The widespread faults that occur in railway wheels and can cause a massive dynamic impact are the wheel tread flat. The current work considered changes in vehicle speed or wheel radius deviation and studied the dynamic impact load. The modal technique for the impact evaluation induced by the wheel flat was proposed via the finite element analysis (FEA) software package ANSYS, integrated into a multibody dynamics model of the high‐speed train CRH2A (EMU) through SIMPACK. The irregularity track line has developed and depends on the selected simulation data points. Additionally, a statistical approach is designed to analyze the dynamic impact load response and effect and consider different wheel flat lengths and vehicle speeds. The train speed influence on the flat size of the vertical wheel‐rail impact response and the statistical approach are discussed based on flexible, rigid wheelsets. The results show that the rigid wheel flat has the highest vertical wheel impact load and is more significant than the flexible wheel flat force. The consequences suggest that the wheelset flexibility can significantly improve vertical acceleration comparably to the rigid wheel flats. In addition, the rendering of the statistical approach shows that the hazard rate, PDF, and CDF influence increase when the flat wheel length increases.

A probabilistic assessment is made of the design S-N curves for 1Cr18Ni9Ti pipe-welded joint. The material exhibits a character of random cyclic stress-strain and fatigue life responses. Then, both the S and N in the virtual stress amplitude-fatigue crack initiation life (S-N) data are random variables. A general maximum likelihood method, which considers the randomness of the test S-N data, is developed to model the probabilistic S-N relations of material. The design S-N curves with reduction factors of 20 on cycles, 2 on stress and 2.5 on stress applied to the best-fit S-N curve of test data as used in the ASME code and general practice, are assessed in a probabilistic sense. The results reveal that in the lower S regime the 20 and 2.5 reduction factor curves may be appropriate, while the reduction factor of 2 is slightly nonconservative. But in the higher S regime these curves are all much more conservative. The intrinsic cause is that the standard deviation of logN exhibits an increase with the S decreasing, while the constant reduction factors do not consider this increase. It is shown that from the general statistical scatter of S-N data the design S-N curves should be appropriately determined by a probabilistic approach.