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 study, 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 validate the numerical formulation with analytical solutions to the bioheat equation, and find our model to be accurate and stable with a variety of different grid configurations. We then compare the thermoregulatory model's tissue temperature predictions with experimental data where individuals, equipped with an LCG, exercise according to a 700 W rectangular type activity schedule. The root mean square (RMS) deviation between the model response and the mean experimental group response is 0.16°C for the rectal temperature and 0.70°C for the mean skin temperature, which is within state-of-the-art variations. However, with a mean absolute body heat storage error BHS ε of 9.7 W·h, the model fails to satisfy the ±6.5 W·h accuracy that is required for the automatic LCG cooling control development.In order to improve model predictions, we modify the blood flow dynamics of the thermoregulatory model. Instead of using step responses to changing requirements, we introduce exponential responses to the muscle blood flow and the vasoconstriction command. We find that such modifications have an insignificant effect on temperature predictions. However, a new iv vasoconstriction dependency, i.e. the rate of change of hypothalamus temperature weighted by the hypothalamus error signal (ΔT hy ·dT hy /dt), proves to be an important signal that governs the thermoregulatory response during conditions of simultaneously increasing core and decreasing skin temperatures, which is a common scenario in LCG environments. With the new ΔT hy ·dT hy /dt dependency in the vasoconstriction command, the BHS ε for the exercise period is reduced by 59% (from 12.9 W·h to 5.2 W·h). Even though the new BHS ε of 5.8 W·h for the total activity schedule is within the target accuracy of ±6.5 W·h, BHS ε fails to stay within the target accuracy during the entire activity schedule. With additional improvements to the central blood pool formulation, the LCG boundary condition, and the agreement between model set-points and actual experimental initial conditions, it seems possible to achieve the strict accuracy that is needed for automatic cooling control development. v ACKNOWLEDGMENTS

Predicting Temperature Changes During Cold Water Immersion and Exercise Scenarios: Application of a Tissue–Blood Interactive Whole-Body Model

“…The whole-body model incorporates variations in metabolic heat generation rates, local blood perfusion rates, shivering, and sweating to compute variations in T c , T blood , and T wt using transient heat transfer formulations. Cold water immersion and exercise at constant intensity are among the preferred scenarios for assessing human body models [9]. Both ambient temperature and h values can be varied or kept constant, thereby allowing the model to demonstrate its capability to evaluate the influence of variations in environmental conditions on body temperature.…”

“…The human body strives to maintain T c close to a normothermic value of approximately 37 C [9]. This is made possible, to a large extent, by the circulatory system that helps in blood perfusion redistribution throughout the human body by either increasing or decreasing cardiac output during cold or heat stress conditions.…”

Development of a Cockpit-Pilot Model for Thermal Comfort Optimization During Long-Mission Flight

“…To get a better understanding of how a pilot's body responds to various thermal cockpit conditions a pilot model based on Westin's improved thermoregulatory model, 9 which in turn has Filas thermoregulatory model 7,8 as foundation, was implemented. The model was chosen because it is considered to be the most suitable to use for a pilot and because it has been validated for a broad amount of environments.…”