In this study, a numerical procedure is developed to predict the wear of turnout rails, and the effect of track parameters is investigated. The procedure includes simulation of the dynamic interaction between the train and the turnout, the rolling contact analysis, and the wear model. The dynamic interaction is simulated with the validated commercial software Simpack that uses a space-dependent model of a railway turnout. To reproduce the actual operating conditions of a railway turnout, stochastic variations in the input parameters are considered in the simulation of the dynamic interaction. The rolling contact is analyzed with the semi-Hertzian method and improved FASTSIM algorithm, which enable the contact model to deal with situations of multipoint contact and nonelliptic contact. Based on the Archard's wear law, the wear model requires the calculation of normal/tangential stresses and a relative slide on the contact patches. The numerical procedure is performed for the selected sections of the vehicle, which runs through the railway turnout in the diverging route. By using the numerical procedure, the effect of track parameters (track gage, rail inclination, and friction coefficient) on the wear of turnout rails is analyzed. The results show that the wear of the front wheelset is more serious than the wear of the rear wheelset for a single vehicle. The degree of wear of switch rails is more severe than that of the stock rails and the difference is more obvious for the front wheelset of the switch rails. The wear of switch rails is mainly concentrated on the rail gage corner, while the wear of stock rails is mainly concentrated on the rail crown. For the analysed CN60-1100-1:18 turnout and the high-speed vehicle CRH2 in China, the rail wear rate could be slowed down by increasing the track gage and decreasing the rail inclination. Alternatively, the rail wear rate could be slowed by decreasing the friction coefficient; however, the variation of wear depth is quite small for friction coefficients that are larger than 0.3.
Different from the short-term risk measure for traditional financial assets (stocks, bonds, etc.), the key to illiquid inventory portfolio traded in the over-the-counter markets is to estimate the long-term extreme price risk with time varying volatility. In this article, a new long-term extreme price risk (value at risk and conditional value at risk) measure method for inventory portfolio and an application to dynamic impawn rate interval are proposed. To realize this, we first establish AutoRegressive Moving Average-Exponential Generalized Autoregressive Conditional Heteroskedasticity-Extreme Value Theory model and multivariatet-Copula to depict the autocorrelation, fat tails, and volatility clustering of returns of inventories and the nonlinear dependence structure of inventories. Furthermore, we obtain the long-term extreme price risk with time varying volatility via Monte Carlo simulation instead of square-root-of time rule. The results show that, first, benefits from risk diversification is significant; second, longterm extreme price risk measure of inventory portfolio via Monte Carlo method outperforms the square-root-of time rule; the last is that the dynamic rate interval based on the long-term price risk is superior to the crude rules of thumb in terms of reducing efficiency loss and improving risk coverage. In summary, this article provides a new quantitative framework for managing the risk of portfolio in inventory financing practice for banks constrained by risk limitation.
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