Bayesian-based probabilistic fatigue crack growth evaluation combined with machine-learning-assisted GPR

This paper presents a Bayesian-based calibration method that simultaneously improves the model accuracy and the computational efficiency for fatigue crack growth (FCG) life prediction on turbine discs. Uncertainties derived from geometry, material and models are elaborately quantified based on the data from measurements and experiments. A Bayesian approach is used for uncertainty quantification, where Markov Chain Monte Carlo algorithm is employed to estimate posterior distributions. Gaussian process regression (GPR) is introduced to describe the propagation of uncertainties and improve the efficiency in high-dimensional analysis. With the integrated methodology, uncertainties are embodied in life prediction results of a whole turbine disc. A full-scale spin test is carried out under low cycle fatigue loading, where three effective crack samples are generated and monitored. Compared with the experimental results, the mean values of the predictions are bounded within a factor of ±2.0, validating the potential usage of the proposed method in the probabilistic FCG life assessment.