European Network of Heads of Schools of Architecture European Association for Architectural Education
Architectural Education and the Reality of the Ideal: Environmental design for innovation in the post-crisis world
Transactions on Architectural Education No 61 Editor Maria Voyatzaki
Cover Image and Logo Design: Emmanouil Zaroukas Layout design: Dimitris Apostolidis Printed by: Charis Ltd, Thessaloniki, Greece
ISBN 978 2 930301 60 0 Copyright Š 2013 by the authors and the EAAE
All rights reserved. No part of this book may be reproduced in any form, by print, photoprint, microfilm or by any other means without written permission from the publisher. Despite the fact that the editor proof read the texts, authors are responsible for the English of their contributions.
Theme 3 Computation: Environment and Architectural Innovation Two prominent streams in architectural education currently are computational design and environmental design, streams which are followed almost entirely independently. This theme examines the possible benefits that would accrue if the gap between them were bridged in ways that could motivate students to create new opportunities for architectural innovation.
Nimish BILORIA Anurag BHATTACHARYA Faculty of Architecture, TU Delft THE NETHERLANDS
Performance Driven Generative Design Methodology: Interfacing Multi-Agent Simulation Driven Design Techniques with Environmental Modeling Methodologies in Architectural Education
Operating at a post-graduate level with a program spanning the entire Masters education (MSc1 to MSc4), The Chair of Hyperbody (Faculty of Architecture, TU Delft), has been carefully developing an effective performance driven design educational agenda. The agenda focuses on imparting a holistic architectural education, wherein urban, architectural, componential as well as fabrication techniques interface with cutting-edge computational and environmental design tools and methodologies. Instead of inculcating the attainment of a glorified form, the underlying pedagogy involves initiating a process involving iterative knowledge building computational experiments involving a relational mode of thinking for cultivating a variety of emergent (Johnson. S. 2001) spatial morphologies embodying different metric structures and embodied parametric relationships for deciphering creative, ingenious yet apt logics per design research experiment. This mode of operation focuses upon associatively developing a symbiotic relationship between dynamic contextual information such as environmental data, energy regulations, socio-cultural patterns and material property based form-finding techniques. A research investigation termed ‘InfoMatters’ initiated by the author (Biloria, N. 2011), at Hyperbody, TU Delft, to understand the intrinsic linkage between digital information and material systems was thus set up in order to develop inter-disciplinary design driven processes focused on developing performative spatial systems. Understanding this informatics constituent specifically involved the development of datasets of spatial, environmental as well as social behavior as a medium for understanding the urban. This information field serves as an experimental set-up within which multi-agent simulations with differential yet inter-dependednt agencies are generated. The multi-agent simulation idea is based on an understanding of iteration, differentiation and optimization based processes in natural systems, which result in multi-performative, adaptive, self-organizing formations (Bentley, P.J. 1999). These formations apart from displaying highly performative traits concerning structural, environmental and metabolic optimization are also equally interest provoking when understanding them in the context of polymorphic topologies (Hensel and Menges, 2006). A real-time adaptive process of evolution in time, corresponding to internal genetic regulations and external environmental factors in nature was thus seen as a vital domain of research. Self-organizing multi-agent system based generative methodologies for evolving spatial formations in time, based on the impacts of associative relationships between the aforementioned data sets typically take three contingency areas into account: Agency, Structure and Behavior. Two sets of agents are typically considered, one being the higher order and second being the lower order. The higher order agents embody an agency covering a broader contextual background, updating and revising their behavior with respect to time. These have an impact on lower order agents which act as followers or trackers in multiple sub-swarms. This differentiation in agency makes the computational workflow faster and possible to run on computer systems with limited capability to handle calculations. These agencies can be further sub-classified and structured under swarms of agents catering to infrastructural, functional or even social routing streams attained from layered network definitions. These agents are further embedded with flocking (Reynolds, Craig W. 1987) behavioral instincts (alignment, cohesion, seperation, directionality etc with respect to themselves and with other agent populations) which helps them to interact with varying agent 544
ENHSA - EAAE 61 Architectural Education and the Reality of the Ideal: Environmental Design for Innovation in the Post-Crisis World
typologies, be attracted to or repelled by differentiated denisty zones within the urban. After a series of reiterative cycles of negotiations and simulation runs, a subsequent stage of multi-swarm optimization, involving typically involving self-organization based on obstacles, noise proximity, traffic allowance, power and field of vision of individual agents is embarked upon. Another behavioral attribute taken into account during this optimization, being density control, wherein incoming agents evaluate the density of the area based upon determinant factors like sensitivity to existing agent density and the number of similar or different flocking agents. Quintessentially the computational simulation makes decisions ertaining to the flocks self-organizing patterns and growth or eventually the option of fading out and dying due to entropy, forming clusters based on the overall agent saturation levels reached (prescribed for specific agents in a certain period, gained network intelligence and cluster distances within an environmental, social and spatial context). The paper, via an experiment of such research driven design processes will exemplify the computational methodology and resultant performative outputs at different scales (urban, architectural and componential). Sustainable design via an integration of computational methodologies and environmental analysis driven iterative formfinding processes shall thus be put at the forefront via this research paper. A reduction in post-optimization routines of built form and consequently the development of a rational understanding of performance criteria and its impact on formal articulations throughout the design process is thus professed through the medium of this research article.
Performance design experiment: Agriflux, Mikelli, Finland An example where the pedagogy involved in the aforementioned pedagogical approach towards performance design development can be traced in a recent (2012-13) graduation project by Anurag (co-author): “Agriflux�. Agriflux aimed to investigate spatializing the co-evolving relationship between digital information and physcal matter for the generation of environmentally driven performative architectural formations. Focusing on the development of self-sufficient architectural scale urban inserts using performance driven computational techniques, the project deals with a complex issue of urban reconnect and regeneration of dilaptated lake-front creating buildings of a hybrid nature in Mikelli, Finland. The emphasis being on designing of a science center integrated with jetty/ public esplanades and public greenhouses. Utilizing the System design thinking theory as a logical framework within which multi-layered networks self-organize based on local rules and information exchanges. A methodology has been adopted that combines performative design processes with a set of generative simulation tools, for mapping the dynamic behavior of the site and the functions dispersed within. The simulation tool is used as an input for the form generation. A secondary level of self-organization which reflects programmatic and organizational bound internal information which guides the process of form-finding to the next level of optimizing the spatial conditions has also been included. At this level one of the major influences is the Scandinavian weather with harsh winters, uncomfortable wind turbulences and low solar gain. The methodoloy involved a networked interconnection between the following meta research components: Urban Context, Localization, Programmatic Dispersion, Nimish BILORIA, Anurag BHATTACHARYA THE NETHERLANDS
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Fig. 1 Methodological Stages.
Topology, Structural Optimization, Environmental Design, Fabrication Protocols and Material Translation. The prefix meta implying each component being the resultant of relational matrices of variable interlinked parameters.
Research Components The following section shall illustrate each component in sufficient detail and in the process shall establish interrelationships between each component.
Urban Context The site provided pre-defined urban attractor points in the form of existing land-use functions (residential, public facilities, retail, mixed used, wasteland, open spaces, etc.) with varying degree of weightage with embedded starting points for different agent typologies. Each agent scans every other agent from a specified flock and infrastructure obstacle boundaries, they either follow or move away from other flocks depending on the logics (here logics being the degree of attraction between various flocks, the power of separation, cohesion, alignment and obstruction) fed during the simulation. By doing this each agent successively gets a higher order in the network. Simultaneously each attractor point calculates the number of agents influenced by it. The agents which are influenced by these attractor points also receive very specific set of instructions valid for that spot as coded by the designer. This exercise gives rise to various network typologies like hybrid, distributed, centralized, etc. (Figure 2a). The more the agents cluster around a specific spot, the more the probability that they reach a threshold and furthermore gain a higher popularity rating. This triggers the “KILL� command (thus culling swarming) and creating an inter546
ENHSA - EAAE 61 Architectural Education and the Reality of the Ideal: Environmental Design for Innovation in the Post-Crisis World
Fig. 2 a: Multi Agent simulation rules (urban context), b: Network layers.
connected cluster in 3D space depending upon the cluster distance provided by the designer for specific flocks. These clusters can further be classified into high density and low density areas. For a potential spot, the smaller the minimum distance of connectivity between differential agents, the higher the density that area will have and vice versa. The urban level configuration and spatial distribution networks generated from this first round of simulation again trigger an emergent behavior and inherit influence over the agent flocking at the local site level. Nimish BILORIA, Anurag BHATTACHARYA THE NETHERLANDS
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Localization At local site level, agents gather local information parameters generating dynamic density and forming clusters inheriting urban conditions from the first round of urban simulations. These local site level agents are programmatic agents and the topology of the programs are determined based on the F.A.R. (Floor area ratio) limits imposed by city planning authorities. In this example, the site is a myriad of functions and open areas, which come along with certain degree of historical and cultural aspects. This called for different layers of interactions, namely: Infrastructural, Transport, Functional and Landscape (Figure 2b). Post urban simulation evaluation hints towards various infrastructure development trends for instance at an urban level, automobile connections show trends of better connection with existing primary highways. Hint of creating a new bridge dedicated to pedestrian influx to the site perimeters and new traffic infrastructure diversion for secondary and tertiary roads. Agriculture distribution system agents densify along the existing farmers market and warehouses. It rises and meets the highway dissipating produce throughout the city (Figure 3a). At the local site level, the program agents such as the Science center tries to remain at the location close to the proximity of infrastructure for easier connectivity. The Exhibition area tries to remain neutral. The Esplanade act like a binding agent connecting all programs. Public areas tend to align close to the esplanade and exhibition areas. Esplanade topologies show differential heights and the possibility for landscape features. A unique typology connecting the science center - restaurant - shops and exhibition area into one complex also emerged. Access to pedestrian under the complex to the adjoining esplanade and lake creating unobstructed vistas of lake through it was suggested. Restaurant mostly acting as a bridging structure, elevated to offer better views and privacy to users, spatially a very interesting hybrid formation springing into the lake. As a result of these first set of simulations, boundary conditions of different functions, the heights and the overall spatial distribution typology of different spatial agencies are attained (Figure 3b). Infrastructure Cluster Abstraction: Post the two urban and local level simulations, Point cloud and Floor Area Ratio (FAR) value datasets extracted out of the simulations are interpolated generating topological surface conditions using a grasshopper script that create spatial and differential heights required to organize internal zones and peripheral boundary.
Programmatic Dispersion The Programmatic Dispersion component operates within the obtained field of varying densities from the aforementioned simulations. Rather than considering the programmatic arrangement as the organization of platonic programmatic elements, here programs were considered as a self-organizing system of programmatic masses, which aggregate based upon weighted connections and parameters to specific anchor programs (these being the programs which would act as primary focal spatial nodes as per the architectural requirement). In order to meet specific programmatic requirements such as circulation, area calculation, accessibility and transparency lev548
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Fig. 3 a: Self organizing Multi-agent system based results, b: Multi Agent System based Local level self organisation outcome.
els. A second degree of simulation was coded using Processing (open source software platform). The behavioral parameters embedded for this simulation being the program area, internal connections, crossover degrees, physical affinities, noise allowance, accessibility thresholds, transparency and height of the functions (Figure 4). Nimish BILORIA, Anurag BHATTACHARYA THE NETHERLANDS
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Fig. 4 Programmatic Dispersion generative outcomes.
In the example, three cores of science center and greenhouse were chosen with a set of programs and parameters embedded within them to achieve differential distribution. The Processing sketch was executed multiple times with random seeds in order to achieve a manually optimized design regarding program distribution and form development. The next step was to make sense of these dispersions, after choosing a good random seed, the extracted geometry is judged based on the overall shape and geometry. Results displayed a degree of unison with the designers intention, for instance restaurants and labs were located more on the external periphery with better views, easy accessibility and distance from noise sources at the same time. Lobby spaces demanded better connectivity and hereby were located as main anchors with high accessibility. The chosen geometry/iteration is then exposed to circulation connectors based on minimal distance algorithm and environmental forces to produce better aesthetically articulated shape and efficient topology.
Geometry|Topology The topology emerges from an exploration of technological possibilities and parametric modes of operation, which allow for information driven complexities, efficiencies and geometric possibilities previously incomprehensible. This methodology allows for the bottom-up generation of architectural complexity from individual component level to a collective spatial level. The allocation of programmes are determined by the trajectory of the Sun. A script was written using Grasshopper in which spaces adapt to the concept of a solar fan whereby meeting the performative requirements of the building. The solar computational abstraction especially plays a pivotal role in fulfilling the buildings’ lighting and solar requirements. In this case, the geo-coordinates 550
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Fig. 5 Solar fan simulations.
impose harsh dark winters and comparatively lower sun angles. Here the script takes into account the base curve, the latitude, total sun exposure (range of months and daily hours), spatial definition inherited from previous simulations thus generating abstracted negative non-orthogonal vector shafts creating unique situations (Figure 5). These shafts give the building shallow plans and enhances diffused light penetrations much needed in those weather conditions.
Structural Optimization Simultaneously another feedback loop is initiated at this moment to generate a structural mesh which takes into account the abstracted solar geometry and encapsulates underlying spaces. This is further refined using mesh refinement algorithms to generate a more cohesive structural vocabulary by the designer. Furthermore the structure is evaluated using Finite Element Method (FEM) Millipede package in Grasshopper to define beam depth, shapes, sizes and identify vulnerable structural elements (Figure 6). In this example, the script takes into account a very essential performative element of the building, the roof performance optimization (3D Roof profile for facing heavy snowdrifts common in Finland caused due to strong winds which are translated into numeric slope angle simulation). The parameters taken into account for the roof involved Genetic Algorithm (GA) optimization using Galapagos, which took into consideration the following: roof slope normal inherited from mesh generation algorithm, negative snowdrift attractors (located in exterior periphery of the building where the designer wants to avoid the snowdrift to occur, mostly pedestrian routing fields), attractor positions where snow can be drained and the active vertexes whose degree of freedom in movement is evaluated during every GA run based on the FEM analysis Nimish BILORIA, Anurag BHATTACHARYA THE NETHERLANDS
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Fig. 6 Snow drift load based structural and spatial optimization.
fitness without compromising the overall structural stability. This cycle of simulation generates various options, the best one is chosen on the basis of the most satisfying result both in terms of performance and aesthetics by the designer. The roof optimization created a unique slope condition for facade at certain angles, which helps in breaking the snow falling off the roof.
Environmental Design Apart from the solar and snow loading conditions, which informed spatial and structural refinements, wind conditions and the manner in which effects of wind turbulence could start refining the developing architectural aesthetics became vital. The structural results are thus further cycled via another feedback loop wherein the project has to mitigate wind turbulence using the classical aerodynamics principles. In fluid dynamics, turbulence or turbulent flow is characterized by chaos and is caused 552
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Fig. 7 CFD (Computational Fluid Dynamics) wind velocity and pressure based site analysis.
Fig. 8 Esplanade level wind forming optimization.
due to rapid variation of pressure and velocity in space and time. It is an ignored aspect in current architectural design discourse but becomes relatively important in sites having strong and cold wind flow conditions. This optimization wil result in increasing thermal comfort of the public spaces, which would normally be disturbed due to the turbulence created by the buildings topology itself. The building topology and its surroundings must interact to minimize its effects (Figure 7). The shown 3D representation is the result of all the previous urban level CFD analyses carried out with Autodesk CFD Simulation 2013. The gradient goes from blue for laminar flow, to red for the turbulent motion. Figure 8 shows the aerodynamic performaNimish BILORIA, Anurag BHATTACHARYA THE NETHERLANDS
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tive promenade, optimized using surface lining (the subdivided surface’s longitudinal edges). The parameters being: wind flow vectors, active promenade surface reparamatrized to gain better control, pressure gradient (0-1), red for high and blue for low. These parameters became the input for Galapagos GA optimization where the main objective is to either deflect the wind above the promenade creating low pressure region behind or else make the promenade act as wind breakers. The same principle applies to the building as well, but the biggest challenge here is to satisfy functional and architectural aspects while meeting the performative aspects. In this example, the building is exposed to wind simulation setup with speeds averaging 38-40m/s, with the vulnerable facade segments with high pressure gradient ranging 32.9 to 63.2 Pa, in other words obstructing the wind flow. The impact caused by air fluxes creates even more dispersion. These were tackled by studying “air vent”, “wind spoilers”; possible air intake shapes and junctions inside the building to redirect wind and efficiently produce energy. In the example, after analyzing various aerodynamic components, a pressure releasing component was finally chosen, which would be proliferated in a pattern on the extracted facade. This component would trap the exterior flow and channelize it further into vents for further being used either to generate energy using turbines or to just release it somewhere else. The ultimate goal is to recover the load loss, “giving pressure” to the system for better smoothness, continuity and reducing turbulence.
Fabrication The fabrication based requirements were derived from the environmental performance criteria as regards thermal insulation and wind flow. The component will be fabricated using thermally insulated epoxy resin shell with a fibre transparent cover that would allow the light to enter but trap the wind (Figure 9). The transparent cover will thus allow light derived heat energy to be stored while trapping the cold exterior wind. The toplogy of the component is also developed carefully to release the pressure difference through inbuilt solid channels in the component.
Material translation The choice of material comes from the logic of engagement. In the example characteristics of wood (Glulam) in conjunction with other materials like steel and concrete are used to create new and optimized hybrid material solutions. The primary structure is also chosen to be Glulam composite due to the local availability and manufacturing skill available in Finland. When compared to steel and concrete-wood composites, this composite was found to have higher air insulation, while steel and wood composite work out best in tension and for joints. Reusability and reclamation coefficients are also higher with the choice of this material palette.
Conclusion The methodology presented in this paper outlines an integrated data driven computational and environmental design approach wherein typical issues of engaging
Fig. 9 Aerodynamic component taxonomy.
computational routines for glorified formal attributes takes a back seat. Instead, the collaborative knowledge sharing between different disciplines of environmental sciences, natural sciences, information technology, computer aided manufacturing and architectural design operate synergistically in order to bottom-up generate the logics of a holistic spatial system (Figure 10). The implementation of layered operation of multi-agent simulations with specific variations in the degree and relationality of agency deployed per agent cluster results in valuable logistics for iterative computational experimentations. Per research component level, in itself relies on and at the same time provides an opportunity to the designer to re-evaluate the underlying results of each stage of simulation. As is clear from the elaborated example, almost all components: Urban Context, Localization, Programmatic Dispersion, Topology, Structural Optimization, Environmental DeNimish BILORIA, Anurag BHATTACHARYA THE NETHERLANDS
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Fig. 10 Computational Workflow.
sign, Fabrication Protocols and Material Translation involve a strong co-relation with environmental factors ranging from sun directionality, snow loads, wind conditions etc. This results in a simultaneous, integrated approach towards generating architectural propositons and detail at various scales, which co-evolve from a quantitaive and qualitative perspective. Issues of aesthetics thus take up a new dimension, namely performance driven design, rather than using computational tricks for generating complexity for the sake of the term. More importantly the parametric nature of developing performative façade systems coupled together with the systematic manufacturing and assembly of the digitally derived geometries offers fitting proof for validating the iterative computational methodology and for proving the realistic spatial nature of the final outcomes. This inter-performing data-driven approach devoid of its reliance on architecture styles and typologies is thus deemed a democratic methodology to understand our built environment and to bottom-up produce sustainable architectural morphologies. An interdisciplinary mode of operation to invent a new take on pre-processing via integration rather than post-design optimization of architectural space for the sake of sustainability is thus seen as a vital outcome of the research and design methodology.
References Bentley, P J (ed.) 1999, Evolutionary Design by Computers, Chapter 14, Morgan Kaufmann. Hensel & Menges 2006, “Differentiation and Performance: Multi-Performance Architectures and Modulated Environments”, Architectural Design AD, 76 (3), John Wiley & Sons, London, pp. 60-69. Johnson, S 2001, Emergence, the Connected Lives of Ants, Brains, Cities and Software, Penguin Press, London. Biloria, N 2011, InfoMatters, a multi-agent systems approach for generating performative architectural formations, International Journal of Architectural Computing, Issue 03, Volume 09, September, pp. 205 - 222. Reynolds, C W 1987, “Flocks, herds, and schools: A distributed behavioral model”, Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, (SIGGRAPH’87) (ACM), doi:10.1145/37401.37406. Hendtlass, T 2005, “WoSP: A Multi-Optima Particle Swarm Algorithm,” in Proceedings IEEE Congress on Evolutionary Computation, pp. 727–734. 556
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