Design Decision 1: Building Facade Design
Objective
The primary goal for this design decision is to create a building facade that optimizes natural lighting while minimizing energy consumption and material costs. This involves finding the ideal balance between the window-to-wall ratio, the depth of external shading devices, and the types of materials used.
Design Variables
1. Window-to-Wall Ratio
This variable determines the proportion of the facade that is covered by windows versus solid wall. A higher ratio increases natural lighting but can also increase energy consumption due to heat loss or gain.
2. Shading Depth
This variable specifies how far the shading devices (like overhangs or louvers) extend from the facade. Deeper shading can reduce heat gain and glare, improving energy efficiency.
3. Material Type
This includes different materials for the facade, such as glass, brick, or composite panels. Each material has different thermal properties, costs, and aesthetic qualities.
Evaluators
1. Energy Consumption
Measured in terms of heating and cooling loads, this output evaluates how much energy is required to maintain a comfortable indoor environment. It is influenced by the window-to-wall ratio and shading depth.
2. Natural Lighting
Measured in lux (a unit of illuminance), this output assesses the amount of natural light penetrating the building, which can improve occupant comfort and reduce the need for artificial lighting.
3. Material Cost
This output calculates the total cost of materials used for the facade, considering both the type and quantity of materials.
In my generative design study, I used the Galapagos evolutionary solver in Grasshopper to optimize a building facade by balancing three key criteria: minimizing energy consumption and material cost while maximizing natural lighting. I configured a gene pool to vary the window-to-wall ratio and shading depth and created a total fitness function that weighted and combined these criteria. The function was structured to decrease with lower energy consumption and material cost and increase with higher natural lighting. The optimization process converged to a consistent optimal solution, suggesting an effective balance of these trade-offs. The next steps involve reinstating the best solution, evaluating its performance, and adjusting weights if necessary to ensure the design aligns with my project goals.