A Probabilistic Framework for Low Cycle Fatigue Life Prediction and Uncertainty Modeling of Turbine Disk Alloys

摘要

Probabilistic life prediction of aircraft turbine disks requires the modeling of multiple complex random phenomena. The aim of the present paper is to develop a framework for probabilistic low cycle fatigue (LCF) life prediction using Bayes' theorem and to quantify the uncertainty of material properties, total inputs and model uncertainty resulting from creation of different deterministic models within a LCF regime. Further, based on experimental data of the turbine disk material (GH4133) tested at various temperatures, the capabilities of proposed probabilistic LCF life prediction framework are verified using four models (the viscosity-based model, generalized damage parameter, Smith-Watson-Topper (SWT) and plastic strain energy density (PSED)). Through updating the input parameters with new data, this probabilistic framework provides more valuable information for assessing the life of structures or materials, showing that the predicted distributions of fatigue life agree well with the experimental results. The results show that the viscosity-based model and the SWT model yield more satisfactory probabilistic life prediction results for GH4133 under different temperatures than the generalized damage parameter and PSED ones.