Alanna Joachim

Step 1 - Generative Design Framework

  • Structural Design
    1. Choice of sizing for the columns of a simple steel braced frame to maximize the efficiency of the structure to loading

    2. Design Variables
      • Loading on the frame
      • Column member size (capacity)
      • Material properties such as moment of inertia, area, etc
      • Dimensions of frame
    3. Evaluators
      • Structural efficiency (Demand Capacity Ratio)
      • Cost
    4. Most Important Tradeoffs to Consider
      • Cost vs Structural efficiency
  • Sustainability/Structural Engineering
    1. Choice of wind turbine dimensions to maximize energy generation, minimize cost to construct (materials and labor), as well as maximize local community acceptance.

    2. Design Variables
      • Tower height
      • Tower radius
      • Distance from shore (modeled as masses or attractor points)
    3. Evaluators
      • Total cost to build and maintain over 20 years (average lifespan of an offshore turbine)
      • Renewable Energy generation
      • Custom numerical metric determining inhabitant happiness based on how far the turbine is located offshore
    4. Most Important Tradeoffs to Consider
      • Cost vs local community acceptance
      • Cost vs energy generation
  • Sustainability
    1. Choice of building floor plan dimensions to maximize natural ventilation and airflow throughout the building during peak heating and cooling times

    2. Design Variables
      • Airflow
      • Floor plan dimensions
    3. Evaluators
      • Heating Loads
      • Cooling Loads
      • Inhabitant Comfort
      • Cost
    4. Most Important Tradeoffs to Consider
      • Cost vs Inhabitant Comfort
      • Cost vs Energy Efficiency
      • Upfront Cost vs Cost Savings from implementation

Step 2 - Generative Design Study

For my study, I chose to simplistically model an offshore wind turbine. I wanted to explore how different metrics can be used to show the tradeoffs associated with building an offshore wind turbine. Coming from the East Coast, I used several common problems posed about offshore wind farms that I have seen as well as learned about in my implementation of my generative design study. One common issue with implementing wind farms offshore is that they are generally seen as unaesthetically appealing and many people claim that seeing the turbines in the distance ruin views of the ocean and lower market value for their properties. This local acceptance (or lack there of) is often an initial roadblock before offshore wind farms can even undergo the additionally complex logistic and financial process of buying land and bidding on projects. I chose to model this idea as shown below.

  • Objective: Choice of wind turbine dimensions to maximize energy generation, minimize cost to construct (materials and labor), as well as maximize local community acceptance.
  • Model: For the model, I will use a simple tube shape to model the wind turbine, and I will neglect the typical tapering of a wind turbine for this activity. The two main metrics going into the design of the turbine will be the height and the radius. I chose to make my radius constant and only change the height as a variable for optimization. When designing my evaluations using my variables, I will assume that a taller and larger tower will generate more renewable energy, and that the tower’s center point being a closer distance to shore (either designated as rectangular masses in a line or an attractor point), is lower cost to construct and maintain but less accepted by the local community because it will ruin the views from the coastline.
  • Design Variables:
    • Wind turbine height (ft)
    • Distance from shore in the x direction (miles)
    • Distance from shore in the y direction (miles)
  • Constants:
    • Cost/MW of energy capacity ( more capacity is more expensive)
      • Estimated as $1.3 million per megawatt (MW) of electricity-producing capacity
    • Energy Capacity in MW per height in feet
      • Roughly estimated as 0.014 MW per foot of height based on GE Haliade-X which can be 853 ft tall and offer up to 12MW of energy generation
    • Cost/hr of labor
      • Assumed to increase by a quarter million for every mile further from shore
  • Evaluators:
    • Total Cost of Materials, Building, and Labor ($)
    • Energy Generation (MW)
    • Local community acceptance (create custom evaluation for a numerical metric that designates how accepting the local community would be to this design)
    • Key tradeoffs:

      Higher costs from building the turbine further from shore, but is more accepted by the community the further away it is

      Higher costs from building the turbine taller, but will have higher energy capacity

Step 3 - Generative Design Study Results


Building Costs: Minimize

Turbine Energy Capacity: Maximize

Local Community Acceptance: Maximize

  • Tradeoff:
    • Turbine Height/Energy Capacity vs Cost of Turbine vs Local Acceptance

The height of the turbine determines both the energy capacity of the turbine as well as affects the cost of the building and materials. In order to have high turbine pile heights, and therefore more energy generation, then there is a higher cost associated. In the graph below, the relationship is shown between cost, energy capacity, height, and local acceptance of each design. As shown, in order to gain local acceptance for the project the local acceptance score must be above 1, ruling out several of the options below the filter (red empty circles). The remaining options showcase the tradeoff between a high energy generation capacity (farther to the right) and the cost (larger circles). As shown, there is a general trend that larger costs are associated with higher energy generation as well as turbine height (darker colors are taller turbines). However, this graph is helpful in that it shows that it is possible to find economical options while still meeting energy needs and turbine height regulations.


The dots smallest in size, farthest to the right, and closest to the base line of 1 (lowest on the y axis) for local acceptance are the most optimal. This would result in relatively low costs, high energy capacities, and would still be accepted by the public as they are above the cut off of a score of 1. An owner has the choice of a slightly less expensive option (slightly smaller dot) for slightly less energy generation (closer to 0 on the x axis). For a slightly larger investment, more energy can be generated. The flexibility in options allows the owner or bidder to take into account other factors such as soil conditions, wave heights, and wind speeds for that area. While it is desirable to lower costs for an owner/bidder, a designer must balance the energy capacity needed by the grid the turbine will be servicing as well as the desires of the people who live in the area that will be affected by the implementation of the turbine. For example, for a certain desired energy capacity, there are a multitude of options of different costs (following the same location on the x axis vertically). This optimization scheme shows how different options are available to find a middle ground between these three metrics.