Hyun Woo Lee

• Design Decision 1 - Data Center Building Dimensions
• Design Variables
• Building Length
• Building Width
• Building Height
• Evaluators
• Power Capacity
• Construction Cost
• Cooling Load
• Most Important Tradeoffs to Consider
• Increasing the building dimensions can improve the total power capacity of the data center, but it also increases construction cost and cooling demand.
• Taller buildings can provide more usable space, but they can increase construction complexity and operational costs.
• Design Decision 2 - Facade Glazing Strategy
• Design Variables
• Glazing Ratio
• Evaluators
• Daylight Score
• Cooling Load
• Construction Cost
• Most Important Tradeoffs to Consider
• Higher glazing ratios improve daylight access and visual quality, but they also increase solar heat gain and cooling load.
• More glazing may also increase facade and installation costs.
• Design Decision 3 - Overall Building Performance Optimization
• Design Variables
• Building Length
• Building Width
• Building Height
• Glazing Ratio
• Evaluators
• Power Capacity
• Cooling Load
• Daylight Score
• Construction Cost
• Most Important Tradeoffs to Consider
• Larger buildings improve power capacity and daylight opportunities, but they increase cooling demand and construction cost.
• Increasing glazing improves daylight performance, but it also raises cooling load and facade costs.
• The study aims to balance high-performance data center operation with lower construction and operational impacts.

For this Generative Design study, I focused on optimizing a conceptual data center building based on performance and cost tradeoffs. The goal of the study was to identify design alternatives that maximize power capacity and daylight performance while minimizing cooling load and construction cost.

The Generative Design study was set up using four main design variables: building length, building width, building height, and glazing ratio. These variables were controlled using sliders in Dynamo and used as the primary inputs for the optimization process.

The study used several evaluators to measure the performance of each generated option. Power capacity was estimated using the building floor area, representing the amount of usable IT equipment space within the data center. Cooling load was estimated based on overall building surface area and glazing ratio to represent the increase in cooling demand caused by larger envelope exposure and higher glazing percentages. Daylight score was calculated using glazing ratio and surface area to estimate natural lighting performance. Construction cost was estimated using overall building dimensions and envelope area to represent material and construction impacts.

The optimization study explored multiple combinations of the design variables and generated different design alternatives. The study was configured to maximize power capacity and daylight score while minimizing cooling load and construction cost.

The scatterplot and parallel coordinates graph visualize the relationships and tradeoffs between the different performance evaluators and building variables generated during the optimization study.

The results show that increasing building dimensions generally improves power capacity because larger floor areas can support more IT equipment and operational space. However, larger buildings also increase construction cost and cooling load due to increased envelope size and energy demand. Similarly, higher glazing ratios improve daylight score but also increase cooling load because of additional solar heat gain.

The scatterplot was configured to compare construction cost and power capacity while also visualizing daylight score and cooling load. This allows efficient comparison of multiple generated design options simultaneously. Design alternatives located toward the higher power capacity region with relatively lower construction costs represent stronger optimization outcomes.

The parallel coordinates graph further illustrates how the design variables interact with the evaluators. It shows how changes in building length, width, height, and glazing ratio directly influence the performance metrics. This visualization helps identify balanced design solutions that achieve high operational performance while controlling cost and cooling demand.

Using this information, I would select design options that provide strong power capacity performance while maintaining acceptable construction cost and cooling load levels. The study demonstrates how Generative Design can support early-stage decision-making by quickly evaluating multiple design alternatives and identifying efficient performance tradeoffs.