Aerodynamic Optimization of the ICE 2 High-Speed Train Nose using a Genetic Algorithm and Metamodels

Paniagua, Jorge Muñoz; Garcı́a, Javier Garcı́a; Martínez, Antonio Crespo; Krajnović, Siniša

doi:10.4203/ccp.98.165

Cited by 3 publications

(5 citation statements)

References 13 publications

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“…The Bézier curve model. Mun˜oz-Paniagua et al 18 replaced the Hicks-Henne shape functions with Be´zier curves to define the parametric contour lines of the train head and used this contour line to analyse…”

Section: Two-dimensional Parametric Modelling Methodsmentioning

confidence: 99%

“…4,9,12,13 The idealized train model, the 2D elliptic curve model, and the Be´zier curve model were also designed to study the aerodynamic performance of the HST head. 8,[14][15][16][17][18] In contrast, 3D modelling methods use more factors to improve the modelling accuracy but this means more time is consumed in the process of optimization. Generally, there are three ways to parameterize the head shape in the 3D space.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Two-dimensional Parametric Modelling Methodsmentioning

confidence: 99%

Section: Introductionmentioning

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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“…For example, the aerodynamic resistance of the train in a tunnel is reduced by approximately 50% if the shape of the train nose is changed from blunt to streamlined (Choi and Kim 2014). Munoz-Paniagua et al (2012) and Paniagua et al (2011) not only optimized the train nose shape to reduce aerodynamic drag in the open air but also applied a genetic algorithm and numerical simulation method to the nose shape optimization of a high-speed train in a tunnel to minimize the pressure gradient of the compression wave and aerodynamic resistance of the train (Muñoz- Paniagua et al 2014). Doi et al (2010) measured the pressure wave of a train traversing a tunnel with a 1/30 scale moving model test device and analyzed the influence of the nose shape of the train on the strength and form of the pressure wave.…”

Section: Introductionmentioning

confidence: 96%

“…Kwak et al (2013) established a three-dimensional control function for the train nose, investigated the influence of the cross-sectional shape of the train nose on the aerodynamic resistance of the train using the Broyden-Fletcher-Goldfarb-Shanno algorithm, and obtained a train nose shape that could reduce the aerodynamic resistance by 23%. Muñoz- Paniagua et al (2012) and Paniagua et al (2011) used a combination of genetic algorithms, metamodels, and artificial neural networks to optimize the nose shape and reduce the aerodynamic drag of high-speed trains. They also provided a schematic diagram of the optimization process and introduced the most relevant elements in detail.…”

Section: Introductionmentioning

confidence: 99%

Numerical Analysis of the Effect of Streamlined Nose Length on Slipstream of High-Speed Train Passing through a Tunnel

2022

JAFM

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The improved delayed detached eddy simulation (IDDES) method was employed based on the shear-stress transport (SST) - two-equation turbulence model to simulate the slipstream distribution characteristics of a high-speed train traversing a tunnel. The accuracy of the numerical simulation method was verified through a full-scale test. First, the wake vortex structure and the distribution of slipstreams of the train with a streamlined nose length of 7 m running in a tunnel were analyzed. Then, the influence of the streamlined nose length on the wake dynamics and slipstream was compared and analyzed. The slipstream positive peak decreased with increasing distance from the top of the rail and center of the track. As the streamlined nose length increases, the vortex intensity in the wake area weakens; moreover, the influence ranges of the wake vortex and the slipstream positive peak value become smaller. Compared with the results of a train having a streamlined nose length of 5 m, the slipstream positive peak value at 1.4 m from the top of the rail and 100 m from the tunnel entrance decreased by 46.6% from that of a train with a streamlined nose length of 9 m.

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“…A high-speed train head involves a lot of design variables and high smoothness requirements. Parametric surface modelling methods such as B-splines surfaces, Bézier surfaces and non-uniform rational B-splines (NURBS) surfaces are widely used in geometric design of the high-speed train head (Yao et al 2016;Suzuki and Nakade 2013;Muñoz Paniagua et al 2011). However, they are not ideal in using few surface patches to describe complicated shapes, easily and accurately controlling surface shapes, and achieving any highorder continuity which are required in shape modelling of high-speed strain heads.…”

Section: Introductionmentioning

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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Aerodynamic Optimization of the ICE 2 High-Speed Train Nose using a Genetic Algorithm and Metamodels

Cited by 3 publications

References 13 publications

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

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

Numerical Analysis of the Effect of Streamlined Nose Length on Slipstream of High-Speed Train Passing through a Tunnel

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

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