1. Data Center Hall Massing (the decision I modeled)
Design Variables: Hall length, hall width
Evaluators: Rack count, enclosing surface area (proxy for construction cost and cooling load)
Tradeoffs: Spreading the floor plan out fits more racks but adds wall and roof you have to build and cool. A compact square minimizes the envelope but limits how racks tile and leaves a deep floor that is harder to cool. Capacity and envelope cannot both be optimized at once.
2. Prefab Module Sizing
Design Variables: Module length, module width
Evaluators: Number of modules and site connections, transport cost penalty
Tradeoffs: Bigger modules mean fewer parts, fewer site connections, and more work done in the controlled factory, but they hit truck and crane limits where cost jumps from permits, escorts, and larger equipment. The sweet spot is the largest module you can still ship cheaply.
3. Structural Bay Sizing
Design Variables: Bay span in each direction
Evaluators: Structural material cost, column count
Tradeoffs: Long spans give open, flexible, higher rent floors, but beam depth and material cost rise quickly and eventually the beam will not fit the floor to floor height. Short spans are cheap and shallow but fill the floor with columns.
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Step 2 - Generative Design Study
Design Decision Studied, Data Center Hall Massing
Data center halls face a constant tension between packing in as much computing capacity as possible and keeping the building cheap to construct and cool. The size and shape of the hall drive both, which makes the footprint a high impact decision.
Design Variables: Hall length, hall width
Constants: Rack footprint, aisle pitch (8 ft lengthwise, 4 ft across), wall height (16 ft)
Evaluators: Rack count (maximize), enclosing surface area in square feet (minimize, as a proxy for construction cost and cooling load)
Tradeoffs: As the hall grows it fits more racks but also takes more surface area to enclose, so capacity and envelope rise together. The real lever is shape: for the same rack count, a more compact (squarer) footprint uses less wall and roof than a long, skinny one. The study sweeps length and width to map this tradeoff. A full reading of the results is below.
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Step 3 - Generative Design Study Results
The screenshot of the Scatterplot or Parallel Coordinates Graph illustrating the tradeoff that you chose to model and study.
Provide a brief explanation of what’s being shown in the Scatterplot or Parallel Coordinates Graph and how the tradeoff being illustrated would impact the design decision. What would you do with this info?
An image of your Dynamo Study Graph (showing all your nodes and the connecting logic) -- You can use the File > Export Workspace As Image... command in Dynamo to save a PNG image to upload with your posting.
The plot puts enclosing surface area on the X axis (cost and cooling proxy) and rack count on the Y axis (capacity). Each point is one hall design from the length and width sweep. The best designs sit toward the upper left, the most racks for the least envelope. Capacity and envelope rise together overall, but the useful insight is shape. The 160 by 60 and 120 by 80 halls both fit 300 racks, yet 160 by 60 costs 16,640 sq ft of envelope versus 16,000 for 120 by 80. The 100 by 100 hall fits 325 racks for about the same envelope. Squarer footprints are consistently more efficient (about 0.020 to 0.022 racks per sq ft versus 0.015 to 0.018 for skinny ones), so the long narrow halls are dominated options I would drop. On a real project I would use this to set footprint proportions first, steering toward the compact, efficient designs, then pick a point along that edge based on required capacity and envelope budget.
