Efficient prediction of aerodynamic forces in rarefied flow using convolutional neural network based multi-process method

Efficient prediction of aerodynamic forces in rarefied flow using convolutional neural network based multi-process method

The direct simulation Monte Carlo (DSMC) is a widely used approach for studying aerodynamics effects of rarefied flows, but it is highly time-consuming and may exhibit statistical fluctuations. In this study, we propose an efficient aerodynamic prediction method based on convolutional neural network...

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Bibliographic Details
Main Authors: Haifeng Huang, Guobiao Cai, Chuanfeng Wei, Baiyi Zhang, Xiang Cui, Yongjia Zhao, Huiyan Weng, Weizong Wang, Lihui Liu, Bijiao He
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Machine Learning: Science and Technology
Subjects:
rarefied flow
direct simulation Monte Carlo
aerodynamic forces
deep learning
convolutional neural networks
Online Access:https://doi.org/10.1088/2632-2153/addf10
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https://doi.org/10.1088/2632-2153/addf10

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