Deep neural network based reduced-order model for fluid–structure interaction system

Abstract

Fluid–structure interaction analysis has high computing costs when using computational fluid dynamics. These costs become prohibitive when optimizing the fluid–structure interaction system because of the huge sample space of structural parameters. To overcome this realistic challenge, a deep neural network-based reduced-order model for the fluid–structure interaction system is developed to quickly and accurately predict the flow field in the fluid–structure interaction system. This deep neural network can predict the flow field at the next time step based on the current flow field and the structural motion conditions. A fluid–structure interaction model can be constructed by combining the deep neural network with a structural dynamic solver. Through learning the structure motion and fluid evolution in different fluid–structure interaction systems, the trained model can predict the fluid–structure interaction systems with different structural parameters only with initial flow field and structural motion conditions. Within the learned range of the parameters, the prediction accuracy of the fluid–structure interaction model is in good agreement with the numerical simulation results, which can meet the engineering needs. The simulation speed is increased by more than 20 times, which is helpful for the rapid analysis and optimal design of fluid–structure interaction systems.