An integrated reliability approach with improved importance sampling for low-cycle fatigue damage prediction of turbine disks

Abstract The objective of this paper is to develop an effective reliability approach to improve the numerical accuracy and computational efficiency of the low-cycle fatigue (LCF) damage prediction for turbine disks. The approach is called as distributed collaborative (DC) probabilistic substructure (PS)-based moving improved importance-sampling least-squares (MIIL), DC-PS-MIIL. In this approach, the MIIL model is firstly developed by combining an improved importance sampling (IIS) with moving least squares (MLS), to reduce the computation burden and improve the precision of surrogate model; then, the PS-MIIL comes up by integrating probabilistic substructure (PS) method into MIIL, to address the interface forces of turbine disks; finally, DC-PS-MIIL is proposed by incorporating distributed collaborative strategy with PS-MIIL. The comprehensive analysis procedure with DC-PS-MIIL is shown to simplify the probabilistic assessment of complex structures for improving numerical accuracy and simulation efficiency. Taking the LCF damage prediction of turbine disks as an example, DC-PS-MIIL is demonstrated to be an effective reliability approach. Also, numerical results show that the confidence levels, applied cycles and coefficients of variation (CVs) of random input variables have important impact on the LCF damage reliability for turbine disks.