Dynamic probabilistic-based LCF damage assessment of turbine blades regarding time-varying multi-physical field loads

Abstract To efficiently and precisely evaluate the low-cycle fatigue (LCF) damage of turbine blades regarding dynamic cyclic loads, a dynamic substructure (DS)-based distributed collaborative extremum moving least squares (DCEM) (called as DS-DCEM) surrogate model is developed by absorbing moving least squares (MLS) with extremum response concept into the substructure-based distributed collaborative strategy in this paper. The probabilistic analysis procedure is given and the corresponding mathematical model is derived. Following that, the DS-DCEM is integrated with confidence level-based strain-life functions to introduce the numerical procedure for the dynamic LCF damage assessment of turbine blades with respect to the uncertainties in working loads, geometric sizes and material properties. From the dynamic probabilistic-based LCF damage assessment of the turbine blade, it is indicated that the confidence levels have important impacts on the damage reliability. As the confidence levels increase from 0.50, 0.90 to 0.95, the reliability values reduce from 0.99625, 0.90285 to 0.80304, respectively. In addition, the four fatigue parameters play a leading role on the LCF life prediction. The comparison of methods shows that DS-DCEM holds high numerical accuracy and computational efficiency for the dynamic LCF damage assessment of turbine blades. This paper develops a promising approach for the dynamic fatigue failure assessment of complex structures.

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