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Research Papers

# An Improved Thermoregulatory Model for Automatic Cooling Control Development in Liquid Cooling Garment Systems

[+] Author and Article Information
Johan K. Westin

Department of Mechanical, Materials and Aerospace Engineering, University of Central Florida, 4000 Central Florida Boulevard, Orlando, FL 32816johan.westin@knights.ucf.edu

Jayanta S. Kapat, Louis C. Chow

Department of Mechanical, Materials and Aerospace Engineering, University of Central Florida, 4000 Central Florida Boulevard, Orlando, FL 32816

J. Thermal Sci. Eng. Appl 2(1), 011002 (Jul 06, 2010) (11 pages) doi:10.1115/1.4001482 History: Received May 13, 2009; Revised March 19, 2010; Published July 06, 2010; Online July 06, 2010

## Abstract

Current state-of-the-art thermoregulatory models do not simulate body temperature responses 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. It would optimize cooling efficiency, aid in making LCGs as portable and practical as possible, alleviate the individual from manual cooling control, and improve thermal comfort and cognitive performance. In this study, we implement an available state-of-the-art thermoregulatory model in a LCG environment and compare the thermal model response with experimental data for a 700 W rectangular type metabolic rate schedule. We modify the blood flow dynamics of the thermoregulatory model and identify a new vasoconstriction 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. With this new vasoconstriction dependency, the thermoregulatory model simulates rectal and mean skin temperature responses with root mean square deviations of $0.10°C$ and $0.48°C$, respectively, which results in 40% and 17% reductions in the mean and peak body heat storage errors, respectively. Although the new model’s mean body heat storage error is within the allowable by an 11% margin, the peak body heat storage error exceeds the allowable by 222%, indicating that further refinements are needed. With additional improvements to the set-point temperatures, the central blood pool formulation, and the LCG boundary condition, it seems possible to achieve the strict accuracy that is needed for the development of automatic cooling control in LCG systems.

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## Figures

Figure 1

Simplified representation of the thermoregulatory system (26)

Figure 2

Body segmentation of the Fiala thermoregulatory model (15)

Figure 3

Metabolic rate during the activity schedule

Figure 4

LCG inlet water temperature (mean±standard deviation) during the activity schedule (38)

Figure 5

Comparing model response with exact analytically derived temperatures after a sudden supply of blood perfusion and metabolic heat in a homogeneous cylinder

Figure 6

Comparing model response with exact analytically derived temperature profiles in a sphere with eight physiologically distinct compartments

Figure 7

Simulated sweat production and evaporation rates of the old model during the activity schedule

Figure 8

Comparing simulated vasoconstriction, vasodilation, and skin blood flow responses of the old and new models

Figure 9

Comparing simulated rectal and mean skin temperature responses of the old and new models with experimental data and recommended accuracy limits

Figure 10

Comparing simulated body heat storage responses of the old and new models with steady state comfort band

Figure 11

Comparing absolute body heat storage error responses of the old and new models with comfort band and target accuracy

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