PREDICTION OF PASSENGER EAR DISCOMFORT IN TUNNEL-RUNNING HIGH-SPEED TRAIN USING ARTIFICIAL NEURAL NETWORKS

H.M. Kang; H.B. Kwon

doi:10.6112/kscfe.2025.30.1.071

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2025 Vol.30, Issue 1 Preview Page Next Page

Original Article

PREDICTION OF PASSENGER EAR DISCOMFORT IN TUNNEL-RUNNING HIGH-SPEED TRAIN USING ARTIFICIAL NEURAL NETWORKS 인공신경망을 활용한 터널 주행 고속철도차량의 승객 이명감 예측

31 March 2025. pp. 71-81

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Abstract

An artificial neural network-based response surface was developed to predict passenger ear discomfort in high-speed trains traveling through tunnels. For this, axisymmetric flow analysis was conducted on high-speed trains running through tunnels, with variations in tunnel cross-sectional area, tunnel length, and train speed. Then, the time-dependent pressure change rate, derived from the pressure changes inside the train’s cabin, was obtained as the results of CFD computations. Subsequently, the response surface based on the artificial neural network was constructed from the results. The prediction errors between the response surface results and the actual CFD analysis values were within 2%. This confirmed the reliability and efficiency of the proposed passenger discomfort prediction method of the artificial neural network-based response surface.

Keywords

Passenger ear discomfort

High-Speed train

Tunnel

Artificial neural network

CFD

키워드

승객이명감

고속철도차량

터널

인공신경망

전산유체역학

References

2022, Ministry of Land, Infrastructure, and Transport, Development for core technologies and specification to increase the operation speed of the high speed train over 370kph.

2002, Raghunathan, R.S., Kim, H. and Setoguchi, T., "Aerodynamics of high-speed railway train," Prog. Aerosp. Sci., Vol.38, No.6-7, pp.469-514.

10.1016/S0376-0421(02)00029-5

2015, Kwon, H., "A study of the minimum cross-sectional area of high-speed railway tunnel satisfying passenger ear discomfort criteria", J. Comput. Fluids Eng., Vol.20, No.3, pp.62-69.

10.6112/kscfe.2015.20.3.62

2005, International Union of Railways, UIC code 779-11 2nd edition: Determination of railway tunnel cross-sectional areas on the basis of aerodynamic considerations.

2019, HS2, Train Technical Specification (HS2-HS2-RR-SPE-000-000007).

2024, Kim, B., Kim, N., Ahn, J. and Kwon, H., "Computational analysis to derive passenger ear discomfort criteria for 400 km/h high speed train," J. Korean Soc. Railw., Vol.27, No.12, pp.1055-1064.

10.7782/JKSR.2024.27.12.1055

2001, Kwon, H., A study on the unsteady compressible flow field induced by a high-speed train passing through a tunnel. Ph.D. dissertation, Seoul National University.

2023, Ministry of Land, Infrastructure, and Transport, Technical specification for high speed railway vehicles, KRTS-VE-Part31-2023(R2).

2013, Park, K., Jun, S., Baek, S., Cho, M., Yee, K. and Lee, D., "Reduced-order model with an artificial neural network for aerostructural design optimization," J. Aircr., Vol.50., No.4, pp.1106-1116.

10.2514/1.C032062

Information

Publisher :Korean Society for Computational Fluids Engineering
Publisher(Ko) :한국전산유체공학회
Journal Title :Journal of Computational Fluids Engineering
Journal Title(Ko) :한국전산유체공학회지
Volume : 30
No :1
Pages :71-81
Received Date : 2025-01-21
Revised Date : 2025-03-18
Accepted Date : 2025-03-20
DOI :https://doi.org/10.6112/kscfe.2025.30.1.071

Journal of Computational Fluids Engineering ISSN:1598-6071(Print) 3022-6252(Online) 한국전산유체공학회지

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