An Improved Thermoregulatory Model for Cooling Garment Applications With Transient Metabolic Rates

Abstract

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 modify the blood flow dynamics of a state-of-the-art thermoregulatory model and identify a new signal, i.e. the rate of change of hypothalamus temperature weighted by the hypothalamus error signal, which governs the thermoregulatory response during conditions of simultaneously increasing core and decreasing skin temperatures. We compare temperature predictions with experimental data for a 700 W rectangular type activity schedule in an LCG environment. The new thermoregulatory model predicts rectal and mean skin temperatures with root mean square deviations of 0.09°C and 0.69°C, respectively, which results in a 44% reduction of the mean absolute body heat storage error. It appears that the new thermoregulatory model can, with some additional improvements outlined here, satisfy the very strict accuracy requirement that is needed for automatic cooling control development.