Multi-objective optimization design method of the high-speed train head

Maruyama, Yu; Zhang, Jiye; Zhang, Weihua

doi:10.1631/jzus.a1300109

Cited by 23 publications

(7 citation statements)

References 19 publications

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“…Li et al [11] investigated a numerical simulation method for the interaction between airflow and high-speed trains, indicating that it is necessary to consider the interaction between airflow and a high-speed train subjected to crosswind. Yu et al [12] selected the aerodynamic drag force and wheel unloading rate as the optimization targets, and proposed a multi-objective shape optimization design method for high-speed train head. In computational fluid dynamics (CFD), the selection of the turbulence model is critical.…”

Section: Introductionmentioning

confidence: 99%

Effect of RANS Turbulence Model on Aerodynamic Behavior of Trains in Crosswind

Qin

Zhang

2019

Chin. J. Mech. Eng.

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The numerical simulation based on Reynolds time-averaged equation is one of the approved methods to evaluate the aerodynamic performance of trains in crosswind. However, there are several turbulence models, trains may present different aerodynamic performances in crosswind using different turbulence models. In order to select the most suitable turbulence model, the inter-city express 2 (ICE2) model is chosen as a research object, 6 different turbulence models are used to simulate the flow characteristics, surface pressure and aerodynamic forces of the train in crosswind, respectively. 6 turbulence models are the standard k-ε, Renormalization Group (RNG) k-ε, Realizable k-ε, Shear Stress Transport (SST) k-ω, standard k-ω and Spalart-Allmaras (SPA), respectively. The numerical results and the wind tunnel experimental data are compared. The results show that the most accurate model for predicting the surface pressure of the train is SST k-ω, followed by Realizable k-ε. Compared with the experimental result, the error of the side force coefficient obtained by SST k-ω and Realizable k-ε turbulence model is less than 1 %. The most accurate prediction for the lift force coefficient is achieved by SST k-ω, followed by RNG k-ε. By comparing 6 different turbulence models, the SST k-ω model is most suitable for the numerical simulation of the aerodynamic behavior of trains in crosswind. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Section: Introductionmentioning

confidence: 99%

Effect of RANS Turbulence Model on Aerodynamic Behavior of Trains in Crosswind

Qin

Zhang

2019

Chin. J. Mech. Eng.

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“…The three parameters l 1 , l 2 , and R 1 cannot describe such complexity. Therefore, Yu et al 28,29 and Li et al 30 established the 3D model with CATIA and generated a VBscript by using the macro recording function of the CATIA, then the left part of this model was generated with the script because the train head had a symmetrical structure. Finally, the deformation of the train head was controlled by modifying the script of the CATIA with the software MATLAB.…”

Section: Catia-based Parametric Modelling Methodsmentioning

confidence: 99%

A survey of parametric modelling methods for designing the head of a high-speed train

Wang

Zhang

Bian

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part F:

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With the continuous increase of the running speed, the head shape of the high-speed train (HST) turns out to be a critical factor for further speed boost. In order to cut down the time used in the HST head design and improve the modelling efficiency, various parametric modelling methods have been widely applied in the optimization design of the HST head to obtain an optimal head shape so that the aerodynamic effect acting on the head of HSTs can be reduced and more energy can be saved. This paper reviews these parametric modelling methods and classifies them into four categories: 2D, 3D, CATIA-based, and mesh deformation-based parametric modelling methods. Each of the methods is introduced, and the advantages and disadvantages of these methods are identified. The simulation results are presented to demonstrate that the aerodynamic performance of the optimal models constructed by these parametric modelling methods has been improved when compared with numerical calculation results of the original models or the prototype models of running trains. Since different parametric modelling methods used different original models and optimization methods, few publications could be found which compare the simulation results of the aerodynamic performance among different parametric modelling methods. In spite of this, these parametric modelling methods indicate more local shape details will lead to more accurate simulation results, and fewer design variables will result in higher computational efficiency. Therefore, the ability of describing more local shape details with fewer design variables could serve as a main specification to assess the performance of various parametric modelling methods. The future research directions may concentrate on how to improve such ability.

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“…As discussed above, the number of control points and weight deformation are chosen to be design variables. Inspired by the algorithm of deforming a curve discussed in Yu et al (2013), we propose a new weight deformation algorithm to deform a surface. It transforms multiple weight variables into a single weight deformation, makes the deformation bigger and bigger when moving from the boundaries to the centre of a NURBS surface, and has no effects on the four boundaries of a NURBS surface patch to ensure the continuity between adjacent NURBS surface patches.…”

Section: Design Variablesmentioning

confidence: 99%

Optimal NURBS conversion of PDE surface-represented high-speed train heads

et al. 2019

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The head shape of high-speed trains has become a critical factor in boosting the speed further. Aerodynamic simulation-based optimization is a dominant method to obtain the optimal head shape which relies on detailed train head models defined by a lot of design variables. Since aerodynamic simulation-based optimization involves heavy calculations, too many design variables not only causes high computational costs, but also makes the optimal solution difficult to obtain. Therefore, how to use few design variables to define detailed train head model is the key to success. Partial differential equation (PDE)-based geometric modelling which creates a complicated PDE patch with few design variables provides an effective solution to this problem. In addition, it also has the advantage of naturally maintaining any high-order continuities between two adjacent surfaces which is very important in designing highly smooth train heads to achieve excellent aerodynamic performance. At the present time, PDE-based geometric modelling cannot be directly applied in computer-aided design (CAD), computer-aided manufacturing (CAM), and computer-aided engineering (CAE) since it has not become an industrial standard. In contrast, non-uniform rational B-splines (NURBS) are commonly used in CAD, CAM, CAE, and many other engineering fields. They have already become part of industry wide standards. In order to apply PDE-based geometric modelling in shape design of high-speed train heads for CAD etc., how to optimally convert PDE surfaces into NURBS surfaces must be addressed. In this paper, a new method of achieving optimal conversion of PDE surfaces representing high-speed train heads into NURBS surfaces is developed. It takes control points and weight deformations of NURBS surfaces to be design variables, and the error between NURBS surfaces and PDE surfaces as the objective function. The least squares fitting and the genetic algorithm are combined to obtain the optimal conversion between PDE surfaces and NURBS surfaces. The application examples demonstrate the effectiveness of the developed method.

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Multi-objective optimization design method of the high-speed train head

Cited by 23 publications

References 19 publications

Effect of RANS Turbulence Model on Aerodynamic Behavior of Trains in Crosswind

Effect of RANS Turbulence Model on Aerodynamic Behavior of Trains in Crosswind

A survey of parametric modelling methods for designing the head of a high-speed train

Optimal NURBS conversion of PDE surface-represented high-speed train heads

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