Identification of hydrodynamic instability by convolutional neural networks

Wuyue Yang Zhou Pei-Yuan Center for Applied Mathematics, Tsinghua University, Beijing, 100084, China Liangrong Peng College of Mathematics and Data Science, Minjiang University, Fuzhou, 350121, China Yi Zhu Zhou Pei-Yuan Center for Applied Mathematics, Tsinghua University, Beijing, 100084, China Liu Hong Zhou Pei-Yuan Center for Applied Mathematics, Tsinghua University, Beijing, 100084, China

arXiv subject: Computational Physics (physics.comp-ph) mathscidoc:2204.49001

2022.2
The onset of hydrodynamic instabilities is of great importance in both industry and daily life, due to the dramatic mechanical and thermodynamic changes for different types of flow motions. In this paper, modern machine learning techniques, especially the convolutional neural networks (CNN), are applied to identify the transition between different flow motions raised by hydrodynamic instability, as well as critical non-dimensionalized parameters for characterizing this transit. CNN not only correctly predicts the critical transition values for both Taylor-Couette (TC) flow and Rayleigh- BĂ©nard (RB) convection under various setups and conditions, but also shows an outstanding performance on robustness and noise-tolerance. In addition, key spatial features used for classifying different flow patterns are revealed by the principal component analysis.
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@inproceedings{wuyue2022identification,
  title={Identification of hydrodynamic instability by convolutional neural networks},
  author={Wuyue Yang, Liangrong Peng, Yi Zhu, and Liu Hong},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220422142041835963099},
  year={2022},
}
Wuyue Yang, Liangrong Peng, Yi Zhu, and Liu Hong. Identification of hydrodynamic instability by convolutional neural networks. 2022. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220422142041835963099.
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