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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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/addf10 |
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| author | Haifeng Huang Guobiao Cai Chuanfeng Wei Baiyi Zhang Xiang Cui Yongjia Zhao Huiyan Weng Weizong Wang Lihui Liu Bijiao He |
| author_facet | Haifeng Huang Guobiao Cai Chuanfeng Wei Baiyi Zhang Xiang Cui Yongjia Zhao Huiyan Weng Weizong Wang Lihui Liu Bijiao He |
| author_sort | Haifeng Huang |
| collection | DOAJ |
| description | 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 networks (CNNs) to further explore the application of deep learning in improving the efficiency of DSMC for calculating the aerodynamics of rarefied flows. The method includes centroid aerodynamics forces prediction (CFP) and surface aerodynamic forces distribution prediction (SFP), both of which are trained using a dataset of free molecular flow around obstacles derived from DSMC simulations. The SFP is designed to bridge the gap between flow field and surface forces, with two characteristics extraction methods developed specifically for this purpose. Additionally, two data preprocessing methods are designed to suppress the statistical noise inherent in DSMC simulations. Both CFP and SFP have demonstrated optimal performance in terms of accuracy and resistance to overfitting, achieving considerable predictive accuracy. The SFP exhibits a significant speedup, enabling real-time prediction of aerodynamic distributions from flow field. The results demonstrate that the proposed CNN-based approach offers a promising solution for the efficient calculation of aerodynamic forces in rarefied flows, and provide a robust foundation for ongoing development. |
| format | Article |
| id | doaj-art-14028f58dd2d4fbaaed64a3a94f89b1c |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-14028f58dd2d4fbaaed64a3a94f89b1c2025-08-20T03:25:26ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016202506010.1088/2632-2153/addf10Efficient prediction of aerodynamic forces in rarefied flow using convolutional neural network based multi-process methodHaifeng Huang0https://orcid.org/0009-0005-9120-7959Guobiao Cai1Chuanfeng Wei2Baiyi Zhang3Xiang Cui4Yongjia Zhao5Huiyan Weng6Weizong Wang7https://orcid.org/0000-0002-6022-1441Lihui Liu8https://orcid.org/0000-0002-3752-038XBijiao He9https://orcid.org/0000-0002-6346-7981School of Astronautics, Beihang University , Beijing 100191, People’s Republic of China; Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education , Beijing 100191, People’s Republic of ChinaSchool of Astronautics, Beihang University , Beijing 100191, People’s Republic of China; Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education , Beijing 100191, People’s Republic of ChinaChina Satellite Network System Co., Ltd , Beijing 100029, People’s Republic of ChinaSchool of Astronautics, Beihang University , Beijing 100191, People’s Republic of China; Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education , Beijing 100191, People’s Republic of ChinaChina Satellite Network System Co., Ltd , Beijing 100029, People’s Republic of ChinaChina Satellite Network System Co., Ltd , Beijing 100029, People’s Republic of ChinaSchool of Astronautics, Beihang University , Beijing 100191, People’s Republic of China; Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education , Beijing 100191, People’s Republic of ChinaSchool of Astronautics, Beihang University , Beijing 100191, People’s Republic of China; Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education , Beijing 100191, People’s Republic of China; Aircraft and Propulsion Laboratory, Ningbo Institute of Technology, Beihang University , Ningbo 315832, People’s Republic of ChinaSchool of Astronautics, Beihang University , Beijing 100191, People’s Republic of China; Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education , Beijing 100191, People’s Republic of ChinaSchool of Astronautics, Beihang University , Beijing 100191, People’s Republic of China; Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education , Beijing 100191, People’s Republic of ChinaThe 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 networks (CNNs) to further explore the application of deep learning in improving the efficiency of DSMC for calculating the aerodynamics of rarefied flows. The method includes centroid aerodynamics forces prediction (CFP) and surface aerodynamic forces distribution prediction (SFP), both of which are trained using a dataset of free molecular flow around obstacles derived from DSMC simulations. The SFP is designed to bridge the gap between flow field and surface forces, with two characteristics extraction methods developed specifically for this purpose. Additionally, two data preprocessing methods are designed to suppress the statistical noise inherent in DSMC simulations. Both CFP and SFP have demonstrated optimal performance in terms of accuracy and resistance to overfitting, achieving considerable predictive accuracy. The SFP exhibits a significant speedup, enabling real-time prediction of aerodynamic distributions from flow field. The results demonstrate that the proposed CNN-based approach offers a promising solution for the efficient calculation of aerodynamic forces in rarefied flows, and provide a robust foundation for ongoing development.https://doi.org/10.1088/2632-2153/addf10rarefied flowdirect simulation Monte Carloaerodynamic forcesdeep learningconvolutional neural networks |
| spellingShingle | Haifeng Huang Guobiao Cai Chuanfeng Wei Baiyi Zhang Xiang Cui Yongjia Zhao Huiyan Weng Weizong Wang Lihui Liu Bijiao He Efficient prediction of aerodynamic forces in rarefied flow using convolutional neural network based multi-process method Machine Learning: Science and Technology rarefied flow direct simulation Monte Carlo aerodynamic forces deep learning convolutional neural networks |
| title | Efficient prediction of aerodynamic forces in rarefied flow using convolutional neural network based multi-process method |
| title_full | Efficient prediction of aerodynamic forces in rarefied flow using convolutional neural network based multi-process method |
| title_fullStr | Efficient prediction of aerodynamic forces in rarefied flow using convolutional neural network based multi-process method |
| title_full_unstemmed | Efficient prediction of aerodynamic forces in rarefied flow using convolutional neural network based multi-process method |
| title_short | Efficient prediction of aerodynamic forces in rarefied flow using convolutional neural network based multi-process method |
| title_sort | efficient prediction of aerodynamic forces in rarefied flow using convolutional neural network based multi process method |
| topic | rarefied flow direct simulation Monte Carlo aerodynamic forces deep learning convolutional neural networks |
| url | https://doi.org/10.1088/2632-2153/addf10 |
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