Large eddy simulation of flow over a circular cylinder using a neural-network-based subgrid-scale model and its application to complex turbulent flows.

A neural-network(NN)-based subgrid-scale (SGS) models are constructed for turbulent flow over a circular cylinder, for the long-term purpose of applying them to turbulent flow over/inside complex geometries. The filtered DNS data at Re_d=Ud/ν=3900 are used for training the NNs, where U is the free-stream velocity, d is the cylinder diameter, and ν is the kinematic viscosity. Various input variables and NN architectures are considered while keeping their output as the SGS stresses: for example, NN with and without fusion, test-filtered variables as well as grid-filtered variables as inputs, etc. The NN architecture with fusion shows good predictions for flow over a circular cylinder even with different grid resolutions and at higher Reynolds numbers than those of the trained conditions. We also show that the normalization of inputs and output with the free-stream velocity and cylinder diameter is not applicable to untrained geometries. Therefore, various normalizations of input variables are considered to construct general NN-based SGS models for more complex flows, and their results will be discussed at the presentation.