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

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.