A novel hybrid deep neural network architecture is designed to capture the spatial-temporal features of unsteady flows around moving boundaries directly from high-dimensional unsteady flow fields data. The hybrid deep neural network is constituted by the convolutional neural network (CNN), improved convolutional Long-Short Term Memory neural network (ConvLSTM) and deconvolutional neural network (DeCNN). Flow fields at future time step can be predicted through flow fields by previous time steps and boundary positions at those steps by the novel hybrid deep neural network. Unsteady wake flows around a forced oscillation cylinder with various amplitudes are calculated to establish the datasets as training samples for training the hybrid deep neural networks. The trained hybrid deep neural networks are then tested by predicting the unsteady flow fields around a forced oscillation cylinder with new amplitude. The effect of neural network structure parameters on prediction accuracy was analyzed. The hybrid deep neural network, constituted by the best parameter combination, is used to predict the flow fields in the future time. The predicted flow fields are in good agreement with those calculated directly by computational fluid dynamic solver, which means that this kind of deep neural network can capture accurate spatial-temporal information from the spatial-temporal series of unsteady flows around moving boundaries. The result shows the potential capability of this kind novel hybrid deep neural network in flow control for vibrating cylinder, where the fast calculation of high-dimensional nonlinear unsteady flow around moving boundaries is needed.
Unified Gas-Kinetic Scheme for Unsteady Multiscale Flows with Moving Boundaries
Hong Kong University of Science and Technology（香港科技大学）；Department of Mathematics, Hong Kong University of Science and Technology（香港科技大学数学系）；Department of Mechanical and Aerospace Engineering, Hong Kong University of Science and Technology（香港科技大学机械与航空航天工程系）；Shenzhen Research Institute, Hong Kong University of Science and Technology（香港科技大学深圳研究院）
Minimization-based polynomial corrections for high-order curved boundaries on fixed and moving domains: assessment on finite volume and discontinuous Galerkin schemes
Université Paris Cité and Sorbonne Université, CNRS, LJLL（巴黎西岱大学与索邦大学，法国国家科学研究中心，拉格朗日分析与应用数学实验室）；Laboratoire de Mathématiques, Université Savoie Mont Blanc, CNRS, UMR 5127（萨瓦-蒙布朗大学数学实验室，法国国家科学研究中心）；Department of Mathematics and Computer Science, University of Ferrara（费拉拉大学数学与计算机科学系）
