Derivation and validation of a whole-body dynamic mean thermal sensation model

Abstract

A new model predicting the whole-body Dynamic Mean thermal sensation Vote (DMV) is described. The model is useful for evaluating transient thermal conditions but is limited to uniform ones. It is based on physiological signals (mean skin temperature and its rate of change, mean skin wittedness, and core body temperature) simulated by using Gagge's two-node thermophysiological model. It is derived from empirical data obtained through experiments conducted under 160 steady-state thermal exposures at rest, 60 transient thermal conditions at rest, and 24 static thermal conditions during exercise. An independent validation is performed against 13 transient thermal conditions during exercise. The model shows good agreement (RMSE less than 0.5) with experimental observations within the range of air temperatures between 15 and 37 °C and when activity levels are below 3 met. It performs better than the widely used Fanger's PMV model, especially when far from thermal neutrality, for step-change thermal transients, and under exercise conditions. Furthermore, the model's simplicity and low computational cost are important advantages over more complex and computationally expensive thermal sensation models based on multi-segment and multi-node thermophysiological models.