期刊論文
| 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 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/70129 ) |