Aural comfort prediction method for high-speed trains under complex tunnel environments. (April 2020)

• Record Type: Journal Article
• Authors: Xie, Pengpeng; Peng, Yong; Wang, Tiantian; Wu, Zhifa; Yao, Song; Yang, Mingzhi; Yi, Shengen
• Is Part Of: Transportation research. Volume 81(2020)
• Publication Date: 2020-04
• Subjects: High-speed train -- Tunnel length -- Aural discomfort -- Ordinal regression -- Prediction model; Transportation -- Research -- Periodicals; Transportation -- Environmental aspects -- Periodicals; 354.76
• DOI: 10.1016/j.trd.2020.102284
• Languages: English
• ISSNs: 1361-9209

Abstract

Graphical abstract: Highlights: History of aural sensations was visualized during the train's running in tunnels. Effect of speed, seal index, car No. and tunnel length on ear comfort was revealed. Ear sensations were classified into four levels based on the duration of discomfort. Mathematic model was established to predict pressure-related ear comfort in trains. A way to seek for preferable speeds was put forward for given train and rail lines. Abstract: Aural comfort is negatively affected during a train's passage through various tunnel environments. The objective of this study was to propose a prediction model for determining optimal operation parameter combinations to improve train occupants' aural comfort. High-speed train model tests, combined with a mathematical transfer model, were used to obtain the interior pressure transients under varying speeds, tunnel lengths and seal indexes. Then, a middle ear finite element model was used to simulate the dynamic responses under the pressure transients, and three indicators were employed to assess the severity of aural sensations. Meanwhile, the aural discomfort were classified into four groups according to the duration. Based on the simulation results, the ordinal regression analysis method was used to reveal the effects of the considered factors on aural comfort. The results indicate that aural discomfort sensations begin when a train runs in the middle of a tunnel but are mitigated when it approaches the tunnel exit. Furthermore, aural discomfort is positively correlated with the train speed and the distance from the driver cabin of the head car but negatively correlated with the seal index and tunnel length. As a conclusion, a mathematical prediction model was established that incorporates factors including the train speed, seal index, tunnel length and car position. It can not only forecast aural sensations under certain operation parameters and tunnel environments but also be used for determining the optimal operation parameters to ensure the best aural sensations for high-speed-train occupants.