OOA-Optimized Kriging-RBF Method for Radial Deformation Reliability Analysis of Compressor Blade Angle Crack

Angle crack defects significantly affect compressor blade radial deformation characteristics, posing critical challenges for reliability assessment under operational uncertainties. This study proposes a novel OOA-optimized Kriging-RBF (OOA-KR) method for efficient reliability evaluation of blade radial clearance with angle crack defects. The approach integrates Kriging's uncertainty quantification capabilities with RBF neural networks' nonlinear mapping strengths through an adaptive weighting scheme optimized by the Osprey Optimization Algorithm. Multiple uncertainty sources including crack geometry, operational temperature, and loading conditions are systematically considered. A comprehensive finite element model incorporating crack size variations and multi-physics coupling effects generates training data for surrogate model construction. Comparative studies demonstrate superior prediction accuracy with RMSE = 0.568 and R² = 0.8842, significantly outperforming conventional methods while maintaining computational efficiency. Reliability assessment achieves 97.6% precision through Monte Carlo simulation. Sensitivity analysis reveals rotational speed as the most influential factor (S = 0.42), followed by temperature and loading parameters. The proposed OOA-KR method provides an effective tool for blade design optimization and reliability-based maintenance strategies.