CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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
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 sravi@design.upenn.edu
full text file.pdf (64,555 bytes)
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