id |
caadria2005_b_4a_a |
authors |
Ravi S. Srinivasan, Ali M. Malkawi |
year |
2005 |
title |
Reinforcement Learning and Real-time Building Thermal Performance Data Visualization |
source |
CAADRIA 2005 [Proceedings of the 10th International Conference on Computer Aided Architectural Design Research in Asia / ISBN 89-7141-648-3] New Delhi (India) 28-30 April 2005, vol. 2, pp. 141-148 |
doi |
https://doi.org/10.52842/conf.caadria.2005.141
|
summary |
Computational Fluid Dynamics (CFD) simulations are used to predict the fluid behavior and particle systems-in-action in three-dimensional space, allowing experts to evaluate a series of environmental decisions in designing buildings. Although computing power has increased in the past decade, detailed CFD simulations introduce time-delay that defeats the notion of real-time data visualization. A method that can bypass the time-consuming simulations and generate results comparable to detailed CFD simulations will allow such visualizations to be constructed. This paper discusses a pilot project that utilizes a Reinforcement Learning (RL) algorithm coupled with a simplified fluid dynamics equation to generate thermal performance data for real-time visualization. |
series |
CAADRIA |
email |
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full text |
file.pdf (64,555 bytes) |
references |
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last changed |
2022/06/07 08:00 |
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