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Genetically Aerodynamic Optimization of High-Speed Train Based on the Artificial Neural Network Method

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Computational and Experimental Simulations in Engineering (ICCES 2019)

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 75))

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Abstract

The optimization of the car body cross-section shape of high-speed train under cross winds has been performed using genetic algorithms (GA), wherein the computational fluid dynamics (CFD) simulation model was established based on Design of Experiments (DOE), and then the aerodynamic performance of a train subjected to crosswind was calculated by CFD. However, directly using the CFD to compute the high-speed trains performances in an optimization scheme is not suitable for the optimization process. This is because the GA requires a large number of CFD solver calls, which increase the computational cost and time-consuming of the optimization process. To decrease the CFD simulation computational cost, an approximate substituted Artificial Neural Network (ANN) model was proposed. The ANN models were trained and tested using the data obtained from the CFD simulation model, and its correlation coefficient values are considerably close to 1, which indicates fine accuracy and prediction capability. Meanwhile, the impact of the car body cross-section design parameters on the aerodynamic performance has been also investigated, and the result show that the design parameters interrelate each other and jointly impact the aerodynamic performance of high-speed trains. After the optimization, the overturn moment coefficient has been reduced by 17.5%, and the lift force and slid force coefficient have been reduced by 11.5% and 8.05%, respectively. Compared to the original shape, the pressure coefficient of optimal shape has great improvement. The analysis method can provide a frame of reference for the high-speed train safe operation running on the track under crosswind conditions.

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Acknowledgements

This work presented here was supported by National Key R&D Program of China under the contract number 2017YFB1300600, and by the National Natural Science Foundation of China under the contract numbers 11772103 and 61304037.

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Authors and Affiliations

  1. School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, 150001, China

    Fu Tao, Chen Zhaobo & Wang Zhonglong

Authors
  1. Fu Tao
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  2. Chen Zhaobo
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  3. Wang Zhonglong
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Corresponding author

Correspondence to Chen Zhaobo .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, Tokyo University of Science, Noda, Chiba, Japan

    Hiroshi Okada

  2. Department of Mechanical Engineering, Texas Tech University, Lubbock, TX, USA

    Satya N. Atluri

Appendix: Orthogonal Table L25(65) and the Numerical Results

Appendix: Orthogonal Table L25(65) and the Numerical Results

See Table 106.2.

Table 106.2 Orthogonal table L25(65) and the numerical results for the aerodynamic performance of high-speed train
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Tao, F., Zhaobo, C., Zhonglong, W. (2020). Genetically Aerodynamic Optimization of High-Speed Train Based on the Artificial Neural Network Method. In: Okada, H., Atluri, S. (eds) Computational and Experimental Simulations in Engineering. ICCES 2019. Mechanisms and Machine Science, vol 75. Springer, Cham. https://doi.org/10.1007/978-3-030-27053-7_106

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  • DOI: https://doi.org/10.1007/978-3-030-27053-7_106

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27052-0

  • Online ISBN: 978-3-030-27053-7

  • eBook Packages: EngineeringEngineering (R0)

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