Predicting Peak Pressures from Computed CFD Data and Artificial Neural Networks Algorithm
學年 96
學期 1
出版（發表）日期 2008-01-01
作品名稱 Predicting Peak Pressures from Computed CFD Data and Artificial Neural Networks Algorithm
作品名稱（其他語言）
著者 Chang, Cheng‐Hsin; Shang, Neng‐Chou; Wu, Cho‐Sen; Chen, Chern‐Hwa
單位 淡江大學土木工程學系
出版者 Abingdon: Taylor & Francis
著錄名稱、卷期、頁數 Journal of the Chinese Institute of Engineers=中國工程學刊 31(1), pp.95-103
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.
關鍵字 CFD;artific neural networks;wind loads;wind tunnel
語言 en
ISSN 0253-3839 2158-7299
期刊性質 國外
收錄於 EI
產學合作
通訊作者 Chang, Cheng‐hsin
審稿制度
國別 GBR
公開徵稿
出版型式 紙本 電子版
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