Identification of hydrodynamic instability by convolutional neural networks

TL;DR

This paper demonstrates that convolutional neural networks can accurately identify hydrodynamic instabilities and critical transition parameters in complex flow systems, showing robustness and interpretability in flow pattern classification.

Contribution

The study applies CNNs to detect flow transitions and critical parameters in hydrodynamic instability, highlighting improved accuracy, robustness, and interpretability over traditional methods.

Findings

CNN accurately predicts transition points in TC and RB flows

CNN demonstrates robustness and noise tolerance

Principal component analysis reveals key spatial features

Abstract

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- B\'enard (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.