Interval analysis and calibration of blade via adaptive dimension decomposition

The structural dynamic characteristics of blades directly impact the overall performance and operational safety of the rotating machinery. However, traditional deterministic finite element model cannot accurately capture the actual dynamic behavior due to the influence of various uncertainties. Existing uncertainty analysis methods based on surrogate models face the dual challenges of low efficiency and poor accuracy when dealing problems with parameter anisotropy and heterogeneous coupling strengths. Moreover, the current interval analysis framework lacks a method for quantifying global sensitivity. To address these issues, this paper first proposes an adaptive surrogate model that integrates dimension decomposition with Chebyshev polynomials. By adaptively identifying the main effects of parameters to determine the order of the anisotropic expansion and retaining only key bivariate interaction terms, the method significantly enhances computational efficiency while ensuring high modeling fidelity. Furthermore, an interval bounds prediction method and an interval-based global sensitivity analysis method were developed based on this surrogate model. Following numerical validation, the proposed method was used to perform boundary predictions and sensitivity analyses on the modal and impact responses of a simplified aircraft engine fan blade. Finally, the finite element model was calibrated based on modal testing and the developed surrogate model. The results demonstrate that the proposed method effectively overcomes the limitations inherent in traditional surrogate models, offering a novel theoretical framework and technical pathway for the interval uncertainty quantification and model calibration of complex structures. Therefore, it holds significant engineering value for applications such as structural optimization design, health monitoring, and digital twin integration based on numerical models.