Development and application of a clothed thermoregulatory model
Mathematical models of human thermoregulation can be used to assess the habitability of thermal environments prior to human exposure. Work continues to improve the performance of these models to reduce concerns surrounding the accuracy of their predictions. The aims of this thesis were to develop an existing thermoregulatory model (LUT25-node model). The developments made to the LUT25-node model, now enable it to predict the thermal responses of heat acclimated subjects of differing size, while its clothing model was improved to consider the addition and distribution of clothing properties. Validations of these modifications, confirmed that predictions from the model were improved. The thesis also looks at practical applications of the LUT25-node model. This included a modification to the model enabling backward modelling; predicting how the thermal stress should be altered to achieve a desired limit of thermal strain. Several hypothetical scenarios illustrated the practical applications of this modification. In addition, the LUT25-node model was used to explain the initial drop in deep body temperature at the onset of exercise. This investigation concluded that the temperature drop is due to the return of cool blood to the body core from initially cool working muscles. Finally, the poor predictions of the LUT25-node model for cold exposures was investigated. Previous investigators suspected that this was due to the limited number of thermal layers in the body segments of the model. However, predictions from a multi-layered LUT25-node model, developed with the finite volume software package PHOENICS, suggest that increasing the number of thermal layers reduces the accuracy of the model's predictions for cold exposures. In conclusion, this thesis has contributed to the continued development of a human thermoregulatory model and illustrated its practical benefits. It is recommended that future work centres on addressing additional limitations of the LUT25-node model identified in this study.