Adaptive processes explain variations in human thermal sensation

Abstract

Models for human perception of thermal environments included in so-called thermal comfort standards are either based on principles of thermal heat balance, or on large empirical datasets that include human adaptations to different thermal environments (i.e. so-called adaptive approach). The framework for an adaptive thermal heat balance model (ATHB) combines these 2 approaches, improves the predictive performance and offers further potentials to explain variations in human thermal sensation as discussed below. At first, due to different foundations of both models it may seem illogical to combine the heat balance approach with the adaptive approach. One is based on a steady-state heat balance of the human body taking into account the indoor environmental parameters air temperature, mean radiant temperature, air velocity, and air humidity as well as the clothing level and metabolic rate of a person. The other established a theoretical framework including behavioral, physiological, and psychological adaptive processes and considers averaged floating outdoor conditions. The combination of the 2 approaches as described by the ATHB is realized by setting up simple exemplary equations for each of the 3 adaptive processes individually. These equations adapt the values for the clothing level and the metabolic rate used as input for the heat balance model equations. The equation related to behavioral adaptation is a linear function with the running mean outdoor temperature as independent and the clothing level as dependent variable. With increasing outdoor temperatures, people are wearing lighter clothing ensembles. Maximum and minimum clothing insulation values are specified. Related to physiological adaptation, a linear equation modifies the metabolic rate based on the running mean outdoor temperature. With increasing outdoor temperatures, metabolic rate decreases as we assumed that people’s thermo-regulative system adapts to warm conditions and gets more efficient. Psychological adaptive processes were assumed to alter metabolic rate, too. This can happen on the one hand in a variable form depending on an environmental stimulus, e.g. with higher indoor temperatures, perceived control was found to decrease, which let the metabolic rate increase. On the other hand, this can be a fixed offset in metabolic rate depending on the type of environment, e.g., a higher number of people in the same room increased metabolic rate due to psychological stress while a higher number of control opportunities decreased metabolic rate. Using data from experimental studies in our LOBSTER facility, a realistic office environment with a controllable thermal indoor environment and possibilities for subjects to interact with the outdoor environment through operable windows (Fig. 1A), we derived the corresponding coefficients for these equations through mixed effect regression analyses. Thereby, the magnitude of increase and decrease of the metabolic rate was inferred from measurements of the heart rate and corresponding regression analyses. Differences in the metabolic rate or the degree of its adaptation between individuals or groups of individuals were neglected so far. However, the results of such analysis could be incorporated in future advancements of the approach as discussed below. Through the application of the framework to Fanger’s PMVmodel, it was possible to draw the relationship between operative temperatures perceived as neutral and the running mean outdoor temperature. Including all 3 adaptive