Weighting of climate parameters for the prediction of thermal comfort in an aircraft passenger cabin. (January 2015) - Maier, Julia; Marggraf-Micheel, Claudia
Abstract: In many approaches to the analysis and description of passenger thermal comfort, temperature is the most important determinant. This is rather a restrictive view, as there are other climate parameters like humidity and air movement that influence the passengers' comfort as well. In the present study, objective and subjective data were used to predict the comfort evaluation of air temperature, humidity and air velocity. The aim was to analyse the relative weights of the climate parameters for the prediction of overall thermal comfort. In three studies empirical data were gathered from 169 subjects. An aircraft mock-up of a Dornier 728 was used as test facility, where different climate scenarios were realised by varying the mean cabin temperature (21.5 °C–26 °C, mean air velocity approx. 0.15, humidity max. 30%). Climate parameters were measured via appropriate sensors and rated regarding their intensity and comfort by the subjects via questionnaires. Statistical models were developed that describe the non-linear and linear relationships between the three climate parameters and their respective comfort ratings. It was found that subjective ratings explained more variance than objective measurements. Regression analyses indicated that air temperature had the largest weight for comfort predictions, but humidity and air draught also had significant effects and should not be neglected. The present results demonstrate that thermo-comfort-models which include subjective data and account for different climate parameters and their interrelations should be considered in the development of aircraft cabin interiors. Highlights: Statistical models are developed for the prediction of thermal comfort. Subjects' ratings have a greater explanatory power than objective measurements. In addition to temperature, air velocity and humidity are vital for thermal comfort. A holistic thermal comfort model is needed to arrive at precise comfort predictions.