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

Wang, R., Zhang, J. J. and You, L.H., 2018. A survey of parametric modelling methods for designing the head of a high-speed train. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit. (In Press)

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Official URL: http://journals.sagepub.com/home/pif

DOI: 10.1177/0954409718756558

Abstract

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.

Item Type:Article
ISSN:0954-4097
Additional Information:Project title: PDE-based geometric modelling, image processing, and shape reconstruction. Funder name: EU Horizon 2020. Funder reference: H2020-MSCA-RISE-2017-778035
Uncontrolled Keywords:Parametric methods; head design; high-speed trains; 2D contour representations in side view; profile definitions for 3D shapes; CATIA-based parameterization; mesh deformation-based modelling
Group:Faculty of Media & Communication
ID Code:30465
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:12 Mar 2018 16:32
Last Modified:12 Mar 2018 16:32

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