A new approach for spatio-temporal interface treatment in fluid–solid interaction using artificial neural networks employing coupled partitioned fluid–solid solvers

Abstract

Partitioned fluid–solid interaction (FSI) problems involving non-conforming grids pose formidable challenge in interface treatment, especially for information exchange, interface tracking, and field variable interpolation between solvers in both space and time. These demand special considerations for accurate and efficient simulations. This paper presents an application of artificial neural networks (ANN) for the interface treatment in a coupled FSI problem employing partitioned solvers. A shallow time-series ANN (nonlinear auto-regressive model with exogenous inputs, NARX) scheme is proposed to handle the exchange of Neumann/Dirichlet information at the coupling interface. This scheme involves two interface treatment models that were developed and analysed. The proposed models interpolate and transfer loads from the fluid to the solid domains, and conversely, displacements from the solid to the fluid domains between non-collocated grids. To validate this approach, we tested it on a 3D FSI problem, which involved damped oscillations of a flexible flap submerged in a fluid cavity. Adequately trained NARX interface models demonstrate reliable input–output mapping and accurate prediction of transient behaviour at the interface. Additionally, we explored the concept of reduced-order modelling (ROM) in the time domain. This allowed us to reduce the model’s complexity by half. Different training algorithms were evaluated to enhance the efficiency and performance of the proposed scheme. The study demonstrates that NARX networks trained with Bayesian Regularization (BR) and Levenberg–Marquardt (LM) algorithms exhibit the best accuracy, while the scaled conjugate gradient (SCG)-based training method provides better computational efficiency with acceptable accuracy. Overall, the NARX interface models provide precise performance and offer a viable potential for applications in FSI problems requiring accurate and faster computations.