土木与环境工程学报（中英文）2024，Vol.46Issue(1)：122-129,8.DOI:10.11835/j.issn.2096-6717.2022.025
基于深度学习的钝体断面外形气动性能高效预测方法
An efficient deep learning prediction method for aerodynamic performance based on the shape of the main beam
摘要
Abstract
The aerodynamic shape of the bluff body section is very important to the aerodynamic performance.However,it takes a lot of time to obtain the aerodynamic performance of the bluff body section using traditional wind tunnel tests and CFD simulation calculations,which greatly affects the aerodynamic performance evaluation efficiency of the bluff body section's aerodynamic shape.This paper proposes to use the deep learning technology of convolutional neural networks to realize the rapid prediction of aerodynamic performance.After the deep learning model is trained,the shape information and the shape-related flow field information can be input to output the drag coefficients under different geometric shapes,then the aerodynamic performance of the bluff body section.However to find the best deep learning model,this paper optimizes the depth and width of the convolutional neural network structure through comprehensive judgment error and time performance.The output resistance coefficient of the deep learning model is compared with the CFD calculation results.It is found that the error meets the expected requirements,and the prediction time based on the deep learning network is an order of magnitude improvement compared with the calculation time required by the traditional method.It can be used as the bluff body section aerodynamic shape optimization in the future.关键词
桥梁静风力/钝体断面/气动性能/深度学习/卷积神经网络Key words
bridge static wind force/bluff body section/aerodynamic performance/deep learning/convolutional neural network分类
建筑与水利引用本文复制引用
李少鹏,李海,李珂..基于深度学习的钝体断面外形气动性能高效预测方法[J].土木与环境工程学报（中英文）,2024,46(1):122-129,8.基金项目
国家自然科学基金(51978108) （51978108）
重庆市自然科学基金(cstc2020icyi-msxmX0773)National Natural Science Foundation of China(No.51978108) （cstc2020icyi-msxmX0773）
Natural Science Foundation of Chongqing(No.cstc2020jcyj-msxmX0773) （No.cstc2020jcyj-msxmX0773）
