Hull Surface Vibration Prediction and Self-Updating Method Based on Co-Kriging

To address the issues of low computational efficiency and scarce experimental samples in hull surface vibration prediction, a multi-fidelity method with self-updating capability for high-dimensional frequency response fields is investigated, focusing on a typical hull structure—the double bottom-rigid platform. Principal component analysis (PCA) is employed to compress high-dimensional frequency response data. A two-layer Co-Kriging surrogate model is then constructed within the excitation-response coordinate space to fuse multi-fidelity data from both simulations and experiments. Furthermore, an active learning strategy based on posterior variance maximization is introduced to implement sequential sample infilling and model updating. Cross-validation results/t/ndemonstrate that the mean absolute error (MAE) of the proposed method is approximately 2.55 dB. By incorporating only three additional samples through the active learning strategy, the global root mean square error (RMSE) is reduced by 7.7%. This method achieves accurate characterization of frequency-spatial high-dimensional response fields and is applicable to high-dimensional surrogate modeling under small-sample constraints, providing a reference for model updating and online vibration assessment in ship digital twin systems.