Built Environment & Energy Laboratory
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. (Particle dispersion, Infectious particle, Building)


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. (Particle deposition, Particulate matter, Aircraft cabin)


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%.  


Xia, T., Chen, C.* (2019). Differentiating between indoor exposure to PM2.5 of indoor and outdoor origin using time-resolved monitoring data. Building and Environment, 147, 528-539. (Particle penetration/emission, Particulate matter, Building)


To effectively control indoor PM2.5 (particulate matter with diameter less than 2.5 μm) in residential buildings, it is essential to differentiate between the contributions of outdoor PM2.5 infiltration and indoor PM2.5 emissions to the total indoor exposure. This study developed a method for automatically differentiating between indoor exposure to PM2.5 of indoor and outdoor origin using only the time-resolved indoor and outdoor PM2.5 concentrations and information about window opening/closing behavior. This investigation focused on naturally ventilated buildings without the use of portable air cleaners. The proposed approach combines change point analysis, a statistical method; the mass balance for PM2.5, a physical model; and window behavior characteristics to analyze the data and identify the indoor PM2.5 emissions. A series of experiments in a small-scale laboratory setup were conducted to validate the proposed method. The results show that the proposed method can automatically and successfully identify the indoor PM2.5 emissions for all of the 17 cases. Also, the proposed method accurately estimated the indoor exposure to PM2.5 of indoor and outdoor origin as a percentage of the total indoor exposure for all 17 cases with an average absolute error of 0.32%. 


2018 Particle