Project Proposal:

Maximizing preserved sunlight by specific design on pneumatic system controlling solar panels

(Design process is a parametric design based on Genetic Algorithm)

 

Abstract:

The goal for this project is to find a design for a facade with solar panels with such that solar panels overshadow each other at a minimum and together receive the maximal amount of sunlight and if required the maximal amount of natural light be allowed to infiltrate the building. This will be done by optimizing the parameters of the design, such as the amount of air pressure in components on the facade, number of components on the facade, layout of components on the facade, number of solar cells on components and positioning of solar cells. This optimization will be done using Genetic Algorithms. Based on the goal, the “fitness function” should be calculated using various factors but must include panel rotation, location number and possibly component number and component position. These Genetic Algorithms should take in account the angle of the Sun over the course of the day for any particular day at any location on Earth, when fully implemented.

 

Introduction:

One of the main problems in architecture is the level of complexity involved in most building projects. Genetic algorithms offer an effective solution to this problem by selecting parameters and optimizing those parameters, operating on a population of possible solutions.Generally in architecture and specifically in this project, GAs will be utilized in two ways:- As an Optimization Tool- As a Form-generation ToolIn the first part, the GA will optimize well defined building problems such thermal and lighting performances. In the second part, GA is used under the scope of the concept of “Emergence". So an attempt is will be made on selecting emergent behavior such that the building façade identified as optimized in collecting solar energy with panels and maximizing natural light for the interior parts of the building.In fact, there are two types of objectives in this project. “Objective Function” that defines the fitness function and “Design Objective” that is not explicitly implemented in fitness function but is used in different steps of calculation. Here, optimizing the air pressure is part of the Objective Function, through maximizing the amount of light for interior space and energy collected by solar panels.

 

Optimization and Form-Generating by using GAs:

Optimization and form-generation processes via implementation of GA, will happen in two scopes, consideration of each floor height (that in tall buildings with usual function is around 3.50 -4.50 meters) and total height of the building. So, the following parameters should be evaluated in the aforementioned scopes, though it may be infeasible to optimize all the following parameters.

- amount of inflation for each component in relation with sunlight

- number of inflated component in each level

- number and size of solar panels upon each component

- different function of interior spaces in building and their different need for natural light

- different amount of sunlight received by panels by the building due to climate in the area

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Picture1 analysis of a single pneumatic component via different inflations and number of solar panels Diagrammatic presentation of a single pneumatic component installed on façade and its responsiveness to the sunlight – main point is that although panels are working like normal vector on inflated surface, their rotation angles determine how much solar energy is collected in panels and how much natural light goes inside the interior spaces.

Picture2: Cross section of one floor- optimizing the number and size of inflated components and solar panels upon them.

 

Emergence of the whole pattern of façade in an optimized way:

Based on the results of GA, all the components on the whole of the building façade will be an optimal configuration. The attempt is that the configurations found will use complex and/or random behavior to be improved. The goal is using this behavior to find aesthetically and functionally optimal configurations of all mentioned parameters.

 

How GA is implemented in this project:

Software:

The GA’s will either be implemented in Python and results will be visualized in grasshopper (plug-in in Rhino Ceros) or will be implemented in grasshopper and some of its add-on plug-ins like “Geco” and “Galapagos” will allow work on genetic algorithms and climatic analysis.The development will iteratively add parameters to the design solutions, creating a more complete and accurate modelling of the design. This will allow for complexity to be accounted for bit by bit without forcing the model to be too complicated to be verified and debugged.