Bi-iterative moving enhanced model for probability-based transient LCF life prediction of turbine blisk

The low cycle fatigue (LCF) life of aeroengine turbine blisk endures uncertain and transient loads from multi-physical fields, so that it is necessary to evaluate the LCF life for improving the safety of turbine blisk and aeroengine from a probabilistic perspective. In this paper, bi-iterative moving enhanced modeling (BIMEM) approach is developed to conduct the probability-based transient LCF prediction of turbine blisk with fluid-thermal-structural interaction, by integrating extremum thought, Kriging model, moving least squares (MLS) technique and modified particle swarm optimization (MPSO) algorithm. The extremum thought is used to simplify the processes of transient responses of turbine blisk into extremum values in time domain. Both MLS technique and Kriging model are employed to establish the relationship functions between output response and input parameters, for deriving transient reliability sensitivity analysis of turbine blisk LCF life. The MPSO algorithm is applied to find the optimal values of hyperparameters in Kriging model with the effective samples; from the probability-based transient LCF prediction of turbine blisk, it is illustrated that turbine blisk reliability degree is 0.9986, when the allowable value of LCF life is 2968 cycles; and the primary factor affecting turbine blisk LCF life is strength coefficient, followed by gas temperature, fatigue strength exponent, and so forth; the developed BIMEM approach is better in modeling precision and simulation efficiency in the probabilistic-based LCF life prediction, relative to other methods. The efforts of this paper offer a promising way for the health evaluation and prediction of complex structures besides turbine blisk.