Rapid prediction of airborne gaseous pollutant transport in aircraft cabins based on proper orthogonal decomposition and the Markov chain method

Rapid prediction of airborne gaseous pollutant transport is important for designing a safe indoor environment. Current models generally solve the airflow fields by CFD first and then predict the transport of a pollutant in fixed airflow patterns. Every time the air-supply parameters are adjusted, the airflow field must be re-solved by CFD, which is time-consuming. This study proposed a model to improve the prediction efficiency. The model first applies proper orthogonal decomposition to the sampled airflow fields, to construct a database related to all the airflow fields in the sample ranges, and then uses the Markov chain method to obtain the airflow field with the desired air-supply parameters for construction of a transport probability matrix. Finally, the airborne gaseous pollutant transport can be predicted quickly in the fixed airflow pattern. The proposed model was applied to an aircraft cabin model, first with a single gaseous pollutant source and then with two sources, for validation of the proposed model. The results show that the proposed model can predict both the airflow field and the transport of a gaseous pollutant with outcomes similar to those obtained by the conventional CFD method, but with a much shorter computing time. When the database has been prepared in advance, the use of the model reduces the computing time by more than 90%. Further improvement of the proposed model in terms of accuracy and extension of the model to prediction of pollutant transport within unsteady airflow fields will be the main objectives of future work.