Research on multi-fidelity surrogate modeling method for non-hierarchical low-fidelity analysis model problem

Objective Multi-fidelity surrogate (MFS) modeling technology can reduce simulation costs in the design process of engineering products. In order to relax the hierarchical relationship between low-fidelity (LF) analysis models and broaden the engineering application of MFS, this paper proposes an MFS modelling method based on variance-weighted sum (VWS-MFS) for the fusion of multiple non-hierarchical LF data.

Method The proposed method builds LF surrogate models using Kriging technology. By quantifying the uncertainty of the LF surrogate models with variance, the non-hierarchical LF data is weighted to construct a trend function. In addition, the improved hierarchical Kriging (IHK) model is introduced to fuse the high-fidelity (HF) and LF data, enabling the correction coefficient of the trend function to change throughout the design space. The proposed method is then tested on nine typical examples and applied to the performance prediction of a vibration isolator.

Results According to the experimental results, the proposed method shows higher prediction accuracy than similar methods by more than 85%, and its vibration isolator performance prediction is significantly improved by more than 60% compared with the static prediction method.

Conclusion The proposed method integrates the HF analysis model and multiple non-hierarchical LF analysis models. While the hierarchical relationship between LF analysis models is relaxed, the information of LF data is mined to the maximum extent.