The Ultimate Computational Design Cheatsheet: Tools, Workflows & Techniques

Introduction to Computational Design

Computational design is an approach that leverages algorithms, mathematical principles, and computational power to generate, optimize, and analyze design solutions. Unlike traditional design processes that rely primarily on manual creation and intuition, computational design employs rule-based systems and data-driven methods to explore design possibilities, often revealing innovative solutions that might otherwise remain undiscovered. This approach has transformed disciplines including architecture, product design, graphic design, engineering, and manufacturing by enabling more complex forms, optimized performance, and customized solutions.

Core Concepts and Principles

Fundamental Paradigms

• Parametric Design: Design driven by parameters and relationships rather than fixed dimensions
• Generative Design: Using algorithms to generate multiple design alternatives based on constraints and goals
• Algorithm-Aided Design: Embedding computational logic within the design process
• Form-Finding: Discovering optimal forms through simulation of physical forces and constraints
• Optimization: Refining designs to meet specific performance criteria
• Emergence: Complex patterns and behaviors arising from simple rules
• Iteration: Repeated refinement through computational cycles

Key Theoretical Frameworks

• Complexity Theory: Understanding design as a complex adaptive system
• Systems Thinking: Viewing designs as interconnected systems of relationships
• Morphogenesis: Computational simulation of natural growth processes
• Topology Optimization: Mathematics-based approach to material distribution
• Cellular Automata: Grid-based systems with simple rules creating complex patterns
• L-Systems: Formal grammar for modeling plant growth and organic forms
• Shape Grammars: Rule-based systems for generating geometric compositions

Methodological Process

General Computational Design Workflow

1. Problem Definition: Articulate design goals, constraints, and parameters
2. Algorithmic Formulation: Develop computational approach and logic
3. Parameter Setting: Define variables and their relationships
4. Generation: Run algorithms to create design alternatives
5. Evaluation: Assess results against performance criteria
6. Iteration: Refine parameters and algorithms based on evaluation
7. Selection: Choose optimal design solution(s)
8. Post-Processing: Prepare for fabrication, documentation, or implementation

Parametric Design Process

1. System Analysis: Identify components, relationships, and dependencies
2. Parameter Identification: Define key variables that control the design
3. Relationship Modeling: Establish mathematical or logical relationships
4. Hierarchy Construction: Create dependency structure between components
5. Interface Design: Build controls for manipulating parameters
6. Exploration: Test parameter variations to understand design space
7. Documentation: Record successful parameter sets for future reference

Key Techniques and Tools by Category

Modeling and Form Generation

• Parametric Modeling Software
  • Grasshopper for Rhino
  • Dynamo for Revit
  • Generative Components
  • CATIA Parametric Design
  • Fusion 360
• Visual Programming Languages
  • Grasshopper (visual scripting)
  • Dynamo
  • Sverchok for Blender
  • Marionette for Vectorworks
• Text-Based Programming for Design
  • Python (with libraries like NumPy, SciPy)
  • Processing/p5.js
  • JavaScript (Three.js, D3.js)
  • C# (Unity)
  • MATLAB

Analysis and Simulation

• Structural Analysis
  • Karamba3D
  • SOFiSTiK
  • Robot Structural Analysis
  • SAP2000
• Environmental Analysis
  • Ladybug Tools (Ladybug, Honeybee)
  • Ecotect
  • EnergyPlus
  • Radiance
• Computational Fluid Dynamics (CFD)
  • Autodesk CFD
  • SimScale
  • ANSYS Fluent
  • OpenFOAM

Optimization Techniques

• Genetic Algorithms
  • Galapagos
  • Octopus
  • Wallacei
• Machine Learning for Design
  • TensorFlow
  • PyTorch
  • Keras
  • ML.NET
• Topology Optimization
  • Altair solidThinking
  • Ansys Mechanical
  • nTopology
  • Autodesk Generative Design

Fabrication and Materialization

• Digital Fabrication Tools
  • Slicer for CAM
  • RhinoCAM
  • Fusion 360 CAM
  • Grasshopper CAM plugins
• Robotic Control
  • KUKA|prc
  • HAL Robotics
  • Robots plugin for Grasshopper
  • RoboDK

Comparison of Approaches

Design Paradigm Comparison

Software Comparison for Parametric Design

Common Challenges and Solutions

Technical Challenges

• Challenge: Computational complexity and performance issues

  • Solution: Use simplified proxy models during development; employ distributed computing

• Challenge: Managing parameter interdependencies

  • Solution: Create hierarchical parameter structures; use data trees; implement constraint solvers

• Challenge: Translating between software platforms

  • Solution: Use neutral file formats (STEP, IGES); develop custom data translation scripts

• Challenge: Version control for parametric models

  • Solution: Document parameter values; use text-based representations when possible; implement naming conventions

Methodological Challenges

• Challenge: Balancing computational and intuitive design

  • Solution: Create hybrid workflows; use computation for exploration but maintain human decision-making

• Challenge: Defining appropriate fitness criteria for generative design

  • Solution: Conduct sensitivity analyses; combine quantitative and qualitative evaluation methods

• Challenge: Making computational design accessible to non-technical designers

  • Solution: Create user-friendly interfaces; develop reusable components with clear documentation

• Challenge: Maintaining design intent through fabrication

  • Solution: Develop integrated digital-to-physical workflows; incorporate fabrication constraints in early design

Best Practices and Tips

Workflow Best Practices

• Start with simple models and incrementally add complexity
• Document your computational logic and parameter relationships
• Create modular, reusable components and definitions
• Use data visualization to understand parameter relationships
• Implement version control strategies for algorithm development
• Test algorithms with extreme parameter values to ensure robustness
• Consider fabrication constraints early in the design process
• Create intuitive interfaces for non-technical users to interact with

Practical Tips

• Name components and groups clearly for future reference
• Comment your code or visual programming graphs extensively
• Organize complex definitions into logical clusters or groups
• Save incremental versions to prevent data loss
• Preview intermediate results to catch errors early
• Use color coding to distinguish different functional aspects
• Create responsive designs by linking parameters together
• Benchmark performance to identify computational bottlenecks

Resources for Further Learning

Software-Specific Resources

• Grasshopper
  • Grasshopper Primer (official tutorial)
  • Parametric House (tutorials and examples)
  • Food4Rhino (plugin repository)
• Dynamo
  • The Dynamo Primer (official documentation)
  • Dynamo Packages (extension library)
  • Dynamo Forum (community support)
• Coding for Design
  • Processing.org (learning platform)
  • Coding Train (YouTube tutorials)
  • GitHub repositories of computational design examples

Books

• “Algorithmic Architecture” by Kostas Terzidis
• “Elements of Parametric Design” by Robert Woodbury
• “Parametric Design for Architecture” by Wassim Jabi
• “AAD Algorithms-Aided Design” by Arturo Tedeschi
• “Computational Design Thinking” edited by Achim Menges and Sean Ahlquist

Online Courses and Platforms

• Lynda/LinkedIn Learning (software tutorials)
• Udemy (parametric design courses)
• Architectural Association (AA) Distance Learning
• Harvard GSD Online Learning
• Designalyze (tutorials and examples)

Communities and Forums

• Grasshopper3D Forum
• SimplyRhino Community
• Stack Overflow (programming questions)
• Dynamo Forum
• McNeel Discourse

Conferences and Events

• Advances in Architectural Geometry (AAG)
• ACADIA (Association for Computer Aided Design in Architecture)
• SimAUD (Symposium on Simulation for Architecture and Urban Design)
• SmartGeometry
• Design Modelling Symposium

This cheatsheet provides a comprehensive overview of computational design concepts, tools, techniques, and best practices, serving as a practical reference for designers looking to incorporate computational approaches into their workflow.