Probabilistic-based design can efficiently quantify the risk and improve the reliability of the turbine blade. In this context, a probabilistic framework concerning the uncertainty quantification for the turbine blade’s TMF life based on the cyclic damage accumulation (CDA) method and Bayesian inference is proposed in this study. Firstly, the damage factors in the critical plane are obtained by the finite element method using Walker constitutive model, during which the discretization error is calibrated using Richardson extrapolation method. After that, the probabilistic TMF life model is established using CDA theory, in which the uncertainty of the material parameters is quantified using Bayesian inference. Finally, TMF life prediction on a single crystal nickel superalloy turbine blade is conducted using the probabilistic framework considering uncertainty quantification. The accuracy and validity of the proposed method is revealed by the comparison between the numerical and experimental results of real turbine blades.

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