Modeling transient particle transport by fast fluid dynamics with the Markov chain method
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Fast simulation tools for the prediction of transient particle transport are critical in designing the air distribution indoors to reduce the exposure to indoor particles and associated health risks. This investigation proposed a combined fast fluid dynamics (FFD) and Markov chain model for fast predicting transient particle transport indoors. The solver for FFD-Markov-chain model was programmed in OpenFOAM, an open-source CFD toolbox. This study used two cases from the literature to validate the developed model and found well agreement between the transient particle concentrations predicted by the FFD-Markov-chain model and the experimental data. This investigation further compared the FFD-Markov-chain model with the CFD-Eulerian model and CFD-Lagrangian model in terms of accuracy and efficiency. The accuracy of the FFD-Markov-chain model was similar to that of the other two models. For the two studied cases, the FFD-Markovchain model was 4.7 and 6.8 times faster, respectively, than the CFD-Eulerian model, and it was 137.4 and 53.3 times faster than the CFD-Lagrangian model in predicting the steady-state airflow and transient particle transport. Therefore, the FFD-Markov-chain model is able to greatly reduce the computing cost for predicting transient particle transport in indoor environments.
Keywordscomputational fluid dynamics indoor environment Eulerian model Lagrangian model particle dispersion aerosol dynamics
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This work was partially supported by the project #RNE-p1-18 of the Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, and the National Natural Science Foundation of China (No. 51808487).
- Bloch AB, Orenstein WA, Ewing WM, Spain WH, Mallison GF, Herrmann KL, Hinman AR (1985). Measles outbreak in a pediatric practice: Airborne transmission in an office setting. Pediatrics, 75: 676–683.Google Scholar
- Bolster DT, Linden PF (2009). Particle transport in low-energy ventilation systems. Part 2: Transients and experiments. Indoor Air, 19: 130–144.Google Scholar
- Boussinesq J (1903). Théorie analytique de la chaleur: mise en harmonie avec la thermodynamique et avec la théorie mécanique de la lumière. Vol. 2. Paris: Gauthier-Villars.Google Scholar
- Chen C, Zhao B, Yang X, Li X (2011c). Role of two-way airflow owing to temperature difference in severe acute respiratory syndrome transmission: Revisiting the largest nosocomial severe acute respiratory syndrome outbreak in Hong Kong. Journal of The Royal Society Interface, 8: 699–710.CrossRefGoogle Scholar
- Chen C, Lin C-H, Long Z, Chen Q (2014c). Predicting transient particle transport in enclosed environments with the combined computational fluid dynamics and Markov chain method, Indoor Air, 24: 81–92.Google Scholar
- Jasak H, Jemcov A, Tuković Ž (2007). OpenFOAM: A C++ library for complex physics simulations. In: Proceedings of the International Workshop on Coupled Methods in Numerical Dynamics, Dubrovnik, Croatia.Google Scholar
- Klepeis NE, Nelson WC, Ott WR, Robinson JP, Tsang AM, Switzer P, Behar JV, Hern SC, Engelmann WH (2001). The National Human Activity Pattern Survey (NHAPS): A resource for assessing exposure to environmental pollutants. Journal of Exposure Science and Environmental Epidemiology, 11: 231–252.CrossRefGoogle Scholar
- Li X, Niu J, Gao N (2012). Co-occupant’s exposure of expiratory droplets—Effects of mouth coverings. HVAC&R Research, 18: 575–587.Google Scholar