Toward machine-learning-based large eddy simulation of flow over a complex geometry

Our purpose is to develop a machine-learning-based subgrid-scale model that can be applied to large eddy simulation (LES) of flow over/inside a complex geometry. We conduct direct numerical simulation (DNS) of flow over a circular cylinder at the Reynolds number of 3900, and use filtered DNS data for training of the neural network (NN). The trained NN is applied not only to the trained flow at higher Reynolds number but also to untrained flows such as flows over a backward-facing step and an airfoil. LES of the trained flow at 2-3 times higher Reynolds numbers provides good predictions. For the application to untrained flow, various aspects including modification of NN architecture, choice of input variable, and normalization method are considered, and they are discussed at the presentation.