For improving the evaluative accuracy of thermal protective clothing performance,the estimated methods of thermal response of skin and the human body were comprehensively described and analyzed.This study reviewed the...For improving the evaluative accuracy of thermal protective clothing performance,the estimated methods of thermal response of skin and the human body were comprehensively described and analyzed.This study reviewed the one-dimensional(1D)and multi-dimensional heat transfer models of the skin heat transfer model,including the corresponding heat and moisture transfer.Further,it investigated the influence of moisture transfer in vivo heat transfer.Moreover,the thermo-physiological model with active regulation was analyzed,especially in the local extremes,such as fingers and toes.Additionally,future research trends are discussed in estimating thermal protective performance.In developing the thermal protective model,it is essential to consider the geometric structure,local heat thermoregulation of extremities,and mass transfer inside the skin.展开更多
The role of skin temperature as a determinant of human thermal sensation and comfort has gained increasing recognition,prompting a need for a systematic review.This review examines the relationship between skin temper...The role of skin temperature as a determinant of human thermal sensation and comfort has gained increasing recognition,prompting a need for a systematic review.This review examines the relationship between skin temperature and thermal sensation,synthesizing insights from 172 studies published since 2000.It uniquely focuses on the indispensable roles of local and mean skin temperatures,a perspective not comprehensively explored in previous literature.The review reveals that the most common measurement points for skin temperature are the face and hands,attributed to their higher thermal sensitivity and the practical ease of measurement.It establishes a clear linear relationship between mean skin temperature and user thermal sensation,though affected by the choice of measurement locations and number of points.A notable finding is the varying impact of local skin temperature on overall thermal sensation in changing environments,with local heating less influential than cooling.The review also uncovers demographic variations in thermal sensation,strongly influenced by differing skin temperatures across age groups,genders,and climatic regions.For example,elderly populations exhibit a decreased temperature sensitivity,especially towards warmth.Gender differences are also significant,with females experiencing higher skin temperatures in warmer environments and lower in colder ones.Machine learning(ML)-based methods,particularly those using classification tree and support vector machine(SVM)techniques,are increasingly used to predict thermal sensation and comfort by leveraging skin temperature data.While ML methods are prevalent,statistical regression-based approaches offer valuable empirical insights.Thermo-physiological model-based methods provide reliable results by incorporating detailed skin temperature dynamics.The review highlights a gap in understanding the influence of gender,age,and regional differences on thermal comfort across various environments.The study recommends conducting more detailed experiments to examine the impact of these factors more closely.It also suggests integrating individual demographic variables into ML models to personalize thermal comfort predictions.展开更多
基金Fundamental Research Funds for the Central Universities,China(No.2232022G-08)International Cooperation Fund of Science and Technology Commission of Shanghai M unicipality,China(No.21130750100)。
文摘For improving the evaluative accuracy of thermal protective clothing performance,the estimated methods of thermal response of skin and the human body were comprehensively described and analyzed.This study reviewed the one-dimensional(1D)and multi-dimensional heat transfer models of the skin heat transfer model,including the corresponding heat and moisture transfer.Further,it investigated the influence of moisture transfer in vivo heat transfer.Moreover,the thermo-physiological model with active regulation was analyzed,especially in the local extremes,such as fingers and toes.Additionally,future research trends are discussed in estimating thermal protective performance.In developing the thermal protective model,it is essential to consider the geometric structure,local heat thermoregulation of extremities,and mass transfer inside the skin.
文摘The role of skin temperature as a determinant of human thermal sensation and comfort has gained increasing recognition,prompting a need for a systematic review.This review examines the relationship between skin temperature and thermal sensation,synthesizing insights from 172 studies published since 2000.It uniquely focuses on the indispensable roles of local and mean skin temperatures,a perspective not comprehensively explored in previous literature.The review reveals that the most common measurement points for skin temperature are the face and hands,attributed to their higher thermal sensitivity and the practical ease of measurement.It establishes a clear linear relationship between mean skin temperature and user thermal sensation,though affected by the choice of measurement locations and number of points.A notable finding is the varying impact of local skin temperature on overall thermal sensation in changing environments,with local heating less influential than cooling.The review also uncovers demographic variations in thermal sensation,strongly influenced by differing skin temperatures across age groups,genders,and climatic regions.For example,elderly populations exhibit a decreased temperature sensitivity,especially towards warmth.Gender differences are also significant,with females experiencing higher skin temperatures in warmer environments and lower in colder ones.Machine learning(ML)-based methods,particularly those using classification tree and support vector machine(SVM)techniques,are increasingly used to predict thermal sensation and comfort by leveraging skin temperature data.While ML methods are prevalent,statistical regression-based approaches offer valuable empirical insights.Thermo-physiological model-based methods provide reliable results by incorporating detailed skin temperature dynamics.The review highlights a gap in understanding the influence of gender,age,and regional differences on thermal comfort across various environments.The study recommends conducting more detailed experiments to examine the impact of these factors more closely.It also suggests integrating individual demographic variables into ML models to personalize thermal comfort predictions.
