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Vortex induced vibration (VIV) occurs when vortex shedding frequency fallsclose to the natural frequency of a structure. Investigation on VIV is of greatvalue in disaster mitigation, energy extraction and other applications.Following recent development in machine learning on VIV in laminar flow, thisstudy extends it to the turbulent region by employing the state-of-the-artparameterised Navier-Stokes equations based physics informed neural network(PNS-PINN). Turbulent flow with Reynolds number $Re$ = $10^4$, passing througha cylinder undergoing VIV motion, was considered as an example. Within thePNS-PINN, an artificial viscosity $\nu_t$ is introduced in the Navier-Stokesequations and treated as a hidden output variable. A recently developedNavier-Stokes equations based PINN, termed NSFnets (Jin \textit{et al.}, JCOMPUT PHYS 426: 109951, 2021), was also considered for comparison. Trainingdatasets of scattered velocity and dye trace concentration snapshots, fromcomputational fluid dynamics (CFD) simulations, were prepared for both thePNS-PINN and NSFnets. Results show that, compared with the NSFnets, thePNS-PINN is more effective in inferring and reconstructing turbulent flow underVIV circumstances. The PNS-PINN also shows the capability to deal with unsteadyand multi-scale flows in VIV. Inspired by the Reynolds-Averaged Navier-Stokes(RANS) formulation, by implanting an additional artificial viscosity, thePNS-PINN is free of complex turbulence model closure, thus may be applied formore general and complex turbulent flows.

Machine Learning for Vortex Induced Vibration in Turbulent Flow