论文标题
通过卷积神经网络识别流体动力不稳定
Identification of hydrodynamic instability by convolutional neural networks
论文作者
论文摘要
由于不同类型的流动运动发生了巨大的机械和热力学变化，流体动力不稳定性的发作在行业和日常生活中都非常重要。在本文中，现代机器学习技术，尤其是卷积神经网络（CNN），用于确定流体动力不稳定性提出的不同流动运动之间的过渡，以及用于表征此转运的关键非二量化参数。 CNN不仅可以正确预测泰勒 - 库特（TC）流量和雷利·贝纳德（RB）对流的临界过渡值，而且在各种设置和条件下都表现出了出色的性能。此外，主成分分析揭示了用于分类不同流动模式的关键空间特征。
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.
