A Low-Dimensional Learning Model via Convolutional Neural Networks for Unsteady Wake-Body Interaction

This paper is concerned with the development of a physical model by learning low-dimensional approximation for laminar wake-body interaction systems. Of particular interest is to predict the long time series of unsteady flow dynamics using the learned low-dimensional model. We consider convolutional neural networks (CNN) for the learning dynamics of wake-body interaction, which assemble layers of linear convolutions with nonlinear activations to automatically extract the low-dimensional features. Using high-fidelity time series data from the stabilized finite element Navier-Stokes solver, we first project the dataset to a low-dimensional subspace using proper orthogonal decomposition (POD). The time-dependent coefficients of the POD subspace are mapped to the flow field via a CNN with nonlinear rectification, and the CNN is iteratively trained using the stochastic gradient descent method to predict the POD time coefficient when a new flow field is fed to it. The time-averaged flow field, the POD basis vectors and the trained CNN are used to predict the long time series of the flow fields and results are compared with the full-order (high-dimensional) simulation results. POD-CNN based predictions maintain a remarkable accuracy in the entire fluid domain including the highly nonlinear near wake region for the long time series. The proposed POD-CNN model based on data-driven approximation has a profound impact on the predictive analysis of unsteady wake flow and fluid-structure interaction.