig. 113. Best performing phenotype of Type-B

Simultaneously to shortlist the the four phenotypes depicted in Figure 90 additional scrutiny through the application of K-means clustering, a machine learning technique facilitated on the Wallacei X platform was applied on pareto fronts as they are the best performing phenotypes same as it was carried out for type-A. Subsequently, these four phenotypes underwent wind analysis via CFD simulations conducted on Autodesk CFD (figure 111), ultimately culminating in the identification of the phenotype with optimal turbulance to enhance the wind speed for wind capture capabilities.

Detailed specifications, encompassing sizes and FC values, are elucidated in Figure 112.

DESIGN DEVELOPMENT SECTION TITLE

SECTIONDEVELOPMENT DESIGN TITLE

8 (m/s)

Following the iterative filtration process employing multiple selection phases, individual 02 from the 11th generation (figure 112) emerged as the most proficient within the cohort of 500 individuals. This determination was based on the predefined fitness objectives set by the generative algorithm.

The selection of this particular phenotype involved a multitiered approach, same as type A porosity component the properties of the component.

187

186

windflow direction

8 (m/s)

15 (m/s)

Z

Z

Z

Y

Z

0

15 (m/s)

15 (m/s)

Z

15 (m/s)

15 (m/s)

Z

X

15 (m/s)

Y

X

0

15 (m/s)

X

15 (m/s)

Y

Y

0

0

0

189

188

0

X

0

Y

15 (m/s)

0

15 (m/s)

Y

0

Y

0

Y

0

X

Y

Y

Y

0

Y

15 (m/s)

MI-LUNG

X

MI-LUNG

15 (m/s)

Highest performing section

X

length of combined porosity panel

length of wind turbulence

Average performing section length of wind turbulence

The chosen individual was amalgamated with others to form a regional-scale model, and this composite was subjected to a comprehensive (Figure 113) Computational Fluid Dynamics (CFD) simulation as the previous one. Similarly, the resulting data, mapped on XY axes, facilitated an in-depth comprehension of wind turbulence and capture efficacy across different segments.

This invaluable information was subsequently not everaged in the meticulous paneling of components onto the morphology but can be further investigated for effecient passive ventilation system development.

Z

Y

Fig. 114. Regional scale CFD tests for Type-B

Y length of combined porosity panel

DESIGN DEVELOPMENT SECTION TITLE

SECTIONDEVELOPMENT DESIGN TITLE

REGIONAL SCALE TYPE-B

MI-LUNG

Generate component ‘TYPE C’ as a connection betweem type A and type B which would turbulate and simultaneosly capture the wind for the passive ventilation system

OBJECTIVES

OPTIMISE the component for maximum wind turbulence and simultaneous wind capture through smoother transition of porosity along with maximum surface area for biofilm application

FITNESS CRITERIA

1. SET INPUT AND OUT PUT VOLUMES

2. CONNECT VOLUMES

MI-LUNG

GOAL

FC 1: smoother transition of porosity for wind capture FC 2: maximum surface area for bio-film application FC 3: maximum wind turbulrnce and wind capture facing surface-volume (front surface) FC 4: minimum total volume FC 5: minimum displacement

PHENOTYPE

Generated using JELLYFISH and WEAVERBIRD plug-in on grasshopper

GENEPOOL

Size of the component Grids on U/V axes, Radius of outer and inner spheres for voids, Radius of connection, Thickness of component

COMPUTATION OF TYPE C

190 DESIGN DEVELOPMENT

3. SPLIT WITH BOUNDING BOX

Fig. 115. Type-A facade formation computational sequence.

The optimization process was undertaken with the overarching objective of refining component type-B for the wind turbulence and collection segment of the passive ventilation system. All other configuration for the formation of porosity panel type-c stays similar to previous experiments.

DESIGN DEVELOPMENT

4. THICKNESS

The generation of primitives encompassed four key procedural stages (Figure 114): which were identical to previous porosity panel experiments. The linking of volumes predominantly relied on Grasshopper components such as “jellyfish” and “weaverbird.”

191

Similar to type A and type B porosity component Wallacei X optimization tool integrated within the Grasshopper environment was employed for type C as well. Within this framework, the input and output volumes for Component type-C were meticulously configured similarly to type A and B. The component type C was halved to form the connective component between type-A and type-B for smoother transition. So, type-C component is characterized as a combination of type-A (high porosity) and type-B (low porosity).

These volumes were strategically spread in a staggered as well as linear fashion, enabling the component to effectively turbulate to enhance the speed while simultaneosly capture the wind by facilitating its unimpeded flow through the system.

FC 5: MIN. DISPLACEMENT -169677 -1081937 247218 110935 71512 52762

41802

34612 29532

25753 22831 20505

-3.18e-013 -6.60e-013 9.13e-012 5.76e-013 2.98e-013

2.01e-013

1.51e-013 1.21e-013

1.01e-013 8.70e-014 7.63e-014

DESIGN DEVELOPMENT SECTION TITLE

-2.10e-013

Fig. 116. Fitness Criteria of porosity panel type -C -92057

EXPERIMENT TRANSFORMATION

-1.56e-013

Fitness Criteria : 5

Fitness Criteria : 4

192

FC 4: MIN. TOTAL VOLUME

193

SECTIONDEVELOPMENT DESIGN TITLE

FC 3: MAX. WIND CAPTURE SURFACE 2.17e-006

2.43e-006

2.75e-006

3.17e-006

3.75e-006

4.58e-006

5.88e-006

8.22e-006

1.36e-005

3.99e-005

-4.30e-005

-1.40e-005

-8.34e-006

Fitness Criteria : 3

27340

30532

34568

39833

46991

57284

73352

101948

167086

462744

-601360

-182255

-107402

Fitness Criteria : 2

FC 2: MAX. BIO-FILM SURFACE 730074

825228

948905

1116186

1355071

1724049

2369158

3785699

9415030

-19332744

-4769523

-2720322

-1902796

Fitness Criteria : 1

MI-LUNG

MI-LUNG

FC 1: AVERAGE. POROSITY

Fig. 117. SD Graphs and pareto fronts of porosity panel type - C

MI-LUNG

2.50e-005

333321.23

3.40e-005

MI-LUNG

1107088.69

9312100000000 Last individual

MODULE SIZE

CFD ANALYSIS

windflow direction

boundary : X

increasing fitness

8 (m/s)

branch width : Y 0

15322382.94

4.27e-006

63576.21

6.48e-006

399780000000

Fitness Criteria :1

Fitness Criteria :2

Fitness Criteria :3

Fitness Criteria :4

Fitness Criteria :5

GENERATIVE ALGORITHM

OPTIMIZATION STRATEGY

In the pursuit of optimizing the morphology for the ideal porosity component, we employed five distinct training objectives (Figure 115):

The optimization process sought to identify the most effective phenotype through the utilization of a low as well highly porous generative algorithm which is collaborative of typeA and typeB experiments. Following the optimization of the training objectives, the initial phase of selection involved applying two conditions (Figure 118) to filter the obtained phenotypic pareto fronts of the entire generative algorithm same as previous experiments.

This selection criterion yielded a refined component type-C characterized by increased lightness and structural robustness.

Fitness Objective 1: Minimization of porosity for one halve and maximization of porosity for the other one.

Fitness Objective 3: Maximization of wind turbulence for one halve and maximization of windcapture for the other one.

Same as it was done for previous experiments for porosity component type-A and B the subsequent phase of the selection process entailed a meticulous wind analysis conducted through Computational Fluid Dynamics (CFD) on Autodesk CFD.

Fitness Objective 4: Minimization of total volume to reduce overall material consumption Fitness Objective 5: Minimization of displacement After running the generative algorithm standard deviation graphs (SD) and parallel coordinate points (PCP) were extracted using Wallecei X platform on grasshopper to analyse for further selection of phenotype same as it was done for previous experiments.

Fig. 118. PCP chart of GA for porosity panel type-C

Fig. 119. Optimisation stratergy for selection of phenotype

DESIGN DEVELOPMENT SECTION TITLE

SECTIONDEVELOPMENT DESIGN TITLE

Fitness Objective 2: Maximization of biofilm application

195

194

First individual

MI-LUNG

MI-LUNG

GENERATION : 10 INDIVIDUAL : 14

8 (m/s) x windflow direction

z

y

w

x : 250.0 mm y : 250.0 mm z : 80.00 mm w : 25.00 mm

0

GEN : 06 IND : 09

Front isometric view

FITNESS CRITERIA VALUES

8 (m/s)

windflow direction

FC2 FC1

FC3

FC5 FC4

0

GEN : 08 IND : 01

FC 1: Maximum porosity for wind capture - 3.5788e+6 Rank : 263/500 FC 2: Maximum surface area for bio-film application - 90909.09 Rank : 252/500 FC 3: maximum wind turbulence - 7.4297e-6 Rank : 245/500 FC 4: minimum total volume - 0.000016 Rank : 262/500 FC 5: minimum displacement - 2.239e+12 Rank : 262/500

Front view

SELECTION CRITERIA

Back isometric view

windflow direction

SECTIONDEVELOPMENT DESIGN TITLE

8 (m/s)

GEN : 17 IND : 10

0 Fig. 120. CFD analysis of various phenotypes to shortlist the best performing one.

Back view Fig. 121. Best performing phenotype of Type-C

Simultaneously to shortlist the the four phenotypes depicted in Figure 119 additional scrutiny through the application of K-means clustering, a machine learning technique facilitated on the Wallacei X platform was applied on pareto fronts as they are the best performing phenotypes same as it was carried out for previous experiments. Subsequently, these four phenotypes (figure 119) underwent wind analysis via CFD simulations conducted on Autodesk CFD, ultimately culminating in the identification of the phenotype with optimal wind capture and turbulence capabilities. Detailed specifications, encompassing sizes and FC values, are elucidated in Figure 120.

DESIGN DEVELOPMENT SECTION TITLE

0

GEN : 10 IND : 14

Following the iterative filtration process employing multiple selection phases, individual 14 from the 10th generation (Figure 120) emerged as the most proficient within the cohort of 500 individuals. This determination was based on the predefined fitness objectives set by the generative algorithm.

The selection of this particular phenotype involved a multitiered approach, same as previous experiments which were the properties of the component.

197

196

windflow direction

8 (m/s)

15 (m/s)

15 (m/s)

15 (m/s)

X

Z

Y

Z

Y

X

Y

Y

0

0

0

0

15 (m/s)

15 (m/s)

15 (m/s)

15 (m/s)

X

Z

Y

Z

X

Y

Y

0

0

15 (m/s)

15 (m/s)

Y

0

0

15 (m/s)

15 (m/s)

X

Z

Y

Z

Y

0

0

0

199

REGIONAL SCALE TYPE-B Highest performing section

Y

Y

length of combined porosity panel

Z

length of wind turbulence

Average performing section

Fig. 122. Regional scale CFD tests for Type-C

X length of combined porosity panel

DESIGN DEVELOPMENT SECTION TITLE

The chosen individual was amalgamated with others to form a regional-scale model (Figure 121), and this composite was subjected to a comprehensive Computational Fluid Dynamics (CFD) simulation as the previous experiments. Similarly, the resulting data, mapped on XY axes, facilitated an in-depth comprehension of wind turbulence and capture efficacy across different segments. This invaluable information was subsequently not everaged in the meticulous paneling of components onto the morphology but can be further investigated for effecient passive ventilation system development.

length of wind turbulence

198

X

Y

Y

0

SECTIONDEVELOPMENT DESIGN TITLE

MI-LUNG

MI-LUNG

15 (m/s)

MI-LUNG

MI-LUNG

TYPE - ‘A’

wind capture

High altitude - more porosity

TYPE - ‘C’

wind turblence

TYPE - ‘A’ : GEN - 23 ; IND - 02.

wind turblence + capture (connectivity)

TYPE - ‘B’

Low altitude - Less porosity

Fig. 123. Types of porosity panel

TYPE - ‘B’ : GEN - 11 ; IND - 02.

In accordance with the empirical findings, Generation 23 individual 02 and Generation 11 individual 02 emerged as the two most optimal solutions. The recommended deployment strategy involves situating G11 individual 02, characterized by low porosity (type B) (figure 122), in proximity to type A components and also at low altitudes which are characterized with less wind velocities. Conversely, G23 individual 02, characterized by high porosity (type A) (figure 122), is recommended for placement in the passive ventilation system exposed to the highest wind velocities which means at higher altitudes. Additionally, Generation 10 individual 02 (type C) (figure 122) was used as a connection between type A and type B for smoother transition. This configuration serves to induce turbulence, channeling the wind into the designated wind-intensive region of the façade system. The distinct roles assigned to these two components are strategically designed to enhance and concentrate the wind flow.

DESIGN DEVELOPMENT

DESIGN DEVELOPMENT

CONCLUSION 201

200

TYPE - ‘C’ : GEN - 10 ; IND - 14.

MI-LUNG

MI-LUNG

R

U PO

WOOD FOAM MIXED LIQUID BA

KE

SILICONE MOLD

APPLICATION PROCESS Following the outcomes derived from the material tests conducted during the research development phase (Figure 48), pertinent properties were discerned for the formulation of the porosity component. The pivotal procedure in the construction process involves the pouring of the liquid wood foam material into silicone molds, followed by a one-hour baking process (Figure 123). However, constrained by the dimensions of the oven, a quarter of the wood foam porosity component was fabricated utilizing diverse agricultural-based materials, including rice husk. This wooden foam element, constituting a 1/6 application, measures 12cm × 12cm × 10cm.

The ensuing steps delineate the comprehensive evolution of the porosity component, employing diverse techniques which are explained in the figure 123.

Fig. 124. Mold fabrication diagram.

DESIGN DEVELOPMENT

DESIGN DEVELOPMENT

1/6 of COMPONENT (1:4 scale)

203

202

OVEN

MI-LUNG

MI-LUNG

SILICONE

SILICONE MOLD

SILICONE MOLD

Fig. 125. Mold fabrication diagram. (Left) Fig. 126. Picture of the mold process. (Right)

The counter molds for silicone molds were crafted using laser-cut 6 mm plywood, and 1/6 component models were generated from 3D printed models (Figure 124,125).

Subsequently, the silicone material was meticulously poured into the meticulously prepared molds. Following a three-hour curing period, the silicone mold was delicately removed.

DESIGN DEVELOPMENT

DESIGN DEVELOPMENT

205

204

MOLD FOR SILICONE

MI-LUNG

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Fig. 127. Pictures of the physical test. (Left) Fig. 128. Pictures of the baking process. (Right)

Notwithstanding the success, it is noteworthy that in certain regions, the accumulation of air bubbles gives rise to fissures that compromise the structural integrity of the material.

(Figure 126) Addressing this challenge in the manufacturing process is crucial for the project’s advancement.

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A quarter segment of the wood foam component was generated following a drying duration of 48 hours and subsequent baking for one hour at 200 degrees Celsius (Figure 127). 1/6 of the porosity component was successfully fabricated using the baking process to test the strength and stability of the fabricated model. This achievement underscores the feasibility of crafting a porosity component from wood foam. However, it is imperative to conduct further research to ascertain its viability for application on authentic building surfaces.

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CNC DEVELOPMENT The efficacy of Computer Numerical Control (CNC) technology in the molding process was investigated, recognized for its superior efficiency compared to 3D-printed molds and its capacity to generate larger molds for facade components.

Given the hyperbolic shape of the facade components and the limitations of the CNC lathe tool in cutting negative angles, two distinct manufacturing methods—positive and negative—were necessitated for CNC sheet machining. This experimental phase involved the meticulous creation of a 1:2 scale mold using grey foam (figure 128), exemplifying the precision and intricacy achieved in the fabrication process.

Fig. 129. Breakdown of the different parts of the facade panel for CNC process.

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While wood foam raw material can be further developed and utilized for experimental phase to be 3D printed directly using robotic arm. Because of its properties, including unusually high fluidity and inherent challenges in attachment and shaping, warrant careful consideration. A robotic arm 3D printer can also be employed to produce the plastic mold, subsequently used to create a silicon mold for the fabrication of the wood foam component. This approach closely aligns with the methodology employed in silicon mold manufacturing.

Fig. 130. Set-up and representation of robotic development process.(Right top) Fig. 131. Pictures of robotic fabrication.(Right bottom)

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Moreover, the integration of a robotic arm presents notable advantages for 3D printing compared to CNC, particularly in the context of larger-scale fabrication, facilitating the production of substantial facade components. The six-axis rotational capability of the robotic arm enhances the flexibility of printing angles, enabling the seamless creation of negativeangle models in a single printing operation. To assess the practicality of employing a robotic arm for 3D printing in the wood foam molding process for real-world applications, a partial 1:1 3D print test of the component using plastic was undertaken of the crucial curvatures to understand the possibility of fabrication (figure 129). The model wasn’t fabricated using wooden foam as a raw materials but that remains a potential avenue for further exploration.

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DESIGN DEVELOPMENT Fig. 132. Life cycle of the facade panels.

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In the context of this project, the facade component was conceptualized as a sustainable replacement material, emphasizing considerations for lifecycle management (Figure 131). A novel agro-composite module component gradually takes the place of the retired module after fulfilling its intended purpose. The replaced module undergoes a recycling procedure, involving shredding and integration into the recycling system. This process facilitates the production of fresh recycled wood panels or recycled facade components.

It is noteworthy that the introduction of biofilm material in this phase expedites the formation of biofilm under new humid environmental conditions, underscoring the circular and sustainable nature of the proposed recycling system.

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Wood foam was employed in this experimental phase due to its distinctive properties. A comprehensive 9-step sequence was devised for the practical execution of the porosity construction process. This included an initial step utilizing a robotic arm for mold fabrication, followed by the subsequent production of a silicone mold. The succeeding steps involved a fermentation process, careful pouring of the wood foam liquid into the mold, and the application of baking, drying, and a biofilm coating to the component’s surface.

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Fig. 133. Column plan with vents and network.

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As we have seen in the preceding chapter that the porosity panels, a critical element of the system, first engage with the outdoor environment to capture the wind (A - Figure 132).

This captured wind is directed to the collection chamber situated directly behind the porosity panels (B - Figure 132). Subsequently, the wind from the collection chamber is conveyed to the convection loop through the tubular system (C - Figure 132). The tubular system is categorized into two types: one designed using biomimetic principles to determine the shortest path (C1 - Figure 132), and the second running parallel to the chimney, establishing a direct connection with the condenser. The wind from the collection chamber enters the tubular network system, created using a shortest path algorithm. This wind from the network is then directed to the tubular system running parallel to the chimney (C2 - Figure 132), from which it is subsequently pumped into the condenser. Simultaneosly the heated tubes on top of the mophology absorbs heat (D - Figure 132) from the enviorment to supply it to condensor for thermosiphon effect. This sequential process of wind capture, from porosity panels to condenser, is applicable throughout the system including the column and arch. All wind accumulated in the condenser is then conveyed to the chimney (E - Figure 132), facilitating its distribution to the vents (F - Figure 132) and ultimately integrating with the HVAC system of the building. (G- Figure 132)

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Fig. 134. Column body parts.

Now, further we will examine how these networks and vents were methodically developed to achieve optimal outcomes for flowing the wind effectively through the system.

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After comprehending the development of the porosity component and the evolution of morphology concerning wind dynamics, this section delve into a more detailed examination of the system. The column, a pivotal element within the entire passive ventilation system, serves as the conduit for channeling all captured wind from the system to the buildings through designated vents. These selfsupporting columns, affixed to the buildings, consist of multiple integral components that play distinct roles, initiating from the capture of wind through porosity panels, directing it to the convection loop via a tubular system, and ultimately supplying it to the building’s HVAC system through vents as seen in Figure 133.

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Upon entering the tubular network system, devised using a shortest path algorithm (A - Figure 132) for optimal efficiency in wind supply to the tubular system running parallel (B - Figure 132) to the chimney, the wind from the collection chamber is directed. The incorporation of the shortest path algorithm (A - Figure 132) ensures the most efficient route for wind supply to the tubular system parallel to the chimney (B - Figure 132), minimizing travel distances. Subsequently, the wind within the tubular system running parallel to the chimney is pumped to the condenser, facilitating the continued flow of wind throughout the entire system.

DESIGN DEVELOPMENT

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IN

Following the wind capture by the porosity panels and its transfer to the collection chamber, an efficient system is imperative to pump this wind to the convection loop, thereby facilitating passive ventilation for the building. Tubular systems were devised to fulfill this purpose, transporting the wind from the collection chamber to the condenser. These tubular systems are classified into two types: one formulated using biomimetic principles to ascertain the shortest path (A - Figure 132), and the second running parallel to the chimney (B - Figure 132), establishing a direct connection with the condenser.

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The wind sourced from diverse sections of the morphology is consolidated within the condenser before being directed to the chimney. This aggregated wind within the condenser is subsequently transported to the chimney, facilitating its dissemination to the vents and seamlessly integrating with the building’s HVAC system. In order to optimize the windflow from the chimney to the building’s existing HVAC system, volume curtailment measures were implemented (Figure 135).

These involved the introduction of chamfers and the creation of internal bubbles, strategically implemented to induce turbulence, thereby augmenting the wind speed. These vents of the passive ventilation system would replace the original source of the existing HVAC system of the building.

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This passive ventilation system, comprising diverse structural components, exhibits adaptability for implementation in diverse urban contexts worldwide, contingent upon climatic variables and specific site and contextual constraints.

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The research inquiry unfolded across two distinct phases, dedicated to developing a passive ventilation system for a new office tower in the MSC phase and a retrofitting endeavour for an existing office building within Milan’s urban fabric. This innovative system, explored with agro-based materials, addressed sustainability concerns by integrating biofilm to sequester carbon and reduce overall energy consumption. The investigation meticulously assessed the impact of urbanisation on energy use, focusing on Milan’s environmental challenges and the EU Green Building Pact.

Moreover, the research scrutinised the energy consumption of various buildings, identifying those with maximal energy consumption to decipher the system’s applicability in highusage scenarios.

CONCLUSION

The initially developed porosity panels, guided by abstracted bionic principles from termite mounds, lacked accuracy in morphing different component types. To address these limitations, a generative algorithm and CFD were employed for re-evolving porosity panels, aligning with criteria defined

by Universal Thermal Comfort Index (UTCI) values. The new panel design comprised Type-A components with high porosity for enhanced wind capture, Type-B with low porosity for increased turbulence, and Type-C as a connecting module between A and B for smoother transitions. Integration of these distinct types formed a regional-scale model subjected to comprehensive CFD simulations. The resulting data, mapped on XY axes, provided a nuanced understanding of wind capture efficacy and turbulence generation across different segments. While not immediately leveraged for panelling onto the morphology, this insightful data is valuable for future investigations into developing an efficient passive ventilation system.

In summary, our research has addressed key issues in sustainable architecture and building design by developing a passive ventilation system for existing office buildings in Milan’s urban fabric. However, further investigation is essential for post-analysis, particularly regarding the holistic approach needed for the individual abstracted principles of termite mounds. Additionally, exploring the convection loop’s potential to absorb ground heat during winter requires further analysis and evaluation as for the current investigation it has been only considered for summer months when the temperature is hot. The system’s efficiency warrants scrutiny at various levels, particularly considering the fabrication of the porosity component, which experienced cracking during the scaled model baking process. Evaluating different binding material ratios is crucial, and a detailed analysis of the regional-scale model’s wind capture efficiency is necessary for optimised penalization. Also, similar to the modularity of porosity panels it is crucial to consider the modularity and LCA (Life cycle assesment) for other parts of the morphologyas well like the parts of structural system, tubular network system while considering the transportation, assembly and its application at various sites of an urban fabric of Milan. A comprehensive Computational Fluid Dynamics (CFD) analysis of the morphology, considering wind intensity on the surface, can inform effective penalization. Furthermore, contextual limitations, such as existing façade types, opening sizes, and building HVAC system routes, need careful consideration for seamless integration with the passive ventilation system.

Further investigation of how it can be adapted to various scenarios which are outside Milan and are not similar to environmental conditions in Milan.

CONCLUSION

Material experimentation constituted the initial phase, exploring agro-based raw materials and binders in varied proportions to determine the optimal ratio. Subsequently, the identified optimal ratio was utilised to fabricate porosity panels in the second phase, which is crucial for developing the passive ventilation system. The fabrication process for 1/6th of the component was baking the mixture in a silicone mould. Notwithstanding the success, it is noteworthy that in certain regions, the accumulation of air bubbles gives rise to fissures that compromise the material’s structural integrity. (figure 105) Addressing this challenge in the manufacturing process is crucial for the project’s advancement. The urban fabric analysis focused on Milan’s air quality crisis, identifying areas with high PM 2.5 values and characterising optimal utilisation during regular office hours. The analysis also

Following the determination of optimal column numbers and corresponding vent sizes for each floor, the study delved into understanding the structural characteristics of these columns, which are crucial for inducing turbulence and optimizing wind capture in the passive ventilation system. Employing Kangaroo simulation methodology, a detailed morphological structure was intricately developed, specifically focusing on segment heights that dictated the domains for subsequent simulation phases. The experimentation involved testing different heights, a critical factor in morphological development, as elevated segments are known to enhance wind capture efficiency. Computational fluid dynamics (CFD) was integral at each stage of morphological development to extract the most efficient design. Insights gained from the MSC phase’s passive ventilation system development for the new office tower, emphasizing bulge and depression considerations, were incorporated into the M.Arch experimentation. The efficient structural system was also developed by considering the hierarchy of structural members. The sizes of these members were rationalized using efficient Finite Element Analysis (FEA).

The modularity of the same wasn’t considered for this part of research, but it can be explored in further investigations.

The porosity panels, integral system components, initiate the wind capture process by interacting with the outdoor environment. Captured wind is directed to the collection chamber situated behind the porosity panels. This wind is then transferred to the convection loop through the tubular system, which comprises two types: one designed with biomimetic principles for the shortest path and the other running parallel to the chimney, forming a direct link with the condenser. Wind from the collection chamber enters the shortest path of the tubular network system. It is subsequently directed to the tubular system running parallel to the chimney, ultimately being pumped into the condenser.

This sequential wind capture process is uniformly applicable throughout the system, including columns and arches. The condensed wind from the condenser is then conveyed to the chimney, facilitating its distribution to the vents and integrating with the building’s HVAC system.

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Drawing inspiration from biomimetic principles extracted from termite mounds, renowned for their effective passive ventilation systems, the study delved into solar radiation modulation; wind capture through depressions, convection loop mechanisms, and egress complexity for wind capture and turbulence. Case studies of Urban-Sequoia by SOM Architects and Melbourne City Council House-2 were instrumental in understanding architectural development requirements in urban settings for a future sustainable approach, contributing to reducing building energy consumption by enhancing spatial thermal comfort through air exchange considerations.

The research generated questions encompassing abstracted biomimetic principles, agro-based biomaterials, and applying living systems as alternatives to conventional building materials. Core tools employed throughout the research encompassed generative algorithms, Kangaroo physics simulation, finite element analysis (FEA), computational fluid dynamics (CFD), and shortest path algorithms. These tools were pivotal in facilitating the flow of air throughout the system, aligning with the central theme of the investigation.

revealed the importance of wind flow, prompting macro and micro-scale wind analyses to pinpoint suitable locations for system applications. In considering the thermal comfort parameters, particularly the Universal Thermal Comfort Index (UTCI), the research linked individual experiments, defining their criteria to optimise indoor and outdoor thermal comfort conditions. The orchestrated approach aimed to develop a holistic passive ventilation system for an urban fabric, elevating indoor and outdoor thermal comfort standards.

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16. Andréen, David, and Rupert Soar. 2023. “Termite-Inspired Metamaterials for Flow-Active Building Envelopes” 10 (May). https://doi.org/10.3389/fmats.2023.1126974.

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