Kriging-based approximate optimization of high-speed train nose shape for reducing micropressure wave

Jongsoo Lee, J. Kim

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Trains travelling at high-speed into a tunnel generate micropressure waves causing an explosive noise at the tunnel exit. This paper deals with the design of high-speed railways nose shape design to minimize the maximum micropressure wave which is known to be mainly affected by train speed, train-to-tunnel area ratio, slenderness and shape of train nose, etc. It is more efficient to develop a proper approximate meta-model for replacing the real analysis code in the context of approximate design optimization. The study has adopted the Kriging meta-model; the central of the paper is to develop and examine Kriging for use in the sequential approximate optimization process. In the sequential approximate optimization process, Owen's random orthogonal arrays and D-optimal design are used to generate training data for building approximate models. The paper describes how Kriging works and how much it is efficient as an approximation model in the context of approximate optimization. Consequently, the present study suggests the new optimal nose shape that is more improved than currently used design in terms of micropressure wave. JRRT110

Original languageEnglish
Pages (from-to)263-270
Number of pages8
JournalProceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
Volume221
Issue number2
DOIs
Publication statusPublished - 2007 Aug 3

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Tunnels
Random processes
Optimal design
Design optimization

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering

Cite this

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abstract = "Trains travelling at high-speed into a tunnel generate micropressure waves causing an explosive noise at the tunnel exit. This paper deals with the design of high-speed railways nose shape design to minimize the maximum micropressure wave which is known to be mainly affected by train speed, train-to-tunnel area ratio, slenderness and shape of train nose, etc. It is more efficient to develop a proper approximate meta-model for replacing the real analysis code in the context of approximate design optimization. The study has adopted the Kriging meta-model; the central of the paper is to develop and examine Kriging for use in the sequential approximate optimization process. In the sequential approximate optimization process, Owen's random orthogonal arrays and D-optimal design are used to generate training data for building approximate models. The paper describes how Kriging works and how much it is efficient as an approximation model in the context of approximate optimization. Consequently, the present study suggests the new optimal nose shape that is more improved than currently used design in terms of micropressure wave. JRRT110",
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