“…The gradients are computed with a least-squares approach in control volumes surrounding the cells, the momentum equation is dealt with by bounded central differencing, and other flow equations are dispersed by the second-order upwind. 36–38 The semi-implicit method for pressure-linked equations-consistent (SIMPLEC) was selected to deal with pressure–velocity coupling. 23,39,40 A dual time-stepping format with second-order accuracy was used for time discretization.…”
Section: Methodsmentioning
confidence: 99%
“…A hexahedral mesh of OpenFOAM was used to divide the computational domain. 17,23,34,37,39 To analyze the grid independence, the computational domain is divided by three grid densities, coarse, medium, and fine, which are shown in Figure 4. The only difference among the three grid densities is the size of the grid on the surface of the train model, 2 mm, 1 mm, and 0.5 mm, which in turn corresponds to coarse, medium, and fine grid density, and some grid parameters for the 1:8 scaled model in the grid independence are presented in Table 1.…”
Sand and gravel blown up by strong wind will cause damage to windows. Following up, it will cause the change in the flow field around a train immediately, which has a significant impact on the aerodynamic performance of the train. Delayed detached-eddy simulation based on the shear stress transport κ-ω turbulence model was used to investigate the aerodynamic characteristics of passenger vehicles under crosswind, and the effect of damaged windows on the train aerodynamic performance and flow field around the train was analyzed and compared. The grid generation and numerical method are verified in the previous studies. Results show that the aerodynamic performance of a passenger vehicle is significantly affected by damaged windows on both sides of the passenger vehicle, both the aerodynamic lateral force and overturning moment of the passenger vehicle were decreased, and this has a small effect on the aerodynamic performance of the downstream passenger vehicle. Fluctuation of the aerodynamic lift, lateral force, and overturning moment of a vehicle with damaged windows on both sides of the vehicle is significantly increased. Damaged windows do not change the existence of the main vortices around vehicles, but the size, location, and strength of the main vortices are obviously changed, especially when windows on both sides of the vehicle are damaged. Therefore, when windows on the windward side of the vehicles are damaged, opening or broking windows on the leeward side of the vehicles will help reduce the risk of vehicles overturning and derailment.
“…The gradients are computed with a least-squares approach in control volumes surrounding the cells, the momentum equation is dealt with by bounded central differencing, and other flow equations are dispersed by the second-order upwind. 36–38 The semi-implicit method for pressure-linked equations-consistent (SIMPLEC) was selected to deal with pressure–velocity coupling. 23,39,40 A dual time-stepping format with second-order accuracy was used for time discretization.…”
Section: Methodsmentioning
confidence: 99%
“…A hexahedral mesh of OpenFOAM was used to divide the computational domain. 17,23,34,37,39 To analyze the grid independence, the computational domain is divided by three grid densities, coarse, medium, and fine, which are shown in Figure 4. The only difference among the three grid densities is the size of the grid on the surface of the train model, 2 mm, 1 mm, and 0.5 mm, which in turn corresponds to coarse, medium, and fine grid density, and some grid parameters for the 1:8 scaled model in the grid independence are presented in Table 1.…”
Sand and gravel blown up by strong wind will cause damage to windows. Following up, it will cause the change in the flow field around a train immediately, which has a significant impact on the aerodynamic performance of the train. Delayed detached-eddy simulation based on the shear stress transport κ-ω turbulence model was used to investigate the aerodynamic characteristics of passenger vehicles under crosswind, and the effect of damaged windows on the train aerodynamic performance and flow field around the train was analyzed and compared. The grid generation and numerical method are verified in the previous studies. Results show that the aerodynamic performance of a passenger vehicle is significantly affected by damaged windows on both sides of the passenger vehicle, both the aerodynamic lateral force and overturning moment of the passenger vehicle were decreased, and this has a small effect on the aerodynamic performance of the downstream passenger vehicle. Fluctuation of the aerodynamic lift, lateral force, and overturning moment of a vehicle with damaged windows on both sides of the vehicle is significantly increased. Damaged windows do not change the existence of the main vortices around vehicles, but the size, location, and strength of the main vortices are obviously changed, especially when windows on both sides of the vehicle are damaged. Therefore, when windows on the windward side of the vehicles are damaged, opening or broking windows on the leeward side of the vehicles will help reduce the risk of vehicles overturning and derailment.
“…The reason for this could be because the cowcatcher and the first bogie have the most turbulent flow structures in the underbody flow, and the turbulence level of the underbody airflow decreases along the streamwise direction. 27 The coarse mesh fails to capture the motion characteristics of the separated flow and thus results in an inaccurate prediction of the pressure distribution. Figure 4.Comparison of the streamwise velocity, surface pressure, snow concentration and accretion mass on the bogies between the numerical results of the coarse, medium and fine grids: (a) locations of the monitoring lines, (b) streamwise velocity, (c) surface pressure, (d) snow concentration and (e) accretion mass on the first bogie within each 0.5 s from 0 s to 3.0 s during the simulation.…”
In this paper, numerical simulations combining unsteady Reynolds-averaged Navier-Stokes (URANS) simulation and the discrete phase model are used to study the application of countermeasure for snow accumulation in the regions of bogie cavities of a high-speed train. The influence of the cowcatcher heights and guide structure configurations on the flow features and snow accumulation was studied. The results of the study show that the cowcatcher with a downward elongation of 4% of the distance between the two axles decreases the snow accumulation in the first and the second bogie regions by about 56.6% and 13.6%, respectively. Furthermore, the guide structures have been found to significantly alter the velocity and pressure distribution in the second bogie region, resulting in a relatively large snow-accumulation reduction. The deflector is found to perform better in reducing snow accumulation when compared to the diversion slots. The cowcatcher, elongated in the downward direction, and the deflector proved to be a good countermeasure for snow accumulation around the bogies of high-speed trains operating in snowy weather conditions.
“…In the present paper, the train model with three carriages will be considered, to greatly reduce the computational cost. Compared to other components, the bogies own more complicated shape and affect greatly on the flow around the train, especially the flow underneath the train body (Gao et al, 2019;Wang et al, 2018aWang et al, , 2018b. As a result, the influence of bogies on the slipstream is mainly discussed in this section, to determine a better way for the simplification of bogies.…”
Section: Simplification Of the Train Modelmentioning
Slipstream severely affects the safety of trackside workers and equipment. With use of profile superimposition method and vehicle modeling method, parametrization of the whole train is carried out. Then, a slipstream optimization study has been performed, taking components of slipstream in different directions at the standard heights, the drag coefficient of the whole train and the volume of the driving cab as the design objectives. For each design objective, one unique ε-TSVR surrogate model has been constructed. Six final Pareto sets have been obtained on the base of six groups of different fitness functions by using multi-objective particle swarm method. Results reveal that the volume of the driving cab keeps almost the same, compared to the original shape. The velocity components of train-induced wind at the positions 0.2 and 1.4 m above the top of the rail, and the drag coefficient of the train are reduced by 11.6%, 33.9%, 24.7%, 25.9% and 13.0% respectively. Sensitivity analysis reveals that the length of the streamline, the height of the train and the width of the train influence significantly on the aerodynamic performance, and the train with a tall and thin streamline will benefit in reducing the slipstream and aerodynamic drag.
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