Model of neural networks with sigmoid and radial basis functions for velocity-field reconstruction in fluid-structure interaction problem

This study presents numerical simulation of velocity-field reconstruction in Fluid-structure Interaction (FSI) problem with the presence of a very step velocity jump at the fluid-solid interface. Models of Neural Network (NN) with sigmoid and radial basis functions were developed and utilized as approaches of investigation to fully reconstruct the velocity-field at the fluid-structure interface of the problem. As a numerical case, one-dimensional compressible fluid coupled with elastic solid under strong impact was simulated. This class of problem belongs to an Eulerian-Lagrangian Riemann problem in which the very step jump of velocity vector does exist. The resolution of the NN models in the vicinity of the interface was further investigated and analyzed in which the accuracy of the NN approach was validated to the problem analytical solution. From the results of the numerical study, high numerical accuracy of the NN models can be obtained in relation with the increase of the interface resolution through which useful insights of this study were also revealed.