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

TL;DR

This paper introduces a hybrid deep neural network architecture combining CNN, ConvLSTM, and DeCNN to accurately predict unsteady flow fields in fluid dynamics, demonstrating high agreement with CFD simulations.

Contribution

A novel hybrid deep neural network design that effectively captures spatio-temporal features of unsteady flows for improved prediction accuracy.

Findings

Predicted flow fields closely match CFD results.

The method works across different Reynolds numbers and flow configurations.

The approach offers a fast alternative to traditional CFD simulations.

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.