Authors: Hongshuang Li; Yibing Xiang; Lei Wang; Jianren Zhang; Yongming Liu
Addresses: Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China ' Arizona State University, Tempe, AZ 85287, USA ' Changsha University of Science & Technology, Changsha, 410114, China ' Changsha University of Science & Technology, Changsha, 410114, China ' Arizona State University, Tempe, AZ 85287, USA
Abstract: This paper proposes a general probabilistic methodology for uncertainty propagation in fatigue crack growth analysis under both constant amplitude and variable amplitude loadings. A recently developed small time scale model is used to predict the deterministic fatigue crack growth curve (a-N curve). The dimension reduction technique is used for uncertainty propagation in fatigue crack growth analysis. The basic idea is to avoid direct simulations and focuses on the statistical moment behaviour of output random variables. A modified Chebyshev algorithm is presented to improve the approximation accuracy when calculating the integral points and associated weights for an arbitrary probabilistic distribution. Uncertainties of some material properties are considered as input random variables and propagated through the mechanical model. Prediction result of the proposed methodology is compared with the direct Monte Carlo Simulation (MCS) and is verified using experimental data of aluminium alloys under constant amplitude loading and block loading.
Keywords: uncertainty propagation; fatigue crack growth; probabilistic methodology; methodology; dimension reduction; small time scale models; mechanical modelling; constant amplitude; variable amplitude; Monte Carlo simulation; aluminium alloys; amplitude loading; block loading.
International Journal of Reliability and Safety, 2013 Vol.7 No.3, pp.293 - 317
Available online: 10 Oct 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article