A Model Based on Markov Chain for Prompt Prediction of the Airborne Gaseous Pollutant Transport in Aircraft Cabins

Abstract

Prompt prediction of the airborne gaseous pollutant transport is important to design a safe and comfortable air environment in an aircraft cabin. This paper proposes a model based on Markov chain to fulfill the task, in which the gaseous pollutant can be released from a source with an arbitrary profile. The model first obtains the airflow field by CFD to construct a transport probability matrix of the gaseous pollutant, then predicts the concentration field at each time step when an impulse is released at the known source location using the transport probability matrix. Finally, detailed trace of the pollutant released from the source with an arbitrary profile can be reproduced by linear superposition. The above strategy is applied on a two-dimensional aircraft cabin with gaseous pollutant released from one passenger for 2s. Results show that the proposed model is able to correctly predict the gaseous pollutant transport in only a few minutes. More than 90% of the computing time can be saved comparing with that from CFD without sacrificing much accuracy.