Stephanie Chang

Journal Entry For
Module 9 - Share Your Design
ACC Folder Link
https://acc.autodesk.com/build/files/projects/ef862b37-e023-4ef6-8ec3-3615621538bf?folderUrn=urn%3Aadsk.wipprod%3Afs.folder%3Aco.0dubKBYEQDaLcj2g-J54_Q&viewModel=detail&moduleId=folders
Link to Student
Chang, Stephanie
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F2P (Form to Performance) Tool User Guide

F2P enables designers to move beyond intuition by quantitatively linking geometry to performance, supporting more informed, data-driven decision-making in early-stage building design.

A recorded F2P video demo with an overview and case study evaluations can be viewed in the following file in the ACC folder; teaser image of F2P typical results is shown in Figure 1.

  • Subfolder “F2P Video Demo”
    • 4units_Stephanie Chang_Module9_F2P_video demo.mp4
  • Attributed F2P main files (with both Galapagos & Octopus capabilities):
    • Grasshopper file: 4units_Stephanie Chang_Module9_F2P.gh
    • Inputs: 4units_Stephanie Chang_Module9_Design Space for GH_inputs.xlsx
    • Outputs: 4units_Stephanie Chang_Module9_Design Space for GH_outputs.xlsx
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Overview (ReadMe)

F2P is a parametric generative design tool developed in Rhino’s Grasshopper that enables designers to explore how building geometry influences key performance outcomes, including envelope efficiency, cost, and solar performance.

The tool allows users to modify geometric design variables, such as scaling, rotation, and base dimensions of a multi-level building form. It also allows for evaluation of how these changes impact measurable metrics like gross floor area, façade surface area, solar efficiency, and total annual insolation. F2P not only enables the 3D visualization of customizable building forms but also capabilities for performance analysis. This allows users to understand tradeoffs and identify high-performing building configurations.

By integrating both Galapagos (single-objective optimization) and Octopus (multi-objective optimization), F2P supports:

  • Decision-driven optimization (identify the best solution for a defined priority)
  • Exploration-driven design (understand tradeoffs between competing objectives)

Why Use F2P?

Early-stage design decisions often rely on intuition and limited iteration. F2P provides a structured, data-driven approach that enables users to:

  • Systematically explore a large design space
  • Understand the relationship between building form and performance metrics
  • Quantify tradeoffs attributed to envelope efficiency, cost, and solar exposure
  • Make more informed data-driven design decisions

Minimum Requirements to Run F2P

  • Rhino, Grasshopper
  • Ladybug tools (for solar analysis)
  • Galapagos (built-in)
  • Octopus plugin (optional, for multi-objective optimization)

F2P Capabilities

  • Generate parametric building forms using lofted elliptical geometry
  • Explore how design variables impact performance metrics
  • Optimize building designs for:
    • Envelope efficiency (Gross floor area / Gross surface area: GFA/GSA)
    • Solar efficiency
    • Total annual insolation
    • Cost per square foot
  • Visualize tradeoffs among competing objectives
  • Compare optimized solutions across different climate conditions

How to Use F2P Tool in Rhino’s Grasshopper?

Note: See node logic breakdown in Figure 2

  • Adjust design variables using sliders.
    • Input variables define the overall form, tapering, and twisting of the building.
  • Observe real-time changes in geometry and performance outputs
    • Outputs evaluate how design decisions affect building scale, efficiency, cost, and solar performance.
  • Choose an optimization strategy:
    • Use Galapagos to find a single optimal design (e.g., max solar efficiency, max envelope efficiency, etc.)
    • Use Octopus to simultaneously explore multiple tradeoff solutions and analyze pareto set of optimal solutions (explore tradeoffs and design space)
  • Evaluate & select a design to reinstate
    • Compare outputs across different runs and identify tradeoffs among performance metrics
    • Select a design that aligns with project goals. Reinstate that design setup in the node logic to auto-populate the corresponding input values to achieve the targeted metric(s) in the node logic (the “reinstate” capability is available in both Galapagos and Octopus).
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Helpful F2P Tips & Additional Notes

  • Input ranges are pre-defined to ensure valid geometry and stable performance outputs
  • Cost model is simplified and can be refined for real-world applications (assumptions are stated in the annotations of the Grasshopper files)
  • Solar results are dependent on selected weather location
  • Update path files for input and output excel spreadsheets to the user’s desired file path locations (example input and output files are included in ACC for reference)
  • Reinstate the desired result to populate the inputs with the corresponding values to achieve the targeted objective (applies to both Galapagos and Octopus evaluations)

F2P Case Study Demonstration for User’s Reference

Note: Images captured during case study evaluations are shown in Figure 1’s teaser image

F2P Case Studies#1 &2: Galapagos Single-Objective Demonstration for (1) Maximum Solar Efficiency & (2) Maximum Envelope Efficiency

  • F2P was used in design case studies for building form design at three climate locations (Santa Monica, Washington, and UAE), each evaluated under two Galapagos goals: maximize envelope efficiency (GFA/GSA) and maximize solar efficiency. The table comprised of the compiled inputs and outputs are shown in Figure 3. This represents six optimized design cases demonstrating how the same parametric framework responds differently by location and objective.
  • Summary of results for demonstrated case studies
    • Solar-efficiency optimization consistently produced smaller, leaner building forms with lower floor area and lower GFA/GSA, but much stronger solar performance. For example, the solar-optimized cases achieved solar efficiencies of 4056.67 kWh/m² in Santa Monica, 2770.29 kWh/m² in Washington, and 4572.04 kWh/m² in UAE, while keeping GFA/GSA below 1 in all three locations.
    • Envelope-efficiency optimization consistently produced larger, fuller massing configurations with much higher floor area and GFA/GSA, but notably had reduced solar efficiency. The GFA/GSA-optimized cases reached 2.1899 in Santa Monica, 1.9747 in Washington, and 2.3465 in UAE, with corresponding floor areas of 962,051 SF, 683,061 SF, and 776,623 SF, respectively.
    • Across all three locations, the results demonstrate a clear tradeoff between solar performance and envelope efficiency. When the form is optimized for solar efficiency, solar performance improves but usable floor area and envelope efficiency decrease; when the form is optimized for GFA/GSA, floor area and compactness increase, but solar efficiency drops.
  • Location-specific takeaways
    • Santa Monica
      • Santa Monica showed the highest total annual insolation potential among all six cases in the solar-efficiency objective, reaching 97,898,000 kWh, with solar efficiency of 4056.67 kWh/m².
      • Under the GFA/GSA objective, Santa Monica produced the largest floor area of all cases at 962,051 SF and a GFA/GSA of 2.1899, but solar efficiency dropped to 1570.81 kWh/m².
      • This indicates that Santa Monica performs especially well when solar potential is prioritized, but it also scales relatively strongly in usable area when compactness and envelope efficiency are prioritized.
    • Washington
      • Washington had the lowest solar performance overall among the three locations, with 2770.29 kWh/m² for the solar-optimized case and 1876.13 kWh/m² for the GFA/GSA-optimized case.
      • However, Washington still demonstrated the same general trend as the other locations: the GFA/GSA single-objective optimization in comparison to the solar efficiency optimization led to increasing floor area from 208,803 SF to 683,061 SF and GFA/GSA from 0.9405 to 1.9747.
      • Compared with Santa Monica and UAE, Washington appears to be the least favorable site for maximizing solar-driven performance but still benefits from optimization when the priority is building efficiency and usable floor area.
    • UAE
      • UAE produced the highest solar efficiency of all six cases in the solar-optimized scenario, at 4572.04 kWh/m², indicating that this location offers the strongest solar-responsive performance when geometry is tuned for solar access.
      • UAE also produced the highest envelope-efficiency result of all six cases in the GFA/GSA-optimized scenario, reaching 2.3465, with floor area of 776,623 SF.
      • This makes UAE the strongest overall performer at both extremes: it yields the best solar-efficiency case when solar efficiency is prioritized and the best GFA/GSA case when envelope efficiency is prioritized.
  • Overall trends demonstrated by case studies
    • The solar-optimized cases generally used smaller base ellipse radii than the GFA/GSA-optimized cases, which aligns with the smaller resulting building volumes and floor areas. For example, Santa Monica shifted from R1 = 108 / R2 = 152 in the solar case to R1 = 194 / R2 = 222 in the GFA/GSA case.
    • The GFA/GSA-optimized cases consistently showed much larger building volumes, facade areas, and floor areas, indicating that maximizing envelope efficiency in this generative design study favors fuller and more compact massing rather than slender solar-exposed building forms.
  • These case studies demonstrated F2P successfully capturing how optimization priorities and climate context influence building form and performance. Across Santa Monica, Washington, and UAE, solar-efficiency optimization consistently favored smaller, more solar-responsive forms, while envelope-efficiency optimization favored larger, more compact forms with greater floor area and GFA/GSA. Among the locations studied, UAE produced the strongest results at both extremes, achieving the highest solar efficiency for solar-driven optimization and the highest GFA/GSA under envelope-efficiency optimization with Galapagos.
  • Attributed files in ACC’s “F2P Galapagos Case Studies” Folder:
    • Compiled inputs and outputs:
      • 4units_Stephanie Chang_Module9_Design Space_compiled inputs & outputs.xlsx
    • Subfolder “Max Solar Efficiency”
      • Grasshopper files:
        • 4units_Stephanie Chang_Module9_UAE - Max Solar Efficiency.gh
        • 4units_Stephanie Chang_Module9_LA- Max Solar Efficiency.gh
        • 4units_Stephanie Chang_Module9_WA - Max Solar Efficiency.gh
      • Input and output files:
        • 4units_Stephanie Chang_Module9_Design Space for GH_input – UAE.xlsx
        • 4units_Stephanie Chang_Module9_Design Space for GH_input – LA.xlsx
        • 4units_Stephanie Chang_Module9_Design Space for GH_input - WA.xlsx
        • 4units_Stephanie Chang_Module9_Design Space for GH_output_Max Solar Efficiency - UAE.xlsx
        • 4units_Stephanie Chang_Module9_Design Space for GH_output_Max Solar Efficiency – LA.xlsx
        • 4units_Stephanie Chang_Module9_Design Space for GH_output_Max Solar Efficiency – WA.xlsx
    • Subfolder “GFA_GSA”
      • Grasshopper files:
        • 4units_Stephanie Chang_Module9_UAE - GFA_GSA.gh
        • 4units_Stephanie Chang_Module9_LA - GFA_GSA.gh
        • 4units_Stephanie Chang_Module9_WA - GFA_GSA.gh
      • Inputs and outputs files:
        • 4units_Stephanie Chang_Module9_Design Space for GH_input – UAE.xlsx
        • 4units_Stephanie Chang_Module9_Design Space for GH_input – LA.xlsx
        • 4units_Stephanie Chang_Module9_Design Space for GH_input – WA.xlsx
        • 4units_Stephanie Chang_Module9_Design Space for GH_output_Max GFA_GSA - UAE.xlsx
        • 4units_Stephanie Chang_Module9_Design Space for GH_output_Max GFA_GSA – LA.xlsx
        • 4units_Stephanie Chang_Module9_Design Space for GH_output_Max GFA_GSA – WA.xlsx
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F2P Demo Case Study#3: Octopus Multi-Objective Demonstration

  • To further demonstrate the capabilities of F2P, an additional case study was conducted using Octopus for multi-objective optimization in Santa Monica. Unlike Galapagos, which optimizes a single variable objective, Octopus was configured to simultaneously evaluate four performance metrics (envelope efficiency (GFA/GSA), cost, solar efficiency, and total annual insolation). This enabled exploration of the full design space and identification of Pareto-optimal solutions that balance competing objectives. The Octopus plug-in enables an analysis of the tradeoffs between maximizing solar performance and improving geometric efficiency, reinforcing the value of F2P as a tool that supports both exploration-driven and decision-driven design workflows. The desired geometric configuration can be selected from the 3D Octopus plot and reinstated in the Grasshopper node logic.
  • Attributed files in ACC’s “F2P Octopus Case Study” Folder:
    • Grasshopper file:
      • 4units_Stephanie Chang_Module9_LA - octopus.gh
    • Input and output files:
      • 4units_Stephanie Chang_Module9_Design Space for GH_inputs.xlsx
      • 4units_Stephanie Chang_Module9_Design Space for GH_outputs.xlsx