Studio 4 - workshop introduction
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The document discusses generative design and provides examples of different generative geometry techniques, analysis methods, and automation examples that can be used in a generative design workflow. It introduces key concepts like parametric design, the design space, performance analysis, search techniques, and different types of generative geometry including morphological, data-oriented, rule-based, and behavioral systems. Specific examples are provided for each of these techniques. The generative design workflow is described as consisting of three main modules: generative geometry, analysis, and automation.
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Studio 4 - workshop introduction
- 1. Columbia University GSAPP Danil Nagy WORKSHOPS ON GENERATIVE DESIGN
- 2. Columbia University GSAPP Danil Nagy WHAT IS GENERATIVE DESIGN?
- 3. Columbia University GSAPP Danil Nagy Novelty and performance “Rules of thumb” Unexpected yet high performing Expressionistic<-------- -------->
- 4. Columbia University GSAPP Danil Nagy How does nature design?
- 5. Columbia University GSAPP Danil Nagy How can we design like nature?
- 6. Columbia University GSAPP Danil Nagy Parametric design
- 7. Columbia University GSAPP Danil Nagy The design space x parameter y parameter zparameter Design 1 x = 9.3 y = 7.9 z = 7.5 Design 2 x = 3.9 y = 6.8 z = 7.5 Design 3 x = 3.9 y = 1.4 z = 0.7
- 8. Columbia University GSAPP Danil Nagy Performance y parameter x parameter performance Design 1 x = 9.3 y = 7.9 z = 7.5 volume = 551.0 Design 3 x = 3.9 y = 1.4 z = 0.7 volume = 3.8
- 9. Columbia University GSAPP Danil Nagy Search
- 10. Columbia University GSAPP Danil Nagy Search
- 11. Columbia University GSAPP Danil Nagy Complexity and continuity Too simple “Just right” complex yet continuous Too discontinuous (random)<-------- -------->
- 12. Columbia University GSAPP Danil Nagy Generative design workflow
- 13. Columbia University GSAPP Danil Nagy Generative design workflow module 1 - generative geometry
- 14. Columbia University GSAPP Danil Nagy Generative geometry types 1) Morphological AdvantageDisavantage 2) Data-oriented 3) Rule-based 4) Behavioral • good top-down control over design • can create discontinous design spaces • control over individual elemens • L-system, shape grammers, 1d CA (single-state) • object-oriented, agent-based behavior models (dynamic) • parametric models, GH • scripting, state-change • reduced number of inputs (abstraction of inputs into rule sets) • can create complexity • reduced number of inputs (abstraction of inputs into agent behaviors) • can lead to emergence • only top-down control • can’t control individual behavior • can’t create emergence • potentially redundant or incomplete design space • little intuitive control over macro design • potentially redundant or incomplete design space • can generate only simple and design spaces • many inputs (each element needs to be controlled seperately
- 15. Columbia University GSAPP Danil Nagy Generative geometry examples: (3) rule-based systems Prusinkiewicz, P. and Lindenmayer A., The Algorithmic Beauty of Plants (1990) Aristid Lindenmayer, Mathematical models for cellular interaction in development (1968)
- 16. Columbia University GSAPP Danil Nagy George Stiny and James Gips, Shape Grammars and the Generative Specification of Painting and Sculpture (1971) Generative geometry examples: (3) rule-based systems
- 17. Columbia University GSAPP Danil Nagy Weisstein, Eric W. “Elementary Cellular Automaton.” From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/ElementaryCellularAutomaton.html Generative geometry examples: (3) rule-based systems
- 18. Columbia University GSAPP Danil Nagy Explanation of Koch Curve. From Daniel Shiffman, The Nature Of Code (2012) Generative geometry examples: (3) rule-based systems
- 19. Columbia University GSAPP Danil Nagy Benoit B. Mandelbrot, The Fractal Geometry of Nature (1977) Generative geometry examples: (3) rule-based systems
- 20. Columbia University GSAPP Danil Nagy J. Tarbell, Substrate Algorithm (2003) Generative geometry examples: (3) rule-based systems
- 21. Columbia University GSAPP Danil Nagy Generative geometry examples: (4) behavioral systems Abstract agent-based modeling system
- 22. Columbia University GSAPP Danil Nagy Generative geometry examples: (4) behavioral systems Description of flocking algorithm. From Daniel Shiffman, The Nature Of Code (2012)
- 23. Columbia University GSAPP Danil Nagy Generative geometry examples: (4) behavioral systems John Conway’s Game of Life (1970)
- 24. Columbia University GSAPP Danil Nagy Circle packing in Rhino Grasshopper Generative geometry examples: (4) behavioral systems
- 25. Columbia University GSAPP Danil Nagy Gramazio & Kohler, Resolution Wall (2007) Generative geometry examples: (4) behavioral systems
- 26. Columbia University GSAPP Danil Nagy Casey Reas, Chandler McWilliams. Form + Code (2010) Generative geometry examples: (4) behavioral systems
- 27. Columbia University GSAPP Danil Nagy Topology optimization Generative geometry examples: (4) behavioral systems
- 28. Columbia University GSAPP Danil Nagy Danil Nagy and David Benjamin, Trabecular bone growth optimization (2013) Generative geometry examples: (4) behavioral systems Search Distance: 40 voxels Formation Bound (Gu): 0.1 Resorbtion Bound (Gl): -10 Maximum Stress Maximum Stress Maximum Stress Maximum Stress Low Low Low Low High High High Test 2: Search Distance: 8 voxels Formation Bound (Gu): 0.1 Resorbtion Bound (Gl): -10 Test 3: Search Distance: 4 voxels Formation Bound (Gu): 0.1 Resorbtion Bound (Gl): -5 Test 4: Search Distance: 8 voxels Formation Bound (Gu): 1.0 Resorbtion Bound (Gl): -10 0 10 20 30 MaximumVoxelStress(10,000Pa) 40 50 1 0 10 20 30 MaximumVoxelStress(10,000Pa) 40 50 60 70 80 1 0 10 20 30 MaximumVoxelStress(10,000Pa) 40 50 60 70 80 1 0 10 20 30 MaximumVoxelStress(10,000Pa) 40 50 60 70 80 1
- 29. Columbia University GSAPP Danil Nagy Demo: tower panalization type 1 type 2 type 3 type 4
- 30. Columbia University GSAPP Danil Nagy Demo: tower panalization ? LEFT CORNER RULE 1 RULE 2 RULE 3 RULE 4 type 2 type 4 RIGHT CORNER type 1 type 4 (3) Rule-based system
- 31. Columbia University GSAPP Danil Nagy Demo: tower panalization neighbor agerage = SUM(neighbor states) / 4 FACTOR = own state - neighbor average + 3 FACTOR range: [0 - 6] FACTOR 0 = RULE 1 FACTOR 1 = RULE 2 FACTOR 2 = RULE 3 FACTOR 3 = RULE 4 FACTOR 4 = RULE 5 FACTOR 5 = RULE 6 FACTOR 6 = RULE 7 # OF ITERATIONS neighbor state own state 0-3 0-3 0-3 0-30-3 (4) Behavioral system
- 32. Columbia University GSAPP Danil Nagy module 2 - analysis Generative design workflow
- 33. Columbia University GSAPP Danil Nagy Graph-based analysis • transoportation • circulation • congestion Finite element analysis (FEA) • structural analysis (routing of forces) in 1/2/3d • fluid dynamics Ray-tracing • view analysis • insolation/daylighting • shadow analysis Physics simulation • cloth simulation • relaxation • form-finding Analysis types
- 34. Columbia University GSAPP Danil Nagy Shadow and insolation Views Analysis types: (1) ray tracing
- 35. Columbia University GSAPP Danil Nagy Analysis types: (2) graph-based analysis The Living, Computational analysis of office circulation based on visibility graphs (2016)
- 36. Columbia University GSAPP Danil Nagy Analysis types: (3) finite element analysis (FEA) - node and beam [1d] Model setup (nodes and beam centerlines) Beam Element (2D Line) Beam elements are long and slender, have three nodes, and can be oriented anywhere in 3D space Beam corss section definition Load application and deflection
- 37. Columbia University GSAPP Danil Nagy Analysis types: (3) finite element analysis (FEA) - mesh [2d/3d] Surface Mesh Solid (Volumetric) Mesh Membrane Element (2D Planar) Membrane Elements are 3 or 4 node 2D elements that can be oriented anywhere in 3D space. 3D Tetrahedra Element (3D Solid) Tetrahedra elements are normally used to model solid objects for which plate elements are not appropriate
- 38. Columbia University GSAPP Danil Nagy Wind tunnel Air movement/heat dissipation Fluid analysis Analysis types: (3) finite element analysis (FEA) / computational fluid dynamics (CFD)
- 39. Columbia University GSAPP Danil Nagy Analysis types: (4) physics simulation Antoino Guadi, hanging model for the Colònia Güell Frei Otto, soap bubble minimal surface model Daniel Piker, Kangaroo plugin for Rhinoceros Grasshopper
- 40. Columbia University GSAPP Danil Nagy MOS, Installation No. 9 (Rainbow Vomit) MOS, Software No. 3 (Stack) Analysis types: (4) physics simulation
- 41. Columbia University GSAPP Danil Nagy module 3 - automation Generative design workflow
- 42. Columbia University GSAPP Danil Nagy Automation examples The Living, Truss optimization (2015)
- 43. Columbia University GSAPP Danil Nagy Arata Isozaki / Matsuro Sasaki, Florence New Station, 2002 Automation examples