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Appl. Sci. 2017, 7(2), 196; doi:10.3390/app7020196

Surrogate Based Optimization of Aerodynamic Noise for Streamlined Shape of High Speed Trains

Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
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Academic Editors: Roberto Citarella and Luigi Federico
Received: 3 January 2017 / Revised: 6 February 2017 / Accepted: 13 February 2017 / Published: 17 February 2017

Abstract

Aerodynamic noise increases with the sixth power of the running speed. As the speed increases, aerodynamic noise becomes predominant and begins to be the main noise source at a certain high speed. As a result, aerodynamic noise has to be focused on when designing new high-speed trains. In order to perform the aerodynamic noise optimization, the equivalent continuous sound pressure level (SPL) has been used in the present paper, which could take all of the far field observation probes into consideration. The Non-Linear Acoustics Solver (NLAS) approach has been utilized for acoustic calculation. With the use of Kriging surrogate model, a multi-objective optimization of the streamlined shape of high-speed trains has been performed, which takes the noise level in the far field and the drag of the whole train as the objectives. To efficiently construct the Kriging model, the cross validation approach has been adopted. Optimization results reveal that both the equivalent continuous sound pressure level and the drag of the whole train are reduced in a certain extent. View Full-Text
Keywords: aerodynamic noise; NLAS; kriging model; cross validation; equivalent continuous SPL aerodynamic noise; NLAS; kriging model; cross validation; equivalent continuous SPL
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Sun, Z.; Zhang, Y.; Yang, G. Surrogate Based Optimization of Aerodynamic Noise for Streamlined Shape of High Speed Trains. Appl. Sci. 2017, 7, 196.

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