A hybrid partitioned deep learning methodology for moving interface and fluid-structure interaction

Abstract

In this work, we present a hybrid physics-based deep learning (DL) framework for handling moving interfaces and predicting fluid-structure interaction (FSI). Using the discretized Navier-Stokes (NS) in the Arbitrary Lagrangian-Eulerian (ALE) reference frame, we generate full-order flow snapshots and point-cloud displacements as target physical data for the learning and inference of coupled fluid-structure dynamics. This integrated operation of the physics-based modeling with the DL-based reduced-order model (DL-ROM) makes our framework hybrid. The proposed multi-level framework is composed of two data-driven physics-DL drivers that predict unsteady flow and track the moving point cloud displacements respectively, while synchronously exchange the force information at the physical interface. The first component of our proposed framework relies on the proper orthogonal decomposition-based recurrent neural network (POD-RNN) as a semi-supervised procedure to infer the point cloud ALE description. This model essentially relies on the POD basis modes to reduce dimensionality and evolving them in the time domain. The second component utilizes the convolution-based recurrent autoencoder network (CRAN) as a self-supervised DL procedure to predict the nonlinear flow dynamics at static Eulerian probes. We introduce these probes as spatially structured query nodes in the moving point cloud to resolve the field Lagrangian to Eulerian conflict as well as conveniently train the CRAN driver. We design a novel snapshot-field transfer and load recovery (FTLR) algorithm to optimally select these Eulerian probes. They are selected in such a way that the two components (i.e., POD-RNN and CRAN) are constrained at the interface to recover bulk force quantities. These hybrid physics-DL drivers ultimately rely on recurrent neural networks to evolve the low-dimensional state. A popular prototypical fluid-structure interaction problem of flow past a freely oscillating cylinder is selected to test the efficacy of the proposed hybrid DL-ROM methodology. The framework tracks the interface description accurately and predicts highly non-linear wake dynamics for nearly 500 time-steps with limited input demonstrator within excellent to a reasonable accuracy. These results motivate us to further explore the application of this hybrid framework for the concept of digital twinning of engineering systems, especially those involving moving boundaries and fluid-structure interactions.