Evaluating a Thermoregulatory Model for Cooling Garment Applications With Transient Metabolic Rates

Current state-of-the-art thermoregulatory models do not predict body temperatures with the accuracies that are required for the development of automatic cooling control in liquid cooling garment (LCG) systems. Automatic cooling control would be beneficial in a variety of space, aviation, military, and industrial environments for optimizing cooling efficiency, for making LCGs as portable and practical as possible, for alleviating the individual from manual cooling control, and for improving thermal comfort and cognitive performance. In this paper, we adopt the Fiala thermoregulatory model, which has previously demonstrated state-of-the-art predictive abilities in air environments, for use in LCG environments. We compare the model’s tissue temperature predictions with analytical solutions to the bioheat equation, and with experimental data for a 700 W rectangular type activity schedule. The thermoregulatory model predicts rectal temperature, mean skin temperature, and body heat storage (BHS) with mean absolute errors of 0.13°C, 0.95°C, and 11.9 W·hr, respectively. Even though these accuracies are within state-of-the-art variations, the model does not satisfy the target BHS accuracy of ±6.5 W·hr. We identify model deficiencies, which will be addressed in future studies in order to achieve the strict BHS accuracy that is needed for automatic cooling control development.