Predicting airborne particle deposition by a modified Markov chain model for fast estimation of potential contaminant spread. (July 2018)

• Record Type: Journal Article
• Authors: Mei, Xiong; Gong, Guangcai
• Is Part Of: Atmospheric environment. Volume 185(2018)
• Page Start: 137
• Page End: 146
• Publication Date: 2018-07
• Subjects: Airborne particle deposition -- Markov chain model -- Gravity-based adjustment -- Fast prediction -- CFD; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2018.04.050
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: As potential carriers of hazardous pollutants, airborne particles may deposit onto surfaces due to gravitational settling. A modified Markov chain model to predict gravity induced particle dispersion and deposition is proposed in the paper. The gravity force is considered as a dominant weighting factor to adjust the State Transfer Matrix, which represents the probabilities of the change of particle spatial distributions between consecutive time steps within an enclosure. The model performance has been further validated by particle deposition in a ventilation chamber and a horizontal turbulent duct flow in pre-existing literatures. Both the proportion of deposited particles and the dimensionless deposition velocity are adopted to characterize the validation results. Comparisons between our simulated results and the experimental data from literatures show reasonable accuracy. Moreover, it is also found that the dimensionless deposition velocity can be remarkably influenced by particle size and stream-wise velocity in a typical horizontal flow. This study indicates that the proposed model can predict the gravity-dominated airborne particle deposition with reasonable accuracy and acceptable computing time. Highlights: A modified Markov chain model is proposed to predict particle deposition. Gravity is considered as the dominant factor for particle deposition. The original State Transfer Matrix is adjusted to include gravitational setting. Impact of particle size and ambient flow velocity on model accuracy is analyzed.