A novel artificial neural network-based interface coupling approach for partitioned fluid–structure interaction problems

This paper presents a novel interface technique employing Artificial Neural Networks (ANN) for efficient data transfer between incompressible fluid and deformable solid domains in a partitioned fluid–structure interaction (FSI) framework. Governing differential equations (GDE) with potential flow assumptions are solved to obtain pressure values at computational nodes in the fluid domain. Pressure values are then used to train an ANN-based model to interpolate pressure loads for application at non-collocated nodes on the solid boundary. Both finite element method (FEM) and meshfree element free Galerkin (EFG) formulations are used to calculate the solid deformation. Proposed methodology is applied to solve a two-dimensional (2D) unidirectional flow problem, consisting of a flexible cantilever beam immersed in a laminar, steady, and inviscid fluid. Overwhelming mathematical intricacies of computational solvers are avoided for the sake of simplistic interface treatment, flux transfer and associated phenomena. Dedicated partitioned solvers are developed for both solid and fluid domains, which are individually benchmarked for established problems from the literature. The presented ANN-based interpolation scheme provides an alternative to higher-order polynomial algorithms at the interface boundary. The proposed scheme is simple to employ, computationally efficient, and offers competitive accuracy as well as stability.