Zhengwei Long

Predicting transient particle transport in enclosed environments with the combined computational fluid dynamics and Markov chain method

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 underfloor air distribution 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 vs. 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.

A procedure for predicting pressure loss coefficients of duct fittings using computational fluid dynamics (RP-1493)

Knowing loss coefficients of duct fittings is crucial for designing a duct network for HVAC systems. Traditionally, coefficients have been obtained using experimental measurements according to AHSRAE Standard 120, a process that is time consuming and expensive. An alternative is to use computational fluid dynamics, but this has uncertainty due to the approximations used in modeling turbulence. This study first validated three turbulence models by comparing the predicted loss coefficient of an elbow with the corresponding experimental data from the literature. The standard k-ϵ model and Reynolds stress model could accurately predict the loss coefficient; however, the more advanced large Eddy simulation model failed. The study found that the surface roughness of the straight duct connected to the elbow had a significant influence on the predicted pressure loss and that the accurate surface roughness could be determined. Then, this study applied the same procedure and turbulence models to predict the pressure loss coefficient of a lateral and a tee junction with both converging and diverging flows. The results show again that the surface roughness of the straight duct connected to the junctions was very important and that the best value could be estimated. The pressure loss coefficients predicted were accurate when compared with the experimental data available after the simulations. Some discrepancies between the calculated and measured results exist that could be attributed to the approximations used in the simulations or the errors in the experimental measurements.