A probabilistic life prediction framework for vibration fatigue of turbine blade based on refined polynomial chaos expansion

Vibration fatigue is a typical form of multiaxial fatigue. In this study, initial vibration fatigue tests were conducted on rocket turbine blades, and significant dispersion in fatigue life was found. Consistent with this finding, a novel probabilistic life prediction framework was proposed. This framework integrates sensitivity analysis and sequential sampling technology and introduces polynomial chaos expansion as a computationally efficient alternative to finite element analysis. And a continuous mechanics‐based damage evolution model was employed to examine the vibration fatigue life of turbine blades. The findings validate the effectiveness of the framework, as no significant difference was found between the experimental results and simulated predictions at the 95% confidence level. Furthermore, comparison with the Monte Carlo simulation indicated that this framework achieves comparable prediction accuracy, while significantly reducing the required samples by 2 orders of magnitude, which effectively addresses the fatigue problem of small sample data. This framework enables rapid and accurate multiaxial fatigue probabilistic life prediction, which holds important implications for the reliability design of reusable spacecraft. Highlights This study is a novel probabilistic framework for predicting fatigue life of small sample data. The polynomial chaos expansion (PCE)‐based framework achieves the Monte Carlo simulation (MCS)‐level accuracy with less than 1% samples. The vibration fatigue tests have verified the effectiveness of proposed method.