Mach Graduate Architectural Design_Bartlett School of Architecture_University College London

Tutors: Alisa Andrasek, Jose Manuel Sanchez Student: Nicolò Friedman  

       

BIOmimetic-Fabric  Nature as a “recipe” for Generative Design      

       

Mach Graduate Architectural Design_Bartlett School of Architecture_University College London

Tutors: Alisa Andrasek, Jose Manuel Sanchez Student: Nicolò Friedman  

   BIOmimetic-Fabric Nature as a “recipe” for Generative Design 1// ABSTRACT

• ………………………………….……………………………………..…………….………...i

2// INTRODUCTION

• ……………………………………………………..……………………………………….p.1

3// THEORETICAL BASIS

• 1// The role of variation in evolution: ……………………………………………...…..…..……………..………………….…….p.2

• 2// Biology and computational embriology:

1a) Developmental Biology and Embriology…..…..……………..………………….…….p.4 2a) Computational embryology…………….…..…..……………..………………….…….p.6

3a) The Evolutionary Development System.…..…..……………..………………….…….p.7

• 3// Self-organization for Collective behavior: 1b) Complex systems……...………………….…..…..…………..………………….…….p.9

2b) Self-organization……………………….…..…..……………..……………...……….p.10 3b) Basic group behaviors……………………….…..…..………………………….…….p.11

• 4// Self-organization for patterns formation:

1c) Self-organization……...…..……………….……...…………..…………..…….…….p.13 2c) Reaction-diffusion in animal coat pattern………………...…..………………...…….p.17

4// THE RESEARCH • The concept………………………………………..………………………………..…….p.20 • Selected images…………………………………..……………………………………….p.21

 5// CONCLUSION

• ………………………………….……………………………………..…………….…….p.31

6// SELECTED REFERENCES • ………………………………….……………………………………..…………….…….p.32

     

Biomimetic Fabric  

i  

2//ABSTRACT Nowadays Generative design is becoming one of the most powerful processes to build up forms with an architectural aim. Actually, these fabrics have the ability to be extremely versatile and able to adapt according to external demands of the environment. One the most fascinating aspect of this method is represented by its theoretical bases which take their inspiration from biological processes related to the ability of generate forms. The unrelenting rise of this theory has been possible thanks to the research concerning Computation and the introduction of self-organization concept and complex systems theory. This paper briefly analyzes some of these theoretical aspects and provides a practical example of application through some images of the Research1 which demonstrate how to set up a design project within this territory. After describing the role of variation in evolution, three main concepts are introduced: computational embryology, collective behavior and patterns formation through self-organized systems. All these “ingredients” are accurately mixed in order to compose a “recipe” for designing adding imagination and speculative ability to push the system for aesthetic and functional expression.  

                                                                                                               1  " The research project referred to in this report is the collective effort of Team Variance, a research group comprised of the following four MArch Graduate Architectural Design students: Vincenzo D'Auria, Nicolo Friedman, Mark Geoffrey Muscat and Pallavi Sharma. Each member's report deals with a different aspect of the research project. "  

Biomimetic Fabric

  1

2//INTRODUCTION Biology, considered as a set of configurations to generate living forms, has rapidly grown to be a crucial part of the theory behind generative process both in engineering and architectural practice. This phenomenon is mainly due to two different reasons, the availability of a great number of inexpensive simulation software and the introduction of complex systems theory -and of the science of complexity into the design theory and practice. After a brief description of the role of variation in evolution (necessary to introduce the meaning of Evolvability and Selection as key concepts in the generation of forms or organisms), this paper will go through some of the fundamental theoretical basis related to this territory. Firstly, the concept of computational embryology is introduced focusing on his close relationship with developmental biology and providing a practical example of the construction of the Evolutionary Development System (EDS), an object-oriented model referring to these natural processes. Looking at embryology as a reference means understanding the process of growth and the evolution of the zygote, the way in which different components can act together for ending in an high-specialized form. Secondly, a concise definition of complex systems and self-organization helps us to introduce the theory of collective behavior and patterns formation. While collective behavior means dealing with agents (the basic components of complex systems), which have the ability to produce global behavior starting from local neighborhood interactions, animal coat patterns are scientifically explained through Reaction-Diffusion system. This system, which was firstly investigated by Alan Turing, starting from a mixture of two chemical compounds, can generate a huge catalogue of different patterns. Concluding, some meaningful images of the Research1 are included to demonstrate how these theoretical bases can be used as a source to generate design in the field of Architecture. In fact, for instance, agency logic allows to create high-resolution patterns that change locally conformation depending on the global behavior of agents and the versatility of the Reaction-Diffusion algorithm provides an intricate distribution of matter in an organic form.

                                                                                                               1 " The research project referred to in this report is the collective effort of Team Variance, a research group comprised of the following four MArch Graduate Architectural Design students: Vincenzo D'Auria, Nicolo Friedman, Mark Geoffrey Muscat and Pallavi Sharma. Each member's report deals with a different aspect of the research project. "  

Biomimetic Fabric

  2

3//THEORETICAL BASIS 1// The role of variation in evolution In order to completely understand evolution, we have to focus our attention on the process of generating structure. Nowadays research related to modern biology is mainly concerned with the question of how form is generated and how important is evolution during this phenomenon. Darwin’s theory2 clearly shows the idea of variability of organisms seconded by their own fitness during each generation but do not answer the question of how these organisms achieve their genetic variation. On the other hand, biologists of nineteenth century focused their research in this process. Going deeply in the the theory of evolution we need to think about a group of individuals, better defined as population, which change their traits. Some of these traits, called dominant, are more suitable to environmental conditions and inevitably affect the next generation. For this reason the theory of evolution is closely linked to the concept of natural selection which forces each individual to be in competition with others. The dominant traits, generation after generation, lead to a gradual mutation and are always connected to a need. For instance, the giraffe’s neck is stretched to allow the animal to eat higher leaves. Another important aspect in the evolutionary process is represented by the concept of evolvability. Evolvability is the ability of a population of organisms to generate adaptivegenetic diversity and it has to have three characteristics. Firstly, the possibilities of variation are relatively limited, secondly, the fallacy of variation is suppressed because it does not contribute to evolution and finally the idea of provision of useful variation. Concluding, we realize that the evolutionary process can not be considered random although the perturbation and the mutation of the system is completely random. Selection becomes the main factor that affects evolution. Wanting to create a connection between evolutionary novelty and human design, it is clearly shown that the interest is not defined in a geometric form, but in the characteristics of the process that is regulated by constraints and deconstraints. “ …These are constraints, because any change in them will be lethal. They are also deconstraints, because they allow a generation of novelty. This is something we see in other form of human behaviour, in art and architecture, and in social organization. Evolvability itself has evolved...”3                                                                                                                2  Darwin’s theory is based on three fundamental principles: the theory of mutation, which could also be called theory of inheritance, the theory of variation and the theory of selection.  3  Mark Kirschner, “Variations in Evolutionary Biology”, Lars Spuybroek ,The Architecture of Variation, (Thames & Hudson, 2009), pp. 32-33.

Biomimetic Fabric

  3

(a) (a) Poodles AKC/UKC approved Puppy Clip4

                                                                                                               4  Lars Spuybroek, The Architecture of Variation, (Thames & Hudson, 2009), pp. 76-83.  

Biomimetic Fabric

  4

2// Biology and computational embryology 1a) Developmental Biology and Embryology

“…Life is clearly the most complex of all designs to have evolved, making our very best evolved designs look absurd in their simplicity …”

- Bentley, P. J. & Kumar, S.

Three Ways to Grow Designs5 – Nowadays Developmental Biology and in particular, Embryology is one of the most exiting subject for research and a substantial reference for Evolutionary Computation. Today we consider Embryology the study of formation and development of animal and plant embryos. The role of development is the key to understanding the actions that lead from egg to embryo to adult. Three fundamental processes are involved: morphogenesis 6 , regional specification 7 , cellular differentiation8. These actions collaborate in certain areas of the embryo at different times and during specific phases in according to a “recipe” known as an embryogeny9. In nature, development begins with a single cell: the fertilized egg, or zygote. In addition to receiving genetic material from its own parents, the zygote is seeded with a set of proteins – the so-called “material factors” deposited in the egg by the mother (Wolpert, 1998). After that the material factors force the zygote to cleave10, cells begin to divide and differentiate in order to develop a not homogenous embryo. DNA controls the development and proteins express or repress genes through signals within cells and other nearby cells in order to standardize the complex processes of cellular differentiation, patter formation, morphogenesis and growth.                                                                                                                5  (Bentley, P. J. & Kumar, S. (1999). Three Ways to Grow Designs: A Comparison of Embryogenies for an Evolutionary Design Problem. In Genetic and Evolutionary Computation Conference (GECCO) Orlando, Florida, USA., introduction, p.1)  6 Morphogenesis – which involves the emergence and change of form (Bard, 1990). (Sanjeev Kumar , Peter J. Bentley, (2003), Computational embryology: past, present and future ) 7 regional specification ( pattern formation ) – in which compartmentalization of the embryo into specific regions occurs (Slack, 1991). (Sanjeev Kumar , Peter J. Bentley, (2003), Computational embryology: past, present and future ) 8 cellular differentiation – in which cells become specialized for particular functions (Wolpert, 1998). (Sanjeev Kumar , Peter J. Bentley, (2003), Computational embryology: past, present and future ) 9 An embryogeny is the process of growth that defines how a genotype is mapped onto a phenotype. (Bentley, P. J. & Kumar, S. (1999). Three Ways to Grow Designs: A Comparison of Embryogenies for an Evolutionary Design Problem. In Genetic and Evolutionary Computation Conference (GECCO) Orlando, Florida, USA.) Genotype: the entire genetic constitution of an individual; Phenotype: the observable physical or biochemical characteristics of an organism, as determined by both genetic makeup and environmental influences; (“Medical Dictionary”, http://medical-dictionary.thefreedictionary.com, (accessed 2 June 2012) ) 10 cleave (fast cell division with no growth) (Sanjeev Kumar , Peter J. Bentley, Biologically Inspired Evolutionary Development, p.2)  

Biomimetic Fabric

  5

Gallus gallus. “The Chicken embryo is a staple educational tool in developmental biology. Their availability and similarities with mammalian embryo, help shape our present understanding of embryology. After 21 days of incubation, the chick attempts to break out of its shell, pushing its beak through the air cell. Since the specimens were received out of the egg and without its yoke, I lacked the ability to document the chicken’s interaction in its element. The specimens document a range from 5, 6, 9, 12, to 18 days of development11”.

                                                                                                               11  “Chicken  embryo  –  Microscopy  UK”,  http://www.microscopy-uk.org.uk/mag/artnov04macro/mlchicken.html (accessed 22 April 2012)  

Biomimetic Fabric

  6

2a) Computational embryology

“…Nature has been successfully evolving complex animals for millions of years. It is the concept of an embryogeny (which itself evolved in nature)

that has allowed the evolution of these complex design…”

- Bentley, P. J. & Kumar, S. Computational Embryology12 –

Evolutionary computation (EC), actualized through several types of evolutionary algorithms inspired from nature, is one of the most successful area of computer science. The possibility to investigate these territories was materialized for the first time by Alan Turing in 1952 with his studies related to morphogenesis. However, The first studies used crude approximations of the natural embryological processes ignoring important factors such as Regional specification. Current computational embryology can be divided into three different type: external, explicit and implicit. Briefly, while external embryogenies are defined globally and externally to genotypes, explicit models specify each step of the growth process in the form of explicit instructions13. Lastly, in an implicit embryogeny the growth process is implicitly specified by a set of rules similar to a “recipe” that govern the growth of a shape. Looking in detail at the four major types of evolutionary algorithm (EA),[the genetic algorithm (GA), evolutionary programming (EP), evolutionary strategies (ES) and genetic programming (GP)] the genetic algorithm (GA) can be considered the most accurate. In fact the GA is the only one able to deal separately genotype from phenotype, using a mapping stage from the beginning of its development. Figure a: Schematic illustrating a basic concept in genetic algorithms - that there are two components to a representation: the phenotype and the genotype. The genotype is an encoded set of parameters that determine attributes in the phenotype. The phenotype is evaluated by an objective fitness function or a human evaluator, and the genotype is affected by this evaluation, through the operators of the genetic algorithm14

                                                                                                               12  (Sanjeev Kumar , Peter J. Bentley, (2003), Computational embryology: past, present and future, conclusions )  13  (Sanjeev Kumar , Peter J. Bentley, (2003), Computational embryology: past, present and future, computational embryology )  14  “Background  and  Related  Work”,  http://www.ventrella.com/Alife/Thesis/background.html, (accessed 15 April 2012)  

Biomimetic Fabric

  7

3a) The Evolutionary Development System After having defined the relationship between Developmental Biology and Computational Embryology, i present an overview of a plausible model of development for evolutionary design very close to biological growth in order to discover the key components and their potential for computer science. The Evolutionary Development System (EDS) is an object-oriented model comprising individual characters of these biological processes within a computer model. Genes, proteins and cells can be considered the basic ingredients of the system and the embryo is composed by a collection of cells and proteins contained by them.

Figure b: A single cell in the EDS15                                                                                                                15  (Sanjeev Kumar , Peter J. Bentley, Biologically Inspired Evolutionary Development, p.3)  

Biomimetic Fabric

  8

In the process of growth, proteins may be regarded as the engine for the development. The EDS treats the proteins as objects and gives them an ID tag (integer number). Moreover, proteins do not exist in isolation but are linked to cells and have specific spatial coordinates. In addition, protein molecules diffuse. Diffusion is the process by which molecules spread or wander due to thermal motions (Alberts et al., 1994)16. It represents an efficient method for molecules to move short distances, but an inefficient method to move over large distances. Generally, small molecules move faster than large molecules (Alberts et al., 1994)17. Two genomes are employed by the system. The first contains protein specific values, the second describes the architecture of the genome to be used for development. In embryology, the role of genes is twofold: they comprise the cis-regulatory region18 ( Davidson, 2001) and the coding region19. The EDS considers a “one gene, one protein” simplification rule and the activation of a single gene results in the transcription of a single protein. Finally, cells are the the last ingredient of the “recipe”. They are viewed as agents performing different behaviors ( multiply, differentiate, and die). In addition, the cell membrane plays an important role as it becomes a receptor of the presence of certain molecules within the environment. To develop the growth of the model a genetic algorithm (GA) is used. The algorithm provides genotype for development and a task of function, Individuals within the population of the genetic algorithm comprise a genotype, a phenotype and a fitness score. After the population is created, each individual has its fitness assessed through the process of development and is permitted to execute its program according to the instructions in the genome20.                                                                                                                16  (Sanjeev Kumar , Peter J. Bentley, Biologically Inspired Evolutionary Development, p.5)  17  (Sanjeev Kumar , Peter J. Bentley, Biologically Inspired Evolutionary Development, p.5)  18  Cis-regulatory regions are located just before their associated coding regions and effectively serve as switches that integrate signals received from both the extra-cellular environment and the cytoplasm. (Sanjeev Kumar , Peter J. Bentley, Biologically Inspired Evolutionary Development, p.6)  19  Coding regions specify a protein to be transcribed upon successful occupation of the cis-regulatory region by assembling transcription machinery. (Sanjeev Kumar , Peter J. Bentley, Biologically Inspired Evolutionary Development, p.6)  20  (Sanjeev Kumar , Peter J. Bentley, Biologically Inspired Evolutionary Development, p.8)  

Biomimetic Fabric

  9

3// Self-organization for Collective behavior 1b) Complex Systems

“…Simple and complex systems exhibit… the spontaneous emergence of order, the occurrence of self-organization…”

- S. A. Kauffman, The Origins of Order:

Self-Organization and Selection in Evolution – Complex systems have emergent properties and their behaviors are unpredictable and uncontrollable. On the other hand, they are characterized by an irreversible evolution, by an “arrow of time” that points unambiguously from the past to the future, and that allows no turning back (Prigogine & Stengers, 1984). Looking at their multiple positive features, i can underline adaptivity, autonomy, robustness and other aspects all related to the process of self-organization. Moreover, these systems spontaneously organize themselves to cope external and internal stresses and, after evolving, they become more complex, “mind-like” and less “matter-like”21. Thus, the arrow of time tends to point towards an improved, better organized or more adapted version of the evolving system (Stewart, 2000).22 The components of complex systems are called agents. Generally, agents individually follow a simple cause-and-effect or condition-action logic but as the same time are affected by other agents’ activities. Although these interactions are initially local and only related to neighborhood conditions, consequently they become global and affect the system as a whole. Another important aspect linked to agency logic is that their interactions are very sensitive to initial condition. A small and undetectable initial change may generate a drastically altered outcome23. This means that even though the dynamic of the system is deterministic, the result is often unpredictable.

                                                                                                               21  In the philosophy of dualism the world is seen to be made out of two substances: matter and mind. In complex systems these two features are different aspects of the same phenomenon of organization. (Heylighen F., (2011), Self-organization in Communicating Groups: the emergence of coordination, shared references and collective intelligence, Vrije Universiteit Brussel, p.2)  22  Heylighen F., (2011), Self-organization in Communicating Groups: the emergence of coordination, shared references and collective intelligence, Vrije Universiteit Brussel, p.2 23  This phenomenon is called “butterfly effect”: after the observation that, because of the non-linearity of the system of equations governing the weather, the flapping of the wings of a butterfly in Tokyo may cause a hurricane in New York. (Heylighen F., (2011), Self-organization in Communicating Groups: the emergence of coordination, shared references and collective intelligence, Vrije Universiteit Brussel, p.3)  

Biomimetic Fabric

  10

2b) Self-organization Nowadays the notion of self-organization has substantial applications in computer science because it is seen as a solid reference for designing systems without centralized control. Finally, self-organization can explain previously mysterious phenomena linked to complex structures generated by basic interactions between components. A self-organizing system may be characterized by global, coordinated activity arising spontaneously from local interactions between the system’s components or “agents”. 24 This phenomenon affects all components of the system without the need of a central supervisor or director of the global behavior. I am referring to simple interactions at local levels that generate complex solutions at the global level.25 Organization can be considered as a structure with function, in fact agents of the system work together in order to reach a common goal building a structure given by their general behavior. To clarify this concept i need to introduce the notion of coordination (Crowston et al., 2006)26. The key point is the collective work and the way in which agents can globally behave as a single element.27 Moreover, agents can find solutions working together, the same solutions that would not be able to find individually. This phenomenon is called synergy (Corning, 1998; Heylighen, 2007, 2008). “Coordination can be defined as: the structuring of actions in time and (social) space so as to minimize friction and maximize synergy between these actions.”28

                                                                                                               24  Heylighen F., (2011), Self-organization in Communicating Groups: the emergence of coordination, shared references and collective intelligence, Vrije Universiteit Brussel, p.3  25  This phenomenon is called emergence  26  Heylighen F., (2011), Self-organization in Communicating Groups: the emergence of coordination, shared references and collective intelligence, Vrije Universiteit Brussel, p.5  27  This is what Heylighen has called teh avoidance of friction. (Heylighen F., (2011), Self-organization in Communicating Groups: the emergence of coordination, shared references and collective intelligence, Vrije Universiteit Brussel, p.5)  28  Heylighen F., (2011), Self-organization in Communicating Groups: the emergence of coordination, shared references and collective intelligence, Vrije Universiteit Brussel, p.5  

Biomimetic Fabric

  11

3b) Basic group behaviors Coordination can be divided in four basic behaviors: alignment, division of labor, workflow and aggregation. Alignment is the simplest group action: it means that agents aim the same goal or point the same final target. Looking into a group of agents, the more are already aligned, the larger the force in the direction of their alignment, the more difficult it will be for others to oppose that movement, but easier it will be for them to join in with that movement. Moreover, if agents start in an extended region of space, it could happen that the agents of one region start to align on one direction, while those in another region align on a different direction.29 This phenomenon creates local homogeneity but global heterogeneity. Since agents align with the neighbors they have the strongest interactions with, the borders between the regions will be where the initial interactions are weakest.

Figure c: global alignment of directions of action, from random (left) to homogeneous (right).30

Figure d: local alignment of directions of action, from random (left) to locally homogeneous, but grobally heterogeneous (right).31

                                                                                                               29  Heylighen F., Self-organization in Communicating Groups: the emergence of coordination, shared references and collective intelligence, Vrije Universiteit Brussel, p.6  30  Heylighen F., Self-organization in Communicating Groups: the emergence of coordination, shared references and collective intelligence, Vrije Universiteit Brussel, p.6  31  Heylighen F., Self-organization in Communicating Groups: the emergence of coordination, shared references and collective intelligence, Vrije Universiteit Brussel, p.6  

Biomimetic Fabric

  12

Division of labor, instead, is related to the possibility of agents to perform different actions, specialized in what they do better. Therefore, since agents have reduced abilities, the whole system has the ability to compensate the lack of individuals. Referring to self-organizing logic, agents prefer to do actions they are most skilled at, so they will pick up these tasks, leaving less fitting ones to the others. Thus, the number of remaining tasks will gradually diminish.32 While division of labor coordinates activities that happen in parallel, Workflow (van der Aalst & van Hee, 2004) coordinates activities sequentially. The mechanism is related to the idea that an agent, after finishing the task he is most skilled at, will look around to find another task that may fit its profile and will perform it or will finish to perform another agent’s task. This kind of behavior can be found in animal collaboration: social insect, such as ants and termites, perform sequentially and in parallel complex activities. Finally, to understand the benefits of synergic action, aggregation (Surowiecky, 2005) has to be introduced. It is a parallel process: different streams of activity come together simultaneously.33A practical example can be found in the organization of ant societies. In fact, army ants leave a trace of pheromones to remember where to find food. Using this logic, after a while, ants create a network of pheromone trails connecting their nest to all the surrounding food sources.                                                                                                                32  Heylighen F., Self-organization in Communicating Groups: the emergence of coordination, shared references and collective intelligence, Vrije Universiteit Brussel, p.8  33  Heylighen F., Self-organization in Communicating Groups: the emergence of coordination, shared references and collective intelligence, Vrije Universiteit Brussel, p.9  

Biomimetic Fabric

  13

4// Self-organization for patterns formation 1c) Self-organization

“Technological systems become organized by commands from outside, as when human intentions lead to the building

of structures or machines. But many natural systems become structured by their own internal processes: these are

the self-organizing systems, and the emergence of order within them is a complex phenomenon that intrigues

scientists from all disciplines.”

- F. E. Yates et al., Self-Organizing System: The Emergence of Order –

Self-organizing systems can be found both in the field of biology and in physics. These systems, based on pattern-formation processes, have the power to achieve their own order and their structure through internal interactions and not by the intervention of external directives. Some examples can be found in the aggregation of sand grains assembled in dunes and fish moving together in schools. In order to clarify the relationship of these systems with the formation of patterns in biology, i quote the following definition:“Self-organization is a process in which pattern at the global level of a system emerges solely from numerous interactions among the lower-level components of the system. Moreover, the rules specifying interactions among the system’s components are executed using only local information, without reference to the global pattern.”34 Another important aspect to underline is the meaning of pattern. Related to this research, pattern is a group of items organized in a given space according to a time. These patterns can be built by living units or inanimate objects.35 However, in the both cases subjects are able to build pattens without external orders and influences but using local information. Each element relates his behavior to his nearest neighbors without knowing absolutely the global behavior. Moreover, these elements use simple behavioral rules in a local level in order to build complex patterns in global level. Therefore systems are defined complex because of their global result. Choosing to focus on biological systems, it is clear that the mechanism is more complex than the process which involves physical systems. Firstly, the subunits are living elements so the interactions between them are more complex. Secondly, biological systems have to submit the law of physics as well as their own genetic properties. This dual aspect drives systems to specific and stronger interactions.

                                                                                                               34  What is Self-Organization?, Part I, S. Camazine J. L. Deneubourg N. R. Franks J. Sneyd G. Theraulaz E. Bonabeau, Self-Organization in Biological System, (Princeton University Press, 2001), p.8.  35  Examples of living units are animals as fish, ants or cells while examples of inanimate objects are bits of dirt or sand grains.

Biomimetic Fabric

  14

(b)

(c) (b) skin pigmentation on fish (clockwise from top – vermiculated rabbitfish (Siganus vermiculatus), male boxfish (Ostracion solorensis), and surgeonfish (Acanthurus lineatus)); (c) zebra and giraffe coat patterns;36

                                                                                                               36  S. Camazine J. L. Deneubourg N. R. Franks J. Sneyd G. Theraulaz E. Bonabeau, Self-Organization in Biological System, (Princeton University Press, 2001), p.10.  

Biomimetic Fabric

  15

(d) (e)

(f) (g)

(h) (i) Self-organized pattern formation in physical and chemical systems. (d) Wind-blown ripples on the surface of a sand dune. (e) Spiral waves produced by the Belousov-Zhabotinski chemical reaction. (f) Pattern of cracks produced by muda s it dries and shrinks along the shore of a pond. (g) Hexagonal pattern of Bènard convection cells created when a thin sheet of viscous oil is heated uniformly from below. A small amount of aluminum powder has been added to the oil to reveal the pattern of convection. (h) Polygonal pattern of cracks on wooden surface. (i) Wrinkle pattern formed by a coat of varnish on a wooden surface.37

                                                                                                               37  S. Camazine J. L. Deneubourg N. R. Franks J. Sneyd G. Theraulaz E. Bonabeau, Self-Organization in Biological System, (Princeton University Press, 2001), Plate 1.  

Biomimetic Fabric

  16

(l) (m)

(n) (o)

(p) (q)

Animal coat patterns and insect coloration belived to involve self-organized pattern formation. (l) Zebra, Equus grevii. (m) Giraffe, Giraffa sp. (n) Tiger, Felis tigris. (o) Gila monster, Heloderma suspectum. (p) Rice paper or tree nymph butterfly, Idea leuconoe. (q) Locust borer beetle, Megacyllene robiniae.38

                                                                                                               38  S. Camazine J. L. Deneubourg N. R. Franks J. Sneyd G. Theraulaz E. Bonabeau, Self-Organization in Biological System, (Princeton University Press, 2001), Plate 2.  

Biomimetic Fabric

  17

2c) Reaction-diffusion in animal coat patterns

“… and after long time, what with standing half in the shade and half out of it, and what with the slipperry-slidy shadows of the trees

falling on them, the Giraffe grew blotchy, and the Zebra grew striply, and the Eland and the Koodoo grew darker, with little wavy grey lines on their backs like bark on a tree trunk; and so, though you could hear

them and smell them, you could very seldom see them, and then only when you knew precisely where to look.”

- Rudyard Kipling

The Just So Stories –

Animal coat patterns are extremely important for two reasons. Firstly, patterns help animals to blend with the surrounding environment: for this reason prey are less visible by predators and predators can hide better and have more opportunity to attack their prey. Secondly, animals of the same species are able to recognize other members easily because they have got the equal patterns. These two reasons are correct but we need something more. We want to understand how patterns are formed and why they reach a final configuration over another. The first attempt to answer can be found in the research of Alan Turing, a British mathematician who published in 1952 a paper entitled “The chemical basis of morphogenesis”. Specifically, his assay brings out the idea of a reaction-diffusion system which can generate symmetry breaking, leading to stable spatial patterns, in an initially uniform mixture of chemical compounds. 39 Moreover, Turing realized that manipulating the chemical concentrations of the systems, the final patterns can considerably change. The mechanism is based on the relationship between activation by compound A and inhibition by compound B. This process can generate patterns of stripes or spots. One of the most important applications of the reaction-diffusion system is found in animal coat patterns. The strength of the process is demonstrated by the fact that, using the same mechanism, the system can reach extremely different results. Different patterns can be generated by changing the reaction conditions. Another fundamental constraint in the growth of patterns is represented by the size and the shape of the region concerned. Looking at animal tails as an example of study, we can easily understand how the size affects the formation of patterns: in small tails only bands are formed while in bigger ones more complex systems are produced. Concluding, we underline the role of the size and shape of the embryo at the time of pre-patterning. One implication of this is that small animals with short gestation periods should have less complex pelt patterns than larger animals, because their smaller embryos support fewer modes.40                                                                                                                39  Philip Ball, The self-made tapestry, (Oxford University Press, 1999) p. 79.  40  Philip Ball, The self-made tapestry, (Oxford University Press, 1999) p. 88.  

Biomimetic Fabric

  18

(r) How an activator-inhibitor scheme works. The activator generates more of itself by autocatalysis, and also activates the inhibitor. The inhibitor disrupts the autocatalytic formation of the activator. Meanwhile, the two substances diffuse through the system at different rates, with the inhibitor migrating faster.41

(r) 1 2 3

(s) The patterns produced on tapering cylindrical “model tail” by an activator-inhibitor scheme depends on their size and shape. Small cylinders support only bands (stripes) (1), whereas spots appear on larger cylinders (2) as they widen. On a more slowly tapering tail (3), the transition from bands to spots is more clear.42

(s) 4 5 6 7 8 (t) (t) The adult zebra Equus grevyi (5) has more and narrower stripes than the adult Equus burchelli (4). This is thought to be because the striped “ pre-pattern” is laid down on the embryo of the latter at an earlier stage: after 21 days for Equus burchelli (6), but after 5 week for Equus grevyi (8). The smaller embryo supports fewer stripes, and so by the time it is of comparable size (7), its stripes are wider.43                                                                                                                41  Philip Ball, The self-made tapestry, (Oxford University Press, 1999) p. 80.  42  Philip Ball, The self-made tapestry, (Oxford University Press, 1999) p. 86.  43  Philip Ball, The self-made tapestry, (Oxford University Press, 1999) p. 87.  

Biomimetic Fabric

  19

4// THE RESEARCH group name //VARIANCE; tutors: Alisa Andrasek, Jose Manuel Sanchez; students: Mark Muscat, Nicolò Friedman, Pallavi Sharma, Vincenzo D’Aura.

2D pattern produced by simple flocking of agents – 2D Agency Logic

Biomimetic Fabric

  20

The concept The research topic focuses round the idea of building a three dimensional mutating fabric formulated of matter that changes its density and porosity in response to specific conditions. The concept is closely related to a continuum of responsive matter which adopts highly specialized behavior in localized scales. After analyzing microscope images of human bones (kindly given to us by students at the UCL Department of Surgery), we got inspired by their high porosity structure, strength and flexibility. Moreover, we also investigated bone remodeling; a lifelong process where mature bone tissue is removed from the skeleton and new one is formed. Hence we are aiming at achieving a building fabric that has a high response to the external demands of the surrounding environment and an intrinsic ability to adapt locally. Bones are also relevant in their ability to work together with other tissues (tendons and muscles) to form a single proficient system. The human body is a powerful machine composed of several elements which simultaneously demonstrate independent and collaborative behavior. Our fabric responds to ecological pressures, created by using an interrelated synthesis of local and global behaviors. A new kind of tectonic language could be built using deposition techniques and generative methods of topological formation. We decided to focus our attention on the constructability of the shapes generated by establishing a design approach of performative fabrics where form, material and structure are closely related. Voxel-based computational agency logic supports the search for a form with clear state conditions using a discrete logic. After defining a possible material organization, the fabric, within its determined boundaries, is refined to adapt to various environmental pressures around it. The final goal is to create an architectural fabric using these generative approaches which will produce a high-resolution output (refer to original case studies) on a human-scale. Responsive material technologies give computation a new dimension with a rich array of material affordances and behaviors, which may ultimately be materialized through Additive Manufacturing techniques.

Biomimetic Fabric

  21

Selected images Starting references:

microscope image of human bones44 Bio-material: Collagen45 Deposition tecniques:

Left: Overview of a 3D structure covered with a thin metal layer. Right: Detail of the structure’s corner46

                                                                                                               44 Image kindly provided by Medical School UCL, Division of Surgery (15 October 2012). 45 “Collagen Fibrils - FEI”, http://www.fei.com/resources/image-gallery/knee-joint-capsule-7329.aspx (accessed 06 March 2012) 46 cropped image of “New 3D metal deposition technique for metamaterials fabrication – DTU fotonik”, http://www.fysik-nano.fotonik.dtu.dk/Projekter%20F2011/Fagprojektbeskrivelser/(accessed 06 March 2012)

Biomimetic Fabric

  22

Reaction-Diffusion:

Reaction Diffusion 47

Reaction-diffusion 2d catalogues. Different configuration of patterns related to different values of F, K, dU and dV.

                                                                                                               47  manipulated  image  of    “  Variable  resolution  Reaction  Diffusion  -­‐    flight  404  ”,  http://www.flickriver.com/photos/flight404/sets/72157623905785665/ (accessed 06 June 2012)  

Biomimetic Fabric

  23

3d color-based Reaction-Diffusion model:

Biomimetic Fabric

  24

2D Agency Logic:

“ high level of expression through the intricacy of the patterns produced by collective behavior of agents”

Biomimetic Fabric

  25

functionally graded material: Agents have the ability to create different patterns.The patterns denote by their own composition the materiallity they are made of. TWISTED and INTRICATE patterns have a natural STABILITY. FIBROUS patterns have a clear ELASTICITY AND FLEXIBILITY. Materiality, laser sintered experiments: A serious of “test” prints were created on the SLS machines at the Bartlett to determine material limits and printer capabilities. The output fabric is completely dependenton the machine by which it will be constructed.

Biomimetic Fabric

  26

Makerbot replicator:

From a digital word to reality: the power of the machine to reproduce the intricacy of the digital model.

Biomimetic Fabric

  27

Biomimetic Fabric

  28

3D Agency Logic:

Biomimetic Fabric

  29

Biomimetic Fabric

  30

Biomimetic Fabric

  31

5// CONCLUSION “Of course our motivations in computer science are often very different from the motivations of biologists. Nevertheless, it has long been the goal of evolutionary computationists to evolve complex solutions to problems without needing to program in most of the solutions first. The dream of complex technology that can design itself requires rejection of the idea of knowledge-rich systems where human designers dictate what should and should not possible. In their place we need systems capable of building up complexity from a set of low-level components. Such systems need to be able to learn and adapt in order to discover the most effective ways of assembling components into novel solutions. And this is exactly what development processes in biology do, to great effect.”48 The aim of the paper is to underline the possibility to look at “Nature” as a source for a new design approach. The point is to look at these biological processes as “recipes” for generate certain forms or fabrics with specific characteristics. Although today computational science is able to create quite accurate models that emulate the growth of the embryo or systems that mimic the pattern formation of the mantle of some animals, the key point is the ability to use this tools for designing. In fact, the process of generative design can be divided into two principal steps: while the first part is related to build up systems with high adaptive capacity using simple initial variables the second one deals with the ability of the designer to speculate a possible scenario in an architectural territory. This is the most difficult challenge since the designer should be able to push the system in order to find aesthetic and functional expression without ending up in a design project that expresses only high technicality. The research part provides a practical example of how to use these theoretical bases as the initial ground to build up a coherent design project, without forgetting the imaginative capacity, essential quality that an architect must possess. The computational models, if driven in the rigt way, have got the superpower to generate fabrics with an high adaptability and a self-intelligence in according to the environment. Complex systems are generated using simple initial rules between the components and this is exactly what happens in Nature.                                                                                                                48  (Sanjeev Kumar , Peter J. Bentley, Biologically Inspired Evolutionary Development, p.1)  

Biomimetic Fabric

  32

6// SELECTED REFERENCES _Brian Goodwin, How the Leopard Changed its Spots: The Evolution of Complexity, ( a Phoenix Paperback 1994). _Heylighen F., Bollen J & Riegler A., The Evolution of Complexity (Kluwer Academic, Dordrecht, ed. 1999). _John H. Holland, Emergence from chaos to order, (Oxford University Press 1998). _Philip Ball, The self-made tapestry, (Oxford University Press, 1999). _Sanford Kwinter, African Genesis, in Assemblage36, (MIT Press 1998). _Sanford Kwinter, ‘Soft Systems’, Culture Lab 1, Brian Boigon ed., (Princeton, 1993). _S. Camazine J. L. Deneubourg N. R. Franks J. Sneyd G. Theraulaz E. Bonabeau, Self-Organization in Biological System, (Princeton University Press, 2001). _Bentley, P. J. & Kumar, S. Biologically Inspired Evolutionary Development _Bentley, P. J. & Kumar, S. (2003), Computational embryology: past, present and future _Bentley, P. J. & Kumar, S. (1999). Three Ways to Grow Designs: A Comparison of Embryogenies for an Evolutionary Design Problem. In Genetic and Evolutionary Computation Conference (GECCO) Orlando, Florida, USA. _Heylighen F. (2005): "Conceptions of a Global Brain: an historical review", , Technological Forecasting and Social Change[in press ] _Heylighen F. (2011) Self-organization of complex, intelligent systems: an action ontology for transdisciplinary integration, Integral Review (in press) _Heylighen F., (2011), Self-organization in Communicating Groups: the emergence of coordination, shared references and collective intelligence, Vrije Universiteit Brussel _Heylighen F. (2010) The Self-organization of Time and Causality: steps towards understanding the ultimate origin, Foundations of Science, 15(4), 345-356. (doi:10.1007/s10699-010-9171-1) _Architectural Design, Versatility and Vicissitude Performance in Morpho-Ecological Design Guest-edited by Michael Hensel and Achim Menges, March/April 2008.

Biomimetic Fabric

  33

“Background and Related Work”, http://www.ventrella.com/Alife/Thesis/background.html, (accessed 15 April 2012) “Chicken embryo – Microscopy UK”, http://www.microscopy-uk.org.uk/mag/artnov04macro/mlchicken.html (accessed 22 April 2012) “Medical Dictionary”, http://medical-dictionary.thefreedictionary.com, (accessed 2 June 2012) Front page image_ cropped image from “wildencounters”, http://www.wildencounters.net/weblog/, (accessed 21 April 2012). “Lesser Flamingos grouped together on shallow-water mud and silt flats, in the shape of a devil's tail, aerial shot, Lake Natron, Tanzania (aerial shot)