Step 1 - Generative Design Framework
1. Adaptive Facade Fins
Design Variables
- Fin rotation angle
- Fin depth
- Fin spacing
- Fin shape
Evaluators
- Solar heat gain
- Daylight levels
- Energy performance
- Visual appearance
Tradeoffs
- More shading = lower cooling loads but less daylight
- Larger fins = stronger facade expression but higher material cost
- More movement = better performance but more mechanical complexity
2. Window-to-Wall Ratio Optimization
Design Variables
- Glass percentage
- Window size
- Panel spacing
- Glazing type
Evaluators
- Daylight quality
- Energy use
- Construction cost
- Exterior appearance
Tradeoffs
- More glass = better views but higher energy use
- Less glass = lower cooling loads but darker interiors
- High-performance glazing = better efficiency but higher cost
3. Parametric Facade Panel System
Design Variables
- Panel size
- Panel depth extrusion
- Panel density
- Surface pattern variation
Evaluators
- Material usage
- Shading performance
- Fabrication complexity
- Aesthetic quality
Tradeoffs
- More complex panels = stronger visual identity but harder fabrication
- Denser panels = more shading but less visibility
- Larger panels = faster construction but less facade variation
Step 2 - Generative Design Study
A simple parametric facade model was created in Grasshopper. Vertical fins are placed across one building facade and can be adjusted using sliders that control their angle and depth. The model automatically updates the facade geometry and evaluation metrics when the design variables change.
Design Variables
- Fin rotation angle (0–90°)
- Fin depth (1–5 ft)
- Fin Length (1-5ft)
Constants
- Building form (rectangular prism)
- Building height and width
- Fin placement pattern
- Facade orientation
Evaluators
- Shading Score = Fin Angle × Fin Depth
- Daylight Score = 90 − Fin Angle
- Material Use = Total Fin Surface Area
Tradeoffs
Increasing fin angle and depth improves shading performance but reduces daylight and increases material use. Smaller fins provide more daylight and use less material but offer less solar protection. The Generative Design study explores these competing objectives to identify balanced facade solutions.
Step 3 - Generative Design Study Results
The scatter plot shows different facade fin designs generated during the study. Each point represents a unique combination of fin angle and depth. The graph highlights the tradeoff between shading, daylight, and material use. I would choose a design near the center of the high-performing cluster because it provides a balanced solution.
The evolution graph shows the fitness score improving over time as Galapagos tests different design options. The curve gradually levels off, indicating that the solver found increasingly effective facade configurations and approached an optimal solution.
The Grasshopper model creates facade fins on one side of a prism building. Fin angle, length, and depth are used as design variables, while shading, daylight, and material use are used as evaluators. These outputs are combined into a fitness score that Galapagos optimizes. The evolution graph shows the fitness score increasing from approximately 250–300 in the early generations to around 430–450 by the final generations. The optimal fin count would be:
- Fin angle: 90°
- Fin depth: 5.0 ft
- Fin length/height: 5.0 ft
What might have gone wrong:
- The Material Use calculation may not be receiving valid geometry, causing null values.
- The fitness score is dominated by shading performance, leading to clustered results.
- Only two design variables were used, limiting variation between solutions.
- Simplified evaluator formulas caused many alternatives to perform similarly.
