A Detached Eddy Simulation (DES) method based on the SST k-ω turbulence model was used to investigate the instantaneous and time-averaged flow characteristics around the train with a slender body and high Reynolds number subjected to strong crosswinds. The evolution trends of multi-scale coherent vortex structures in the leeward side were studied. These pressure oscillation characteristics of monitoring points on the train surfaces were discussed. Time-averaged pressure and aerodynamic loads on each part of the train were analyzed inhere. Also, the overturning moment coefficients were compared with the experimental data. The results show that the flow fields around the train present significant unsteady characteristics. Lots of vortex structures with different intensities, spatial geometrical scales, accompanied by a time change, appear in the leeward side of the train, in the wake of the tail car and below the bottom of the train. The oscillation characteristics of the flow field around the train directly affect the pressure change on the train surfaces, thereby affecting the aerodynamic loads of the train. The loads of each car fluctuate around some certain mean values, while the positive peak values can be higher than the mean ones by up to 34%. The load contributions of different parts to the total of the train are also obtained. According to it, to improve the crosswind stability of the high-speed train, much more attention should be paid on the aerodynamic shape design of the streamlined head and cross section. In addition, this work shows that the DES approach can give a better prediction of vortex structures in the wake compared with the RANS solution.

It is well known that the train nose shape has significant influence on the aerodynamic characteristics. This study explores the influence of four kinds of nose shapes (fusiform, flat-broad, bulge-broad, ellipsoidal) on the aerodynamic performance of two opposing high-speed trains passing by each other through a tunnel at 250 km/h. The method of three dimensional, compressible, unsteady Reynolds-averaged Navier-Stokes equations and RNG k-ε double equation turbulence model was carried out to simulate the whole process of two trains passing by each other inside a tunnel. Then the pressure variations on tunnel wall and train surface are compared with previous full-scale test to validate the numerical method adopted in this paper. The assessment characteristics, such as transient pressure and aerodynamic loading, are analyzed to investigate the influence of nose shape on these assessment parameters. It is revealed that aerodynamic performance of trains which have longitudinal nose profile line B (fusiform, flat-broad shape) is relatively better when passing by each other in a tunnel. The results can be used as a guideline for high-speed train nose shape design.

A time-dependent simulation method, DES (detached eddy simulation), combined with Realizable k-ε turbulence model, has been adopted to study the underbody flow and near wake structures of a high-speed train with two bogie cavity configurations laid on the stationary ground. The numerical data, including timeaveraged aerodynamic drag forces and pressure coefficients, were compared with experimental results from previous wind tunnel tests. A detail comparison of the instantaneous flow structures, mean velocity vector contours, velocity and pressure profiles under the train bottom in the symmetry plane and velocity contours overlaid with streamlines in the wake has been conducted in the two configurations. Also the aerodynamic drag coefficients for the two cases are discussed herein. The two cases show that the bogie cavity configurations contribute to the differences of velocity and pressure distributions in each bogie region, as well as the complex vortex structures around the bogie regions. Compared to the inclined bogie cavity configuration, the train with straight plates experiences a lower drag force by 2.8% for a three-car model in the stationary ground. Thus, an effective simplification criterion for the train model will contribute to an accurate prediction of forces of trains in simulations.