Failure modeling and life prediction of railroad wheels
Sura, Venkata Sasidhar
This dissertation develops a general methodology for probabilistic prediction of railroad wheel failure life considering uncertainties from several possible sources. The two most dominant failure types, shattered rim and vertical split rim failures, occurring due to sub-surface crack propagation, are considered. The crack modeling uses 3-D finite element analysis and linear elastic fracture mechanics. For computational efficiency, the finite element analysis is divided into two stages: full model analysis and sub-model analysis. In the full model analysis, complete wheel geometry is considered and rolling contact analysis is performed. In the sub-model analysis, a small block with an embedded 3D fatigue crack is considered and elastic-plastic analysis is performed by applying full model results as boundary conditions. A mixed-mode crack model based on critical plane concepts is used to compute the equivalent stress intensity factor range (Keq) at the crack tip using the uni-modal values obtained from the finite element analysis. Variable amplitude loading, multi-axial fatigue, residual stresses (both as-manufactured and service-induced), and wheel wear are included in the analysis. Residual stresses in the wheel rim can affect Keq at sub-surface crack tips, and thereby the wheel failure life. Therefore, residual stresses developed during both the manufacturing process and due to the thermal brake loading under service conditions, are estimated using three-dimensional decoupled thermal-structural finite element analyses, and these estimated results are included as initial stresses for rolling contact analysis. Wheel wear is assumed to be uniform for the sake of illustration. Since finite element analysis to estimate the Keq at a sub-surface crack tip for cycle-by-cycle calculations is computationally expensive, a Kriging-based meta-model is developed to represent the relationship between the input parameters and Keq at the crack tip. The uncertainties in various input parameters are considered through probabilistic analysis. Multiple sets of Monte Carlo simulations are performed to obtain the failure life probability distributions and the scatter in the computed results. The numerical results are validated using field data.