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

Abstract

Air travel is a contributor to carbon emissions and therefore climate change. For regional transportation, turboprop aircraft (i.e., those with a propeller) are more efficient than equivalent jets. Turboprops typically carry less than 100 passengers and include series from De Havilland (formerly Bombardier, Dash 8), Embraer EMB, and ATR42/72. Many future propulsion systems for ultra-low carbon aviation include propeller power units. A barrier to wider acceptance of turboprops has been the perception that they are uncomfortable due to the tonal nature of the noise and vibration. 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. Data was obtained from a study performed in an environmental chamber where human participants rated pairs of noise and vibration stimuli simulating the cabin of a turboprop. The temperature of the environment was also adjusted. Each individual modality was modeled using curve fitting; these modalities did not show significant cross-modal effects. The overall discomfort was modeled using multiple regression and applied a k-fold machine learning algorithm. Preferred modality for adjustment was modeled using a multiple regression approach and a logic function. The model requires inputs of noise level, vibration magnitude, and temperature, within the validated range. It outputs a prediction of subjective rating of noise, subjective rating of vibration, subjective rating of temperature, overall discomfort, and which modality should be altered to improve comfort. These predictions will allow aircraft designers to predict human response to the turboprop cabin environment and to prioritize areas for improvement.