During flight missions, fighter pilots are in general exposed to vast amounts of stress including mild hypoxia, vibrations, high accelerations, and thermal discomfort. It is interesting to predict potential risks with a certain mission or flight case due to these stresses to increase safety for fighter pilots. The most predominant risk is typically thermal discomfort which can lead to serious health concerns. Extensive exposure to high or low temperature in combination with a demanding work situation weakens the physical and mental state of the pilot and can eventually lead to life-threatening conditions. One method to estimate the physical and mental state of a person is to measure the body core temperature. The body core temperature cannot be measured continuously during flight and needs to be estimated by using for instance a human thermoregulatory model. In this study, a model of the human thermoregulatory system and the cockpit environment is developed. Current thermoregulatory models are not customized for fighter pilots but a model developed by Fiala et al. in 2001, which has previously shown good performance in both cold and warm environments as well as for various activation levels for the studied person, is used as a theoretical foundation. Clothing layers are implemented in the model corresponding to clothes used by pilots in the Swedish air force flying the fighter aircraft Gripen E in warm outside conditions. Cooling garments and air conditioning systems as well as avionics, canopy, and cockpit air are included in the model to get a realistic description of the cockpit environment. Input to the model is a flight case containing data with altitude and velocity of the fighter during a mission. human heat transfer; body temperature regulation; physiological model;cooling garment; cockpit modeling

By using a pragmatic paradigm, different research methodologies were employed in this thesis. MS-related symptoms may be exaggerated due to heatsensitivity and it is supposed that cooling garments relieve the symptoms. The effects of wearing a Rehband® vest were evaluated in a sample of 42 persons with MS in a randomised controlled crossover study. Both objective and subjective statistically significant improvements were found when a cooled Rehband® vest was worn compared to the wearing of a room-tempered vest. Using a repeated-measures design, 10m and 30m timed walks and Timed Up and Go were studied in 42 persons with MS. Reproducibility was investigated within and between test points. High reproducibility was found both within (r=0.97–0.98) and between measure points (r=0.91–0.93). The correlation between the three tests was high (r=0.85). Differences at –23% to +40% were established as being needed to detect genuine changes. Severity of MS infl uenced the size of the differences, especially for the 30m timed walk test. The 12-item MS Walking Scale was translated and used in a cross-sectional study. Out of 81 persons with MS, 89–96% perceived limitations in standing or walking. The internal consistency of the scale was acceptable for nine items (0.69–0.84). The concurrent validity between the 12-item MS Walking Scale and the investigated objective tests was low: Berg Balance Scale (r=–0.368**), Four Square Step Test (r=0.338**) and Timed Up and Gocognitive (r=0.319*). A prevalence of falling was found at 63% in a longitudinal cohort study with prospectively registered falls including 76 persons with MS. The odds of falling were fi ve fold when there was a reported need of using a walking aid indoors and outdoors and by 2.5 to 15.6 times while there was disturbed proprioception, depending on severity. The highest sensitivity was found for the Berg Balance Scale (94%) and the highest specifi city was found for the 12-item MS Walking Scale (82%). Positive predictive values at 70–83% were found for the Berg Balance Scale, Timed Up and Gocognitive, the Four Square Step Test and the 12-item MS Walking Scale. Finally, we explored and described factors that persons with MS perceive as related to accidental falls. A content analysis with a deductive approach was chosen. By conducting interviews, we found previously untargeted factors: divided attention, reduced muscular endurance, fatigue and heat-sensitivity. The content of the interviews also gave support to previously reported risk factors such as changes in gait pattern, walking disability, impaired proprioception and vision, and spasticity.

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 (e_BHS_mean) 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 vasoconstriction dependency, i.e. the rate of change of hypothalamus temperature weighted by the hypothalamus error signal (DThy·dThy/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 DThy·dThy/dt dependency in the vasoconstriction command, the e_BHS_mean for the exercise period is reduced by 59% (from 12.9 W·h to 5.2 W·h). Even though the new e_BHS_mean of 5.8 W·h for the total activity schedule is within the target accuracy of ±6.5 W·h, e_BHS 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.