Computational-experimental approaches for fatigue reliability assessment of turbine bladed disks. (July 2018)

• Record Type: Journal Article
• Authors: Zhu, Shun-Peng; Liu, Qiang; Peng, Weiwen; Zhang, Xian-Cheng
• Is Part Of: International journal of mechanical sciences. Volume 142/143(2018)
• Page Start: 502
• Page End: 517
• Publication Date: 2018-07
• Subjects: Fatigue -- Reliability -- Uncertainty -- Sensitivity analysis -- Bladed disk; Mechanical engineering -- Periodicals; Génie mécanique -- Périodiques; Mechanical engineering; Maschinenbau; Mechanik; Zeitschrift; Periodicals; 621.05
• DOI: 10.1016/j.ijmecsci.2018.04.050
• Languages: English
• ISSNs: 0020-7403

Abstract

Highlights: A computational-experimental framework for fatigue reliability assessment. Two schemes based on probabilistic S-N curves and stochastic FE simulations. Probabilistic plasticity induced stress-strain response analysis under uncertainty. Real case of a bladed disk by combing overspeed testing with stochastic FE analysis. Abstract: In the present study, a computational-experimental framework is developed for fatigue reliability assessment of turbine bladed disks. Within the framework, the overspeed testing is innovatively combined with stochastic finite element (FE) analysis for quantifying uncertainties in the experimental data, material properties and loads. Meanwhile, two schemes are elaborated based on probabilistic S-N curves and stochastic FE simulation coupling with sampling technique. The stochastic FE simulation incorporates the Chaboche constitutive model with Fatemi–Socie criterion for fatigue behavior modeling and life prediction. Moreover, experimental deformation and numerical FE analysis are conducted with regard to the full-scale bladed disk test with increased step-stress overloading. Reliability sensitivity analysis is performed to provide an importance ranking of random variables for fatigue design of the bladed disk. Results indicate that stochastic FE analysis-based scheme provides more conservative predictions than the probabilistic S-N curves-based one. Graphical abstract: