Prediction of Confined Flow Field Around a Circular Cylinder and Its Force Based on Convolution Neural Network

Abstract

In this paper, a large number of numerical simulations of the external confined flow field around a circular cylinder is conducted by using the open source library OpenLB. The boundary information of the flow field is defined by Euclidean distance function. Then, massive data, including the velocity field and the drag and lift coefficients of the cylinder, are obtained and used for model training. Subsequently, two convolutional neural network models are presented. The input ends of the two models are the boundary information of the flow field, and the output ends are the velocity field and the force on the cylinder respectively. Once the two models are established, they are used to predict the velocity and force respectively. The predicted results are in good agreement with the real values. It turns out that the convolution neural network models can be used to predict the flow field and force with complex boundaries, and it has the advantages of high accuracy and efficiency in practical engineering applications.

Keywords

• Convolutional neural network
• deep learning
• computational fluid dynamics
• OpenLB