空气动力学学报2023，Vol.41Issue(11)：116-126,11.DOI:10.7638/kqdlxxb-2022.0157
基于深度学习的超分辨率重构方法在CAARC标模绕流流场重构中的应用
Applications of deep learning-based super-resolution for reconstruction of flow around the CAARC benchmark model
摘要
Abstract
The deep-learning-based super-resolution reconstruction methods developed in recent years are effective methods to obtain detailed flow fields.A deep learning-based super-resolution reconstruction method was applied to reconstructing high-resolution wind field of flow around building structures in this paper.The super-resolution reconstruction model was based on the convolutional neural network(CNN)and combined with the mixed downsampled skip-connection multi-scale(Multi-scale CNN)model.The super-resolution reconstruction model was applied to the reconstruction of the surface pressure on and the velocity field around the CAARC benchmark model.The reconstruction ability of the deep learning-based model for different under-resolution flow fields was investigated.The results show that the proposed deep learning model can greatly enhance the super-resolution reconstruction performance and the reconstruction accuracy is better than the original convolutional neural network model and the traditional bicubic interpolation method.Due to its universal applicability,this method can be extended to super-resolution reconstruction of wind field of any building structure with complex turbulent flow.关键词
深度学习/超分辨率重构/卷积神经网络/CAARCKey words
deep learning/super-resolution reconstruction/convolutional neural network/CAARC分类
数理科学引用本文复制引用
梁仍康,张伟,杨思帆,黄刚,李朗..基于深度学习的超分辨率重构方法在CAARC标模绕流流场重构中的应用[J].空气动力学学报,2023,41(11):116-126,11.基金项目
国家自然科学基金(11902278) （11902278）
光合基金A类项目(202302015072) （202302015072）
空天飞行空气动力科学与技术全国重点实验室项目 （）
