A new prediction method of unsteady wake flow by the hybrid deep neural network

Abstract

The fast and accurate prediction of unsteady flow becomes a serious challenge in fluid dynamics, due to the high-dimensional and nonlinear characteristics. A novel hybrid deep neural network (DNN) architecture was designed to capture the unsteady flow spatio-temporal features directly from the high-dimensional unsteady flow fields. The hybrid deep neural network is constituted by the convolutional neural network (CNN), convolutional Long Short Term Memory neural network (ConvLSTM) and deconvolutional neural network (DeCNN). The flow around a cylinder at various Reynolds numbers and the flow around an airfoil at higher Reynolds number are carried out to establish the datasets used to train the networks separately. The trained hybrid DNNs were then tested by the prediction of the flow fields at future occasions. The predicted flow fields using the trained hybrid DNNs are in good agreement with the flow fields calculated directly by the computational fluid dynamic solver.

arXiv admin note: text overlap with arXiv:1903.00033 by other authors

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Cited by in corpus (8)

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• Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data
• Transformers for Modeling Physical Systems
• Attention-Enhanced Neural Network Models for Turbulence Simulation
• Three-dimensional deep learning-based reduced order model for unsteady flow dynamics with variable Reynolds number
• Mechanisms of a Convolutional Neural Network for Learning Three-dimensional Unsteady Wake Flow
• Physics-Assisted Reduced-Order Modeling for Identifying Dominant Features of Transonic Buffet
• Generalization capabilities and robustness of hybrid models grounded in physics compared to purely deep learning models