Step 1 - Generative Design Framework
Step 2 - Generative Design Study
Step 3 - Generative Design Study Results
Results:
Results with Filters:
Explanation:
This scatterplot maps glazing area on the x-axis and shading effectiveness on the y-axis, with total material cost shown through the size and color of each point (larger and darker points represent the more expensive options). I originally chose the light blue dot at the top as the most optimal result because it has the highest shading effectiveness and one of the highest glazing values, meaning it is a front runner in terms of both passive shading and access to daylight, which are my main priorities.
Because of how high it was performing in shading effectiveness and glazing area, I assumed this option would be one of the most expensive. However, when I applied filters to highlight only the top-performing options, (shading effectiveness over 100 and glazing area over 100,000) I realized that the dot I picked also has the lowest material cost of the remaining 3.
The scatterplot was useful in helping me visualize how each design choice impacted multiple outputs at once, and it made the tradeoffs very clear. It also confirmed that the design I first identified as a top contender based on performance also ended up being the best choice for cost reduction. If I were a designer filtering through design options to pick based on this graph, I would use the filter and specifically look for options with good glazing area and solid shading effectiveness at a reasonable material cost. I’d probably prioritize designs that lean toward better shading (to reduce long-term energy use) but still offer enough transparency to keep the interior bright and connected to the outside. The goal wouldn’t be to max out one evaluator but to find a balanced configuration that can perform well across all three metrics.
Dynamo Logic:
