Published December 26, 2021 | Version SCDA
Software Open

URBiiLAB/SDCA: SDCA

Authors/Creators

  • 1. Southern Illinois University
  • 1. ROR icon Southern Illinois University Carbondale
  • 2. ROR icon University of Washington

Description

SDCA TOOL; Optimization Tool for Solar-driven Design Using Cellular Automata (SDCA)

 

Synopsis

The SDCA (Solar-Driven Cellular Automata) tool is designed to assist architects in maximizing solar radiation incident (SRI) on building envelopes to enhance solar electricity generation through photovoltaic (PV) technology.

Leveraging parametric optimization and generative cellular automata, the script automatically produces a wide range of three-dimensional volumetric configurations, enabling rapid and intuitive exploration of building forms during the early design stages. It is computationally efficient and capable of generating suggestive architectural massing quickly and effectively.

The SDCA tool is highly flexible, allowing users to adjust parameters such as:

  • Geographic location

  • Building height

  • Presence of surrounding structures

  • Time periods for solar optimization

  • Granularity of volumetric divisions

Developed in Grasshopper, the script includes comprehensive documentation and examples to facilitate ease of use and encourage broader implementation. It integrates various established tools and libraries, including Ladybug, Anemone, and Rabbit.

Please note:

  • The SDCA tool is not open-source; users must obtain explicit permission to access or modify the code.

  • No fees are charged for academic-use subscriptions.

  • To request access or further information, please contact us via the email provided at www.urbiilab.com

Authors: Seth Luitjohan, MA Mehdi Ashayeri, PHD Narjes Abbasabadi, PHD,

Other

References

Luitjohan, S., Ashayeri, M., & Abbasabadi, N. (2022). An Optimization Framework And Tool For Context-Sensitive Solar-Driven Design Using Cellular Automata (SDCA). 2022 Annual Modeling and Simulation Conference (ANNSIM), 593–604. https://doi.org/10.23919/ANNSIM55834.2022.9859496

Files

URBiiLAB/SDCA-SCDA.zip

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