Predicting Peak Pressures from Computed CFD Data and Artificial Neural Networks Algorithm

淡江大學機構典藏 > 工學院 > 土木工程學系暨研究所 > 期刊論文 > Item 987654321/70129

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题名:	Predicting Peak Pressures from Computed CFD Data and Artificial Neural Networks Algorithm
作者:	Chang, Cheng‐Hsin;Shang, Neng‐Chou;Wu, Cho‐Sen;Chen, Chern‐Hwa
贡献者:	淡江大學土木工程學系
关键词:	CFD;artific neural networks;wind loads;wind tunnel
日期:	2008-01
上传时间:	2013-07-11 11:49:01 (UTC+8)
出版者:	Abingdon: Taylor & Francis
摘要:	The goal of this paper is to predict the peak pressure coefficients by combining two simulation models, steady‐state Reynolds averaged CFD model and Artificial Neural Networks (ANN). Many previous studies have shown that CFD can predict mean pressure coefficients, Cp well if inlet profiles, grid adaptation and the turbulent model are well chosen. However, the design codes for wind loads are based on peak pressure coefficients in wind tunnel experiments. The combination of two simulation methods, CFD and ANN, allows us to predict the peak pressure coefficients. The peak surface pressure values on master WERFL models inside urban street canyons are determined by the prognostic model FLUENT using the k‐epsilon turbulence model and Artificial Neural Networks algorithm. The results are compared against fluid modeling from wind tunnel tests.
關聯:	Journal of the Chinese Institute of Engineers=中國工程學刊 31(1), pp.95-103
DOI:	10.1080/02533839.2008.9671362
显示于类别:	[土木工程學系暨研究所] 期刊論文

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