利用卷积神经网络识别水动力不稳定性
Identification of hydrodynamic instability by convolutional neural networks
摘要：
流体动力学不稳定性的发生在工业和日常生活中都是非常重要的，因为不同类型的流体运动会引起剧烈的力学和热力学变化。在本文中，现代机器学习技术，特别是卷积神经网络（CNN），应用于识别由流体动力学不稳定性引起的不同流动运动之间的过渡，以及用于表征这种过渡的关键无量纲参数。CNN不仅能正确预测各种条件下的Taylor-Couette（TC）流和Rayleigh- Bénard（RB）对流的临界转捩值，而且在鲁棒性和噪声容限方面表现出优异的性能。此外，用于分类不同的流模式的关键空间特征，揭示了主成分分析。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
