School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA.
Indoor Air. 2014 Feb;24(1):81-92. doi: 10.1111/ina.12056. Epub 2013 Jul 20.
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.
快速获取密闭环境中空气传播传染病的信息对于降低居住者的感染风险至关重要。本研究开发了一种组合计算流体动力学（CFD）和马尔可夫链方法，用于快速预测密闭环境中的瞬态粒子输运。该方法首先使用 CFD 模拟计算转移概率矩阵。然后，应用马尔可夫链技术计算瞬态粒子浓度分布。本研究使用了三个案例，即等温洁净室中的粒子输运、地板送风系统的办公室以及 MD-82 客机的头等舱，来验证组合 CFD 和马尔可夫链方法。对于这些情况，马尔可夫链方法预测的粒子浓度随时间的变化趋势与 CFD 模拟结果一致。所提出的马尔可夫链方法可以提供关于密闭环境中粒子输运的快于实时的信息。此外，对于固定的气流场，当源位置发生变化时，可以使用马尔可夫链方法避免重新计算粒子输运方程，从而降低计算成本。
