Application of Physics-Informed Neural Networks Algorithm to Predict the Vorticity of a Moving Cylindrical Flow Field

Abstract

Turbulence is a typical physical phenomenon which is involved in many engineering fields. The combination of machine learning and turbulence modeling is an emerging research direction in the field of fluid mechanics. The current achievements in this research direction have strongly verified its feasibility and indicated a positive prospect for the application of machine learning for the turbulence modeling. Machine learning can help discover models of complex dynamical systems from the data directly. In this work, we apply the machine learning algorithm called the physics-informed neural networks (PINNs) to predict the vorticity of a moving cylindrical flow field. Through the neural network method based on physical information, a neural network model is established to simulate the flow around a moving cylinder. Results demonstrate that the vorticity predicted by PINNs algorithm are in good agreement with the benchmark results.