Toward neural-network-based large eddy simulation: application to turbulent flow over a circular cylinder

A neural-network(NN)-based large eddy simulation is conducted for flow over a circular cylinder. We propose NN models with a fusion layer in addition to consecutive hidden layers. The input variables are the grid- and test-filtered strain rate or velocity gradient, and the output is the subgrid-scale (SGS) stresses. The training data are from a direct numerical simulation of flow over a circular cylinder at Re=3,900 based on the free-stream velocity and cylinder diameter. The trained SGS models are evaluated in a priori and a posteriori tests under the trained flow condition and show a slightly better prediction than physics-based SGS models such as the dynamic Smagorinsky model. The SGS models are also applied to higher Reynolds number flows at Re=5,000 and 10,000, and accurately predict the flow statistics.