Thermal sensation prediction model for high-speed train occupants based on skin temperatures and skin wettedness

• Published in: International journal of biometeorology Vol. 68; no. 2; pp. 289 - 304
• Main Authors: Zhou, Wenjun, Yang, Mingzhi, Peng, Yong, Xiao, Qiang, Fan, Chaojie, Xu, Diya
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2024; Springer Nature B.V
• Subjects: Animal Physiology; Biological and Medical Physics; Biophysics; Body parts; Body temperature; Carriages; Design optimization; Earth and Environmental Science; Environment; Environmental conditions; Environmental Health; equations; High speed rail; Locomotives; Meteorology; Modelling; Original Paper; Passenger comfort; Passengers; Plant Physiology; Prediction models; Predictions; regression analysis; Sensation; Skin; Skin temperature; Systems design; systems engineering; Temperature; Temperature gradients; Thermal comfort; High-speed train passengers; Thermal comfort; Skin parameters; Multivariate regression analysis; Prediction model
• ISSN: 0020-7128; 1432-1254; 1432-1254
• DOI: 10.1007/s00484-023-02590-5

Passenger thermal comfort in high-speed train (HST) carriages presents unique challenges due to factors such as extensive operational areas, longer travel durations, larger spaces, and higher passenger capacities. This study aims to propose a new prediction model to better understand and address thermal comfort in HST carriages. The proposed prediction model incorporates skin wettedness, vertical skin temperature difference ( ΔTd ), and skin temperature as parameters to predict the thermal sensation vote (TSV) of HST passengers. The experiments were conducted with 65 subjects, evenly distributed throughout the HST compartment. Thermal environmental conditions and physiological signals were measured to capture the subjects’ thermal responses. The study also investigated regional and overall thermal sensations experienced by the subjects. Results revealed significant regional differences in skin temperature between upper and lower body parts. By analyzing data from 45 subjects, We analyzed the effect of 25 variables on TSV by partial least squares (PLS), from which we singled out 3 key factors. And the optimal multiple regression equation was derived to predict the TSV of HST occupants. Validation with an additional 20 subjects demonstrated a strong linear correlation (0.965) between the actual TSV and the predicted values, confirming the feasibility and accuracy of the developed prediction model. By integrating skin wettedness and ΔTd with skin temperature, the model provides a comprehensive approach to predicting thermal comfort in HST environments. This research contributes to advancing thermal comfort analysis in HST and offers valuable insights for optimizing HST system design and operation to meet passengers’ comfort requirements.