Let the dream set sail

**2019**

**Liu, W.*, You, R., Chen, C.* (2019). Modeling transient particle transport by fast fluid dynamics with the Markov chain method. Building Simulation, 12, 881–889.**

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-Markov-chain 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.

**Pan, Y., Lin, C.-H., Wei, D., Chen, C.* (2019). Experimental measurements and large eddy simulation of particle deposition distribution around a multi-slot diffuser. Building and Environment, 150, 156-163.**

Enhanced soiling around multi-slot air diffusers due to particle deposition is frequently observed in commercial airplanes. The dirty black soiling is very unsightly and influences the passengers’ perception of cabin air quality. This study conducted experimental measurements and large eddy simulations with Lagrangian tracking for the distribution of particle deposition around a multi-slot diffuser. This investigation first used a relatively simple case of indoor particle deposition to compare the LES-Lagrangian model with the RANS-Lagrangian model with near-wall turbulence kinetic energy correction. The comparison shows that the LES-Lagrangian model was more robust than the RANS-Lagrangian model in predicting particle deposition indoors. The superior LES-Lagrangian model was then applied in predicting the particle deposition distribution around a multi-slot diffuser. This investigation also conducted detailed measurements of the distribution of particle deposition around the multi-slot diffuser in a laboratory chamber using a wiping method on a resolution of 3 × 20 mm2. The measurement accuracy of the wiping method was within 20%. The particle deposition distribution predicted by the LES-Lagrangian model was compared with the experimental data to validate the model. The results indicated that the LES-Lagrangian model correctly predicted the order of magnitude of the particle deposition velocity distribution around the multi-slot diffuser with an average relative error of 63.2%.

**2018**

**Liu, C.*, Yang, J., Ji, S., Lu, Y., Wu, P., Chen, C.* (2018). Influence of natural ventilation rate on indoor PM2.5 deposition. Building and Environment, 144, 357-364.**

Particle deposition can significantly affect occupants’ exposure to PM2.5 in indoor environments. However, there is a lack of experimental data for the PM2.5 deposition rate under different ventilation rates in naturally ventilated rooms. This work experimentally examined the influence of natural ventilation rate on PM2.5 deposition in two university classrooms in Nanjing, China. The results showed that the deposition rate was linearly associated with the natural ventilation rate for both classrooms. An empirical equation was developed to correlate the PM2.5 deposition velocity with the natural ventilation rate. The developed empirical equation was then applied in three scenarios to illustrate the influence of natural-ventilation-rate-dependent PM2.5 deposition velocity on the infiltration factor, I/O ratio, and air cleaner selections. The analysis showed that the PM2.5 infiltration factor and I/O ratio were less sensitive to the natural ventilation rate when the influence of natural ventilation rate on deposition velocity was considered, compared with a constant deposition velocity. The required clean air delivery rate (CADR) for the air cleaner might be underestimated if the influence of natural ventilation rate on deposition velocity was not considered. Therefore, the dependence of PM2.5 deposition velocity on natural ventilation rate should be considered in PM2.5 infiltration factor calculation, I/O ratio prediction, and air cleaner selection.

**2016**

__Chen, C.__, Lin, C.-H., Wei, D., Chen, Q. (2016). Modeling particle deposition on the surfaces around a multi-slot diffuser. Building and Environment, 107, 79-89.

Enhanced soiling on the wall/ceiling around a diffuser due to particle deposition is very unsightly and reduces our quality of life. This study aimed to model the particle deposition on the surfaces around multi-slot diffusers, which are widely used in transportation vehicles. An SST k-ω model with a modified Lagrangian method was proposed and validated with experimental data on particle deposition rate from the literature. This investigation then conducted chamber tests to qualitatively validate model’s ability to predict the deposition distribution around a multi-slot diffuser. Using the validated model, this study numerically investigated the effects of slot setting, supply air angle, and temperature differential on particle deposition around a multi-slot diffuser. The results indicated that, with the same supply airflow rate, increasing the area ratio of openings to bars in a multi-slot diffuser can reduce the particle deposition. When the angle between the supply air jet and the wall was increased to more than 45o, the particle deposition was significantly reduced. Furthermore, the impact of thermophoresis on particle deposition around a multi-slot diffuser was negligible.

**2015**

__Chen, C.__, Liu, W., Lin, C.-H., Chen, Q. (2015). Comparing the Markov chain model with the Eulerian and Lagrangian models for indoor transient particle transport simulations. Aerosol Science and Technology, 49, 857-871.

Correctly predicting transient particle transport in indoor environments is crucial to improving the design of ventilation systems and reducing the risk of acquiring airborne infectious diseases. Recently, a new model was developed on the basis of Markov chain frame for quickly predicting transient particle transport indoors. To evaluate this Markov chain model, this study compared it with the traditional Eulerian and Lagrangian models in terms of performance, computing cost, and robustness. Four cases of particle transport, three of which included experimental data, were used for this comparison. The Markov chain model was able to predict transient particle transport indoors with similar accuracy to the Eulerian and Lagrangian models. Furthermore, when the same time step size (Courant number ≤ 1) and grid number were used for all three models, the Markov chain model had the highest calculation speed. The Eulerian model was faster than the Lagrangian model unless a super-fine grid was used. This investigation developed empirical equations for evaluating the three models in terms of computing cost. In addition, the Markov chain model was found to be sensitive to the time step size when the Courant number is larger than 1, whereas the Eulerian and Lagrangian models were not.

__Chen, C.__, Liu, W., Lin, C.-H., Chen, Q. (2015). A Markov chain model for predicting transient particle transport in enclosed environments. Building and Environment, 90, 30-36**.**

Obtaining information about particle dispersion in a room is crucial in reducing the risk of infectious disease transmission among occupants. This study developed a Markov chain model for quickly obtaining the information on the basis of a steady-state flow field calculated by computational fluid dynamics. When solving the particle transport equations, the Markov chain model does not require iterations in each time step, and thus it can significantly reduce the computing cost. This study used two sets of experimental data for transient particle transport to validate the model. In general, the trends in the particle concentration distributions predicted by the Markov chain model agreed reasonably well with the experimental data. This investigation also applied the model to the calculation of person-to-person particle transport in a ventilated room. The Markov chain model produced similar results to those of the Lagrangian and Eulerian models, while the speed of calculation increased by 8.0 and 6.3 times, respectively, in comparison to the latter two models.

__Chen, C.__, Liu, W., Lin, C.-H., Chen, Q. (2015). Accelerating the Lagrangian method for modeling transient particle transport in indoor environments. Aerosol Science and Technology, 49, 351-361**.**

Computational fluid dynamics (CFD) with the Lagrangian method has been widely used in predicting transient particle transport in indoor environments. The Lagrangian method calculates the trajectories of individual particles on the basis of Newton’s law. Statistically speaking, a large number of particles are needed in the calculations in order to ensure accuracy. Traditionally, modelers have conducted an independence test in order to find a reasonable value for this particle number. However, the unguided process of an independence test can be highly time-consuming when no simple method is available for estimating the necessary particle number. Therefore, this investigation developed a method for estimating the necessary particle number in the Lagrangian method. Furthermore, the computing cost of the Lagrangian method is positively associated with the particle number. If this number is too large, the computing cost may not be affordable. Thus, this study proposed the superimposition and time-averaging methods to reduce the necessary particle number. This investigation designed multiple cases to verify the proposed methods. The verification results show that the estimation method can provide the necessary particle number with a reasonable magnitude. Moreover, the superimposition method can reduce the necessary particle number when the particle source duration is relatively long. On the other hand, the time-averaging method can reduce the necessary particle number by up to 30 times. When compared with experimental data, predictions of transient particle transport in indoor environments by the combined Lagrangian, superimposition, and time-averaging method with the estimated particle number are reasonably accurate.

**2014**

__Chen, C.__, Lin, C.-H., Long, Z., Chen, Q. (2014). Predicting transient particle transport in enclosed environments with the combined computational fluid dynamics and Markov chain method. Indoor Air, 24, 81-92**.**

To quickly obtain information about airborne infectious disease transmission in enclosed environments is critical in reducing the infection risk to the occupants. This study developed a combined Computational Fluid Dynamics (CFD) and Markov chain method for quickly predicting transient particle transport in enclosed environments. The method first calculated a transition probability matrix using CFD simulations. Next, the Markov chain technique was applied to calculate the transient particle concentration distributions. This investigation used three cases, particle transport in an isothermal clean room, an office with an Under-Floor Air-Distribution (UFAD) system, and the first-class cabin of an MD-82 airliner, to validate the combined CFD and Markov chain method. The general trends of the particle concentrations versus time predicted by the Markov chain method agreed with the CFD simulations for these cases. The proposed Markov chain method can provide faster-than-real-time information about particle transport in enclosed environments. Furthermore, for a fixed airflow field, when the source location is changed, the Markov chain method can be used to avoid recalculation of the particle transport equation and thus reduce computing costs.

**2013**

__Chen, C.__, Liu, W., Li, F., Lin, C.-H., Liu, J., Pei, J., Chen, Q. (2013). A hybrid model for investigating transient particle transport in enclosed environments. Building and Environment, 62, 45-54**.**

It is important to accurately model person-to-person particle transport in mechanical ventilation spaces to create and maintain a healthy indoor environment. The present study introduces a hybrid DES-Lagrangian and RANS-Eulerian model for simulating transient particle transport in enclosed environments; this hybrid model can ensure the accuracy and reduce the computing cost. Our study estimated two key time constants for the model that are important parameters for reducing the computing costs. The two time constants estimated were verified by airflow data from both an office and an aircraft cabin case. This study also conducted experiments in the first-class cabin of an MD-82 commercial airliner with heated manikins to validate the hybrid model. A pulse particle source was applied at the mouth of an index manikin to simulate a cough. The particle concentrations versus time were measured at the breathing zone of the other manikins. The trend of particle concentrations versus time predicted by the hybrid model agrees with the experimental data. Therefore, the proposed hybrid model can be used for investigating transient particle transport in enclosed environments.

**2012**

__Chen, C.__, Zhao, B., Zhou, W., Jiang, X., Tan, Z. (2012). A methodology for predicting particle penetration factor through cracks of windows and doors for actual engineering application. Building and Environment, 47, 339-348**.**

Epidemiologic evidences have shown a strong relationship between exposure to outdoor particles and adverse impact on human health. A large amount of outdoor particles may penetrate into the indoor environments, where people spend about 90% of their life time. Therefore, predicting particle penetration into buildings could help to quantify the indoor exposure to particles with an outdoor origin and thus to develop effective strategies to remediate the exposure. However, there are few methodologies of particle penetration prediction for actual engineering application. In this paper, we present a methodology for predicting the particle penetration factor for real buildings by estimating the geometries of the cracks in the building envelopes according to the American Society of Heating, Refrigeration, Air-Conditioning Engineers (ASHRAE) Handbook. In addition, the effect of inertial impaction was considered. Furthermore, the effect of gravitational settling was neglected for vertical leakages. Two field measurements showed that the proposed methodology was effective in predicting the particle penetration factor for the test rooms. For particles in the range of 0.5 to 6 μm in diameter, the experimental data of penetration factors vary from 0.2 to 1, which match well with the predicted data. Generally speaking, this methodology can be used to aid the engineers or designers to calculate the particle penetration in actual engineering practices or designs. In addition, a sensitivity analysis was conducted to investigate the influencing factors.

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