A case study on human bio-heat transfer and thermal comfort within CFD

•STB equation was solved in parallel with Navier–Stokes equation in a CFD tool.•Validation was done against experimental and simulated data of past research.•Better insight towards the thermal field in the human body was obtained.•Thermoregulatory activities were parameterized against the environmental variables.•Thermal comfort was assessed using indices, such as PMV and PPD.

Thermal comfort assessment is an imperative aspect of vehicle cabin climate control. Although, in the past few decades, Fanger's thermal comfort model has been extensively employed to evaluate the perceived thermal comfort, its constitutional incompatibility for transient problems due to its inability to account thermoregulatory activities has caused automobile engineers to explore more comprehensive thermal comfort models. Current paper demonstrates how the Simplified Thermoregulatory Bio-heat (STB) equation—with its ability to account thermoregulatory activities viz. vasomotion, sweating and shivering etc.—can be used effectively to gain a better insight in a comprehensive and transient thermal comfort evaluation.The STB equation, for this study, was solved in parallel with the Navier–Stokes (N–S) equation in a CFD tool. The results for a standalone model were initially validated against various experimental and simulated data obtained by past research in human body bio-heat transfer. The study further demonstrates various case studies to gain a better insight towards the thermal field in the human body. A multi-segmented (17 segments, each consisting of four layers viz. skin, fat, muscle and bone) manikin was considered for this simulation to study the perceived thermal comfort assessment over the human body. Thermoregulatory activities as a result of the exposed environmental conditions were parameterized against the environmental variables. Furthermore, the evaluation of the perceived thermal comfort for this study was done with the thermal comfort indices such as Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfaction (PPD).