Degree Type

Dissertation

Date of Award

1999

Degree Name

Doctor of Philosophy

Department

Chemical and Biological Engineering

First Advisor

Derrick K. Rollins

Abstract

The physiology of the human thermoregulatory response to changes in environment and work load has been studied from the perspective of both the physiologist and the engineer. Both fields have brought certain skills and tools to the effort. An effort to bring together the understanding gained from these two perspectives has been lacking. In this work a model for the sweat response and a model for the thermoregulatory response to changes in room temperature were developed. These models were developed using the modeling tools of engineering and the knowledge contained in past experimental data;An empirical model of the sweat response in humans was created from six previously published sets of experimental data. The model was developed using Partial Least Squares (PLS). PLS made it possible to combined data from mulitiple, uncorrelated experiments, there by extracting information from all of the experiments into one model. Previous experiments were limited by small sample sets. Creating a single model from several experiments extended the input space covered in model without the need for expensive experimental work. The previous techniques used to develop the previous models were not able to investigate the multivariate nature of the sweat response. PLS reduced the dimensionality of the problem while keeping the ability to determine the importance of each of the individual physical parameters as well as their interactions;A model of the human body's regulation of skin temperature for changes in room temperature was developed using the Semi Empirical Technique (SET). Previous models of the thermoregulatory system had either been developed using the fundamental principles of heat transfer or were empirical in nature. The theoretical models were limited in there ability to completely describe the complicated system and all its interactions. The empirical models were limited by the amount of data they were developed from. The SET technique brought together the advantages of the theoretical and empirical models. By balancing the understanding gained from theory with the inherent information gained from data the SET model does not suffer the limitations of either of the previous techniques.

DOI

https://doi.org/10.31274/rtd-180813-13457

Publisher

Digital Repository @ Iowa State University, http://lib.dr.iastate.edu/

Copyright Owner

Jennifer Jane Walker

Language

en

Proquest ID

AAI9940252

File Format

application/pdf

File Size

116 pages

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