In this paper,the vortex-induced vibration(VIV)amplitude response is reconstructed and predicted with the VIV fast solving software which can be called DAVIV and the multi-fidelity data fusion method.The multi-fidelity method based on an improved nonlinear Gaussian progress regression combines a large number of low-fidelity DAVIV results with a small amount of high-fidelity experimental data.This method can be used to reconstruct the distribution of VIV response and predict the VIV amplitude under new oncoming flow conditions.Meanwhile,the active learning method is adopted to automatically add new data during the training process to obtain the optimal data sampling situation,which can reduce the data amount involved in training.For multiple oncoming flow conditions,the iterative strategy of increasing the number of sensors or increasing the number of analysis cases is considered during the experimental and CFD prediction process,which can better reduce the actual cost and provide a reference for practical engineering applications.
维普
万方数据