TECHNICAL NOTES
Sep 1, 2007

Developments in Coarse-Grain Modeling of Transient Heat-Flow in Buildings

Publication: Journal of Computing in Civil Engineering
Volume 21, Issue 5

Abstract

The note reports on recent developments to the coarse-grain method (CGM) of modeling transient heat flow in buildings. CGM was originally developed as an alternative to conventional fine-grain modeling techniques [such as the finite-difference method (FDM) and finite-element method (FEM)] to increase simulation speed to a degree that facilitates three-dimensional modeling, and to ease the tasks of model development and experimentation. Earlier work has shown that CGM can provide reasonably accurate simulations at a processing speed several orders of magnitude faster than FDM or FEM. This note describes and demonstrates refinements to the CGM approach that increase its modeling accuracy to a level comparable to FEM, while doubling its processing speed. These refinements are: (1) the use of a hybrid linear regression model with an artificial neural network (ANN) to represent each coarse-grain modeling element (the hybridization of the ANN effectively halves its complexity); and (2) a linear calibration of the ANN-based coarse-grain modeling elements to account for an observed positive bias in their predictions. The improved approach is demonstrated for a two-dimensional model of a bay in a research building located at the University of Florida. The note concludes with some suggestions for continuing research.

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References

Abi-Shdid, C. (2005). “ANN coarse-grain based-model for simulating two-dimensional heat transfer in buildings.” Ph.D. dissertation submitted in partial fulfillment for the degree of Doctor of Philosophy, Univ. of Florida, Gainesville, Fla.
Ensley, D., and Nelson, D. E. (1992). “Extrapolation of Mackey-Glass data using cascade correlation.” Simulation, 58(5), 333–339.
Flood, I., Issa, R. R. A., and Abi-Shdid, C. (2004). “Simulating the thermal behavior of buildings using artificial neural networks-based coarse-grain modeling.” J. Comput. Civ. Eng., 18(3), 207–214.
Moody, J., and Darken, C. J. (1989). “Fast learning in networks of locally-tuned processing units.” Neural Comput., 1, 281–294.

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Information

Published In

Go to Journal of Computing in Civil Engineering
Journal of Computing in Civil Engineering
Volume 21Issue 5September 2007
Pages: 379 - 382

History

Received: Aug 31, 2005
Accepted: Mar 13, 2006
Published online: Sep 1, 2007
Published in print: Sep 2007

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Authors

Affiliations

Ian Flood
Rinker School, College of Design, Construction and Planning, Univ. of Florida, Gainesville, FL 32611-5703.
Caesar Abi-Shdid
Rinker School, College of Design, Construction and Planning, Univ. of Florida, Gainesville, FL 32611-5703.
Raja R. A. Issa
Rinker School, College of Design, Construction and Planning, Univ. of Florida, Gainesville, FL 32611-5703.
Nabil Kartam
Dept. of Civil Engineering, Kuwait Univ., P.O. Box 5969, Safat 13060, Kuwait.

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