Test Rig Design Considerations to Detect Volatile Organic Compounds in Aircraft Cabins

Abstract A numerical investigation for simulating the aircraft cabin as an environmental chamber is set to assist a test rig design assimilating passenger comfort, considering their exposure high concentrations of Volatile Organic Compounds. Computational Fluid Dynamics used evaluate flow inside 800 sec actual time, where mixing and transport chemical species are also evaluated. Measurements close passengers’ noses create Boruta feature selection-based dataset that trains four machine learning classifiers, namely, Support Vector Machine, Random Forest, Naive Bayes, Logistic Regression, compares performance. Furthermore, evaluation molecular weight impact on residence time explored, with additional simulation including filters. The model proven be insensitive inlet air mass variation, indicating air-conditioning system has minor measurements. Bayes classifier shows greatest performance 96 % accuracy being selected digital nose model. Moreover, when comparing results between models without filters, indicate independent each compound’s weight, all showing equivalent reduction. Finally, observed irregularities passengers may share different comfort experiences during flight. This dictates need manufacture full-scale quantify asymmetry frequent travelers aviation professionals.