Development of an Advanced Clothing Model that Considers Heat and Moisture Transport

Despite recent advancements in the development of human thermal physiology models, clothing is often considered in only a rudimentary manner, that is, as a boundary condition at the skin characterized by its bulk thermal and evaporative impedances. While such an approach may be sufficient for thermoregulation modeling of humans operating in relatively low thermal burden scenarios, e.g., within an office environment, a clothing thermal model that does not explicitly consider the layer-to-layer transport of vapor can yield incorrect results, especially under conditions corresponding to high thermal stress or extreme environments. For example, vapor diffusing through layers of clothing can reach its dew point, in which case a phase change will occur that can fundamentally affect the transport of moisture and heat within the clothing ensemble. Specifically, the evaporation and condensation of moisture within the fabric layers can affect liquid content and temperature and, ultimately, affect thermo-physiological response and thermal comfort perception. Development of an advanced clothing model that considers heat and moisture transport within the fabric layers can improve the accuracy of human thermal predictions in scenarios where clothing-based condensation and evaporation occur. This paper describes how a general heat transfer software modeling tool was modified to consider the transport of vapor, the storage of liquid, and the thermal effects of phase change within a clothing ensemble. The advanced clothing moisture model was tested by comparing its predictions to measurements derived from sweating hot plate experiments and human physiological experiments. The model was able to reproduce the thermal and moisture results from tests performed under conditions in which condensation within the clothing layers was known to occur. This study demonstrates the importance of considering layer-to-layer vapor transport in clothing models and its impact on human thermal modeling predictions.