Intensive turbulence exists in the wakes of high speed trains, and the aerodynamic performance of the trailing car could deteriorate rapidly due to complicated features of the vortices in the wake zone. As a result, the safety and amenity of high speed trains would face a great challenge. This paper considers mainly the mechanism of vortex formation and evolution in the train flow field. A real CRH2 model is studied, with a leading car, a middle car and a trailing car included. Different running speeds and cross wind conditions are considered, and the approaches of unsteady Reynold-averaged Navier-Stokes (URANS) and detached eddy simulation (DES) are utilized, respectively. Results reveal that DES has better capability of capturing small eddies compared to URANS. However, for large eddies, the effects of two approaches are almost the same. In conditions without cross winds, two large vortex streets stretch from the train nose and interact strongly with each other in the wake zone. With the reinforcement of the ground, a complicated wake vortex system generates and becomes strengthened as the running speed increases. However, the locations of flow separations on the train surface and the separation mechanism keep unchanged. In conditions with cross winds, three large vortices develop along the leeward side of the train, among which the weakest one has no obvious influence on the wake flow while the other two stretch to the tail of the train and combine with the helical vortices in the train wake. Thus, optimization of the aerodynamic performance of the
Aiming at shortening the design period and improve the design efficiency of the nose shape of high speed trains, a parametric shape optimization method is developed for the design of the nose shape has been proposed in the present paper based on the VMF parametric approach, NURBS curves and discrete control point method. 33 design variables have been utilized to control the nose shape, and totally different shapes could be obtained by varying the values of design variables. Based on the above parametric method, multi-objective particle swarm algorithm, CFD numerical simulation and supported vector machine regression model, multi-objective aerodynamic shape optimization has been performed. Results reveal that the parametric shape design method proposed here could precisely describe the three-dimensional nose shape of high speed trains and could be applied to the concept design and optimization of the nose shape. Besides, the SVM regression model based the multi-points criterion could accurately describe the nonlinear relationship between the design variables and objectives, and could be generally utilized in other fields. No matter the simplified model or the real model, the aerodynamic performance of the model after optimization has been greatly improved. Based on the SVR model, the nonlinear relation between the aerodynamic drag and the design variables is obtained, which could provide guidance for the engineering design and optimization.
As the running speed of high-speed trains increases, aerodynamic drag becomes the key factor which limits the further increase of the running speed and energy consumption. Aerodynamic lift of the trailing car also becomes the key force which affects the amenity and safety of the train. In the present paper, a simplified CRH380A high-speed train with three carriages is chosen as the model in order to optimize aerodynamic drag of the total train and aerodynamic lift of the trailing car. A constrained multi-objective optimization design of the aerodynamic head shape of high-speed trains based on adaptive non-dominated sorting genetic algorithm is also developed combining local function three-dimensional parametric approach and central Latin hypercube sampling method with maximin criteria based on the iterative local search algorithm. The results show that local function parametric approach can be well applied to optimal design of complex three-dimensional aerodynamic shape, and the adaptive non-dominated sorting genetic algorithm can be more accurate and efficient to find the Pareto front. After optimization the aerodynamic drag of the simplified train with three carriages is reduced by 3.2%, and the lift coefficient of the trailing car by 8.24%, the volume of the streamlined head by 2.16%; the aerodynamic drag of the real prototype CRH380A is reduced by 2.26%, lift coefficient of the trailing car by 19.67%. The variation of aerodynamic performance between the simplified train and the true train is mainly concentrated in the deformation region of the nose cone and tail cone. The optimization approach proposed in the present paper is simple yet efficient, and sheds lights on the constrained multi-objective engineering optimization design of aerodynamic shape of high-speed trains.
Based on the concepts of niche count and crowding distance, a modified multi-objective particle swarm optimization (MPSO) is introduced. The niche count and crowding distance are used to determine the globally best particle across four test cases using an external file. A comparative analysis was carried out between MPSO and non-dominated sorting multiobjective adaptive genetic algorithms, both real-coded and binary-coded. The results show that MPSO based on the crowding distance is best for getting the Pareto front, especially for problems with high-dimensional and non-continuous Pareto fronts. In order to verify the efficiency of MPSO in solving engineering problems, the optimal design of the aerodynamic nose shape of high-speed trains was undertaken using a modified vehicle modeling function (MVMF) parametric method. Taking the aerodynamic drag of the whole train (Cd) and the aerodynamic lift of the trailer car (Cl) as the optimization goals, the Kriging surrogate model was introduced to reduce the computational time, and the MPSO based on crowding distance was used to find the Pareto front. The optimization results show that MPSO is efficient at getting the Pareto front; compared to the original shape, the Cd and Cl of the optimal shape are reduced by 1.6% and 29.74%, respectively.
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