This research project explored the use of computational design methods for optimizing urban density while maintaining Required fields daylight access within a dense city block.
The study was developed during the design of a public mixed-use urban center. One of the key challenges was balancing development intensity with daylight performance across buildings, public spaces and surrounding urban fabric.
A custom generative algorithm was developed in Rhino and Grasshopper to automatically modify building heights and evaluate solar access throughout the site.
For every generation, the system:
adjusted the number of storeys of individual buildings;
calculated solar exposure and shadow impact;
evaluated daylight performance;
compared results against predefined criteria;
generated new design iterations.
Thousands of design variations were produced and evaluated automatically, allowing the system to search for configurations that maximized development potential while maintaining acceptable daylight conditions.
The research demonstrates an early application of performance-driven generative design in urban planning, combining environmental analysis with automated form generation and optimization.
Project Information
Project: Insolation-Based Urban Optimization
Year: 2016
Type: Research Project
Location: Minsk, Belarus
Tools: Rhino, Grasshopper, Ladybug, Honeybee
My Role
I developed the generative workflow, environmental analysis setup and optimization logic used to evaluate and evolve urban massing configurations based on solar performance criteria.
The system automatically generated and evaluated thousands of design iterations, using daylight performance as a primary optimization objective.
Research Focus
Generative Urban Design
Performance-Driven Design
Environmental Analysis
Solar Access Optimization
Urban Density Studies
Evolutionary Design Workflows
Computational Urbanism
Categories
Computational Design
Urban Design
Research
