Modelling and experimental study of thermo-physiological responses of human exercising in cold environments

服装 气流 均方误差 皮肤温度 环境科学 通风(建筑) 空气层 空气温度 自然通风 水分 模拟 气象学 大气科学 机械 材料科学 数学 热力学 图层(电子) 计算机科学 工程类 物理 统计 复合材料 生物医学工程 考古 历史
作者
Feiyu Chen,Ming Fu,Yayun Li,Shifei Shen,Xian Guo
出处
期刊:Journal of Thermal Biology [Elsevier BV]
卷期号:109: 103316-103316 被引量:6
标识
DOI:10.1016/j.jtherbio.2022.103316
摘要

A numerical human thermo-physiological model is developed with the consideration of characteristics of exercising people in cold environments. The developed model is characterized by: 1) the concept of net exercise efficiency which is used to correct the calculation of metabolic heat production by excluding mechanical energy; 2) the effects of low temperature on basal metabolic rate and basal blood flow rate; 3) the integration with a multi-layer clothing model to calculate the heat and moisture transfer through the clothing system, which takes into account the air gaps between the clothing layers to reflect the ventilation and air penetration effect from the ambient wind. Human subject experiment is conducted in a climate chamber to validate the proposed model. The human subject experiment is also carried out in a cold environment (-5 °C) combined with different air velocity conditions (still air, 2 m/s), taking into account the activities of different intensities (standing statically, 2 km/h walking and 7 km/h running). Thermo-physiological parameters including the core temperature, 8-point local skin temperatures and the clothing layer temperatures, are measured during the experiment. Comparison between the predicted and experimental results gives the root mean squared error (RMSE) of core temperature and mean skin temperature of 0.06-0.10 °C and 0.17-0.27 °C, respectively. RMSE values for local skin and clothing layer temperatures are no higher than 1.5 °C and most within 0.8 °C. The model is also validated with published data under various ambient temperature and activity intensity conditions. The proposed model is shown to be capable of predict the thermo-physiological responses of people exposed and exercising in cold environments.
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