干旱区生态环境知识资源中心

Quantifying the impact of clothing thermal and evaporation resistance is essential to providing representative boundary conditions for physiological modeling. In many models, sweat is assumed to drip off the skin surface to the environment and is not captured in clothing. In high metabolic rate and high temperature and humidity conditions the sweat produced by the body has the potential to saturate semipermeable clothing ensembles, changing the assumptions of the model. Workers, athletes and soldiers commonly wear encapsulating versions of such clothing to protect against environmental hazards. A saturated clothing model is proposed based on the ASHRAE two-node model using a saturated spot element in parallel with the existing method to account for sweat absorbed in the clothing. The work uses fundamental heat and mass transfer principles, modifying the existing formula using clothing measurements and basic assumptions. The effectiveness of the model is demonstrated by comparing the predictions of the original and proposed models, to the results of 21 soldiers exercising. The soldiers wore combat pants and shirt, helmet, gloves, shoes, socks, and underwear, and walked in a thermal chamber for 2 h at 42.2 degrees C dry bulb temperature, 54.4 degrees C wet bulb temperature, 20% relative humidity, and airspeed of 2 m/s. Core temperature, seven skin temperatures, heart rate, and total sweat loss were measured. The original model provides an average core temperature difference compared with the human subject results of 1.31 degrees C (SD = 0.557 degrees C) while the modified model improves the final prediction of core temperature to within an average of 0.15 degrees C (SD = 0.383 degrees C). The new model shows an improvement in the prediction of human core temperature under the tested conditions where dripping sweat will saturate clothing. The format can be used in multi-segmented thermal models and can continue to be developed and improved as more information on wetted clothing properties become available.