Predicting indoor deposited particle resuspension with a new probabilistic model based on Markov chain and turbulent burst

Abstract

Resuspension of deposited particles, which can be inhaled by human respiratory system, has been identified as a major airborne contaminant source. This study develops a new probabilistic model combing a modified Markov chain and turbulent burst theory to predict resuspension of mono-deposited particles from flat surfaces. First, a steady-state turbulent flow field is calculated by using CFD models, and detailed information of the flow field are exported out to establish a modified Markov chain model. Then the turbulent burst theory is implemented to acquire resuspension probabilities between specific numerical subspaces. Finally, the proposed model is initialized for particle phase simulation. Two rectangle duct flow cases, in which mono-deposited particle are preloaded on the floor surface, are adopted to validate the proposed model. Good agreements between simulated and experimental data indicate that the proposed model has solid capability to predict/simulate particle resuspension. The effects of particle diameter, flow velocity and turbulent burst characteristic α on particle resuspension are investigated. Results show that the accuracy of the proposed model is influenced by α, and α is recommended to be increased with the increase of flow velocity under relatively wide ranges of flow velocities. The resuspension rate becomes almost constant over time as particle size and flow velocity decrease. The computational cost of the proposed model varies with grid resolution and resuspension probability expression, which is still less than that of conventional Lagrangian/Eulerian models. The proposed model has the potential to be an alternative choice to help understanding and improving indoor air quality.

Copyright © 2021 American Association for Aerosol Research

EDITOR:

• Nicole Riemer

Acknowledgments

The investigation was supported by the National Science & Technology Supporting Program through grant No. 2015BAJ03B00, the National Natural Science Foundation of China through grant No. 51808555, the Natural Science Foundation of Hunan Province (Youth Program) through granted No. 2021JJ40591, the Doctoral Scientific Research Foundation of Changsha University of Science and Technology through granted No. 097/000301518 and the Scientific Research Project of Hunan Provincial Department of Education through granted No. 20C0033.