Developments in coarse-grain modeling of transient heat-flow in buildings

被引:1
作者
Flood, Ian [1 ]
Abi-Shdid, Caesar
Issa, Raja R. A.
Kartam, Nabil
机构
[1] Univ Florida, Coll Design Construct & Planning, Rinker Sch, Gainesville, FL 32611 USA
[2] Kuwait Univ, Dept Civil Engn, Safat 13060, Kuwait
关键词
Energy consumption; Finite element method; Grains material; Heat flow; Neural networks;
D O I
10.1061/(ASCE)0887-3801(2007)21:5(379)
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页码:379 / 382
页数:4
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