Narrow Results

When high-speed trains (HSTs) meet on a bridge in a crosswind environment, the train on the windward side (TWS) will produce a wind-shielding effect similar to a windbreak. This effect, coupled with the instantaneous pressure wave generated at the time of the meeting, will cause an abrupt variation in the aerodynamic load of the train on the leeward side (TLS) during the meeting process, thereby affecting the safe and stable operation of the train. This study analyzed the factors affecting the degree of the abrupt variation in the aerodynamic load of the TLS during the meeting, and studied the change rule of the safety index of the TLS under different wind speeds. In addition, we also explored the impact of three types of ventilation rate curved wind barriers on the abrupt variation in the aerodynamic load of the TLS during the meeting. The research results show that the increase in crosswind speed will increase the degree of the abrupt variation in the aerodynamic load of the TLS, further exacerbating the impact on the safe and stable operation of the TLS. Although the increase in train speed will reduce the magnitude of the abrupt variation in the aerodynamic load of the TLS during the meeting, it will increase the rate of abrupt variation. However, a curved wind barrier with a ventilation rate of 30% can effectively alleviate the abrupt variation in the aerodynamic load of the TLS during the meeting. These research results have important reference value for improving the driving safety of HSTs in crosswind environments.

The use of continuous welded rails (CWR) is increasingly common and is particularly important when it comes to high-speed ballasted tracks. As the longitudinal displacements are restricted in CWR tracks, a considerable rise in temperature induces compressive stresses in the rails that can lead to track buckling. Given the nonlinear behavior of the ballast, usually represented by a linear plastic model, the problem of snap-through buckling may occur, for which only a few nonlinear analysis methods can trace the full response of the track structure. However, these methods fail to yield convergent solutions for problems with thermal loads when implemented in their conventional algorithm. For this reason, a new methodology is presented allowing the calculation of the safe temperature. In addition, some analytical results are also derived for comparison with the numerical results, obtained using three-dimensional finite element beam models provided by ANSYS.

To study the probabilistic distribution of maximum wheel unloading rate of high-speed trains and its temporal correlation when a train passes over a bridge, a method for the estimation of the extremal index is proposed. Using the time series threshold theory, the maximum value cumulative distribution function (CDF) when the wheel unloading rate is regarded as a time series is derived and validated. This approach can also address dependent series, which the traditional probability distribution function formulas could not. Then, the difference between treating the wheel unloading rate as a time series and independent series is investigated using Monte Carlo simulations. Finally, the influence of the number of calculation steps on the threshold is studied, and the differences between thresholds calculated by different extremal indices when considering the number of trains running during the service period of the bridge are explored. The maximum value CDFs of the wheel unloading rate for different track irregularities, bridge lengths, and vehicle speeds are investigated for a three-span simply-supported bridge. The results show that the differences in the maximum value probability density functions (PDFs) obtained by considering the wheel unloading rate as time series and independent random series cannot be ignored. However, when studying a high-confidence level problem, such as the threshold of the wheel unloading rate, the difference between the two approaches is small enough. As the number of calculation steps increases, the extremal index will gradually decrease. When considering a long-distance high-speed rail line, its shorter segment can be used to study the threshold of the wheel unloading rate.