Prediction of laminar vortex shedding over a cylinder using deep learning

TL;DR

This paper demonstrates that a generative adversarial network can accurately predict unsteady laminar vortex shedding over a cylinder, offering a promising alternative to traditional Navier-Stokes simulations.

Contribution

The study introduces a deep learning approach using GANs to predict flow fields in laminar vortex shedding, extending predictions to unseen Reynolds numbers.

Findings

GAN predictions closely match numerical simulations

Flow fields are accurately predicted at unseen Reynolds numbers

Deep learning can potentially replace direct Navier-Stokes solutions

Abstract

Unsteady laminar vortex shedding over a circular cylinder is predicted using a deep learning technique, a generative adversarial network (GAN), with a particular emphasis on elucidating the potential of learning the solution of the Navier-Stokes equations. Numerical simulations at two different Reynolds numbers with different time-step sizes are conducted to produce training datasets of flow field variables. Unsteady flow fields in the future at a Reynolds number which is not in the training datasets are predicted using a GAN. Predicted flow fields are found to qualitatively and quantitatively agree well with flow fields calculated by numerical simulations. The present study suggests that a deep learning technique can be utilized for prediction of laminar wake flow in lieu of solving the Navier-Stokes equations.