High-cycle fatigue life prediction for fan blades considering aleatory and epistemic uncertainty with random damage

Boundary Layer Ingestion (BLI) is a novel propulsion technology that enhances thrust by introducing airflow through the fan blades. However, predicting fan blades high-cycle fatigue (HCF) life for such design is challenging due to uncertainties from factors such as airflow. To address this difficulty, a fan blade HCF life prediction method based on aleatory and epistemic uncertainty with random damage is proposed, aimed at accurately assessing the fatigue life of blades under uncertain conditions. The method combines sparse polynomial chaos expansion-Monte Carlo simulation (SPCE-MCS) with finite element analysis (FEA) to establish the relationship between uncertainty parameters and stress responses. Random damage sequences are constructed by sampling the damage cumulative distribution function (CDF). The expectation-maximization Gaussian mixture distribution (EM-GMD) is then used to quantify the epistemic uncertainty and optimize the normal distribution of fatigue life. The results show that, compared to the traditional method (without considering random damage), the proposed method reduces the confidence interval of fatigue life prediction by over 95 %, with errors in the characteristic parameters of the fatigue life distribution below 3 %, confirming the reliability of the fatigue life distribution. Overall, this study provides an effective solution for reliability assessment of BLI blades.