Multi-factorial modeling of comfort in an aircraft cabin considering thermal, noise, and vibration metrics

Abstract

Comfort in aircraft cabins is influenced by many ergonomic and physical environment factors. For reasons of sustainability, the fleet of future regional passenger aircraft are expected to have an increased proportion that are propeller powered. Current turboprop regional aircraft have a reputation for being noisy and exposing passengers to vibration. Laboratory studies have simulated the aircraft cabin including noise, vibration and thermal stressors and sought subjective responses from volunteers. These data were used to build multi-factorial models of comfort in an aircraft cabin. Two modelling approaches were used: second order polynomial curve fitting allowed for prediction of subjective ratings from measurements of noise and vibration at discrete temperatures. A multi-factorial model including noise, vibration, and thermal parameters was developed using a linear regression machine-learning approach. This model allows for the prediction of subjective responses within a range of noise, vibration, and temperature levels that are experienced in aircraft. This paper presents the development of a model of the human response to noise, vibration and thermal stimuli. The model allows for the prediction of the response to noise, the response to vibration, the response to the thermal environment and the overall discomfort. It also predicts which of the modalities will be most important in terms of human response.