Plenary Talk

A recursive neural-network-based subgrid scale model for large eddy simulation of turbulent flow

Haecheon CHOI

Haecheon Choi is a professor of mechanical engineering at Seoul National University, Korea since 1993. He obtained his BS (1985) and MS (1987) degrees from Seoul National University, and a Ph.D degree (1992) from Stanford University. His research areas include turbulence, flow control, CFD, and bio-mimetic engineering. He is a member of The National Academy of Engineering of Korea and The Korean Academy of Science and Technology. He is a Fellow of American Physical Society and serves as an Associate Editor of Journal of Fluid Mechanics.


A novel recursive process is introduced to a neural-network-based subgrid-scale (NN-based SGS) model for large eddy simulation of high Reynolds number turbulent flow. This process is designed to allow an SGS model to be applicable to a hierarchy of different grid sizes without requiring an expensive filtered direct numerical simulation data. We also design a neural network by modifying the bias, batch normalization and activation function for the application of an NN-based SGS model trained with one flow to another flow. The present recursive SGS model is successfully applied to isotropic turbulence and turbulent channel flow at high Reynolds numbers. Its further applications to complex turbulent flow will be also shown.