Identification of hydrodynamic instability by convolutional neural networks
This work addresses the identification of flow transitions for applications in industry and daily life, representing an incremental application of existing methods to a new domain.
The paper tackled the problem of identifying hydrodynamic instability transitions in fluid flows using convolutional neural networks (CNNs), achieving correct predictions of critical transition values for Taylor-Couette flow and Rayleigh-Bénard convection with robustness and noise-tolerance.
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- Bénard (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.