Application of hybrid neural network in data-driven flow field simulation

Xiaowei Zhang; Wentao Dong; Wenshi Wang; Ziyu Zhou; Yucai Dong

doi:10.1117/12.2673552

3 April 2023 Application of hybrid neural network in data-driven flow field simulation

Xiaowei Zhang, Wentao Dong, Wenshi Wang, Ziyu Zhou, Yucai Dong

Proceedings Volume 12599, Second International Conference on Digital Society and Intelligent Systems (DSInS 2022); 125991W (2023) https://doi.org/10.1117/12.2673552
Event: 2nd International Conference on Digital Society and Intelligent Systems (DSInS 2022), 2022, Chendgu, China

Abstract

Due to the strong nonlinearity of navier stokes equation, it is difficult to solve the hydrodynamics simulation problem. As a century problem, it is still a major difficulty in the academic community. With the improvement of computer ability and the development of data platform, some new changes have taken place in the research direction and content of turbulence model. The data-driven machine learning method is different from the traditional approximate equation solving method in physics, and shows its application potential in highly complex flow fields. In this study, convolution cyclic hybrid neural network is used to predict the complex flow field, and the generated confrontation network is used to generate the simulated flow field.

Citation Download Citation

Xiaowei Zhang, Wentao Dong, Wenshi Wang, Ziyu Zhou, and Yucai Dong "Application of hybrid neural network in data-driven flow field simulation", Proc. SPIE 12599, Second International Conference on Digital Society and Intelligent Systems (DSInS 2022), 125991W (3 April 2023); https://doi.org/10.1117/12.2673552

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