English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 60861/93527 (65%)
造訪人次 : 1507390      線上人數 : 11
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/70129

    題名: 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
    貢獻者: 淡江大學土木工程學系
    關鍵詞: CFD;artific neural networks;wind loads;wind tunnel
    日期: 2008-01
    上傳時間: 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
    顯示於類別:[土木工程學系暨研究所] 期刊論文


    檔案 描述 大小格式瀏覽次數



    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋