A novel hybrid neural network of fluid-structure interaction prediction for two cylinders in tandem arrangement

Fluid-structure interaction (FSI) in multibody systems, a non-negligible phenomenon in engineering applications, has been extensively studied via traditional experimental and simulation methods with high cost and time consumption. Deep learning has shown promise in improving computing efficiency while ensuring modelling accuracy in FSI analysis. However, its current capabilities are limited when it comes to constructing multi-object coupling systems with dynamic boundaries. In this paper, we propose a novel FSI hybrid neural network solver integrated by an innovative fluid deep learning model and the structural motion equations for the vortex-induced vibration (VIV) modelling of two tandem cylinders. This well-designed solver, in sequence-to-point manner, can precisely predict the subsequent flow field state by coupling the historical multi-time fluid sequences and the current structural responses, moreover, derives the structural state at the next time based on the interaction forces. Therein, the fluid deep learning model consists of a wall shear stress model and an innovative flow field model with U-shaped architecture jointing the Fourier neural operator and modified convolution long-short term memory model. Two models effectively capture coupling interaction forces, and the latter has higher accuracy in modelling instantaneous flow fields compared with baseline Convolutional Neural Networks-based models with similar parameters. Compared to FSI benchmark case, the proposed FSI model demonstrates superior accuracy and robustness in constructing the nonlinear complex multi-vibration systems. And its prediction speed realises an improvement of over 1000 times than that of the numerical simulation. Significantly, the proposed FSI neural model has substantial potential for advancing FSI modelling of flexible structures featuring pronounced nonlinear deformation boundaries.