Prediction of laminar vortex shedding over a cylinder using deep learning

2017/12/21 by Sang-Seung Lee, Lee, Sangseung, Donghyun You +1 · 1 citation
Engineering · Physics and Astronomy · #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Fluid Dynamics and Turbulent Flows #Fluid Dynamics and Vibration Analysis #Model Reduction and Neural Networks

paper · pdf · doi:10.48550/arxiv.1712.07854

openalex publication_date 2017/12/21 · openalex created_date 2018/01/05 · openalex updated_date 2026/07/28

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.

Citations

• Adam: A Method for Stochastic Optimization
• An Efficient Deep Learning Technique for the Navier-Stokes Equations: Application to Unsteady Wake Flow Dynamics

Cited by

• Predicting Onflow Parameters Using Transfer Learning for Domain and Task Adaptation
• Convolutional Long Short-Term Memory Neural Networks Based Numerical Simulation of Flow Field

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