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Firefighting case study
Initially, the whole body model was validated by predicting the changes in Tc_N during cold water immersion and exercise conditions and was later applied for the firefighting scenarios. Cold water immersion and exercise at constant intensity are among the preferred scenarios for assessing the human body models . Both the ambient temperature and the overall heat trans-fer coefficient (h) values can be varied or kept constant. This allows the model to demonstrate its capability to evaluate the influence of variations in the environmental conditions on the body temperatures. The whole body model incorporates variations in metabolic heat generation rates, local blood perfusion rates, shivering, and sweating to compute the variations in Tc_N,…show more content…
Similar to cold water immersion and exercise scenarios, the higher tem-perature values were observed in the head region. The presence of the firefighting suit impeded the exchange of body heat to its immediate surroundings. This led to a somewhat uniform tem-perature field within the muscle and organ subdomains. Core Body Temperature (Tc). The comparison between the numerical core body tempera-ture (Tc_N_ps) based on perturbed w values (Eq 13) and the experimental core body temperature (Tc_E) showed a maximum difference of 0.3 °C for firefighter 1 (Sc3, Figure 15B1), 0.8 °C for firefighter 2 (R3o, Figure 17B), and 0.9 °C (Sc3, Figure 18B) for firefighter 3. When the fire-fighting gear conditions were varied to account for unknown changes in the field, the maximum variation in Tc_N_ps for firefighter 3 was within 0.4 °C of Tc_E (Figure 18C). Previous studies [5-7] have reported a difference of 0.2 °C – 0.6 °C in the calculation of Tc_N by using their respective models. These models, which utilized homogeneous heat flux conditions, simulated the human body experiments inside a closed and controlled environment. However, the present study dif-fers from the above as it was based on real-life firefighting training…show more content…
The evaluation of addi-tional datasets, improved input parameters, and better correlations for perfusion and sweating can further enhance the capability of the model to produce better results. The whole-body model evaluated in this study can be used to treat patients who have either hyperthermia or hypother-mia. The model can predict changes in Tc_N and rate of sweating during sporting activities like cycling, running, etc. This would help monitor the patient’s and/or athlete’s health. Additional applications for the whole body model include testing the performance and effectiveness of pro-tective apparels for soldiers, firefighters, and deep sea divers. This is expected to