Fontanini Anthony D, Vaidya Umesh, Ganapathysubramanian Baskar
Department of Mechanical Engineering, 2100 Black Engineering, Iowa State University, Ames, IA 50010, USA.
Department of Electrical and Computer Engineering, 2215 Coover, Iowa State University, Ames, IA 50010, USA.
Build Environ. 2015 Dec;94:68-81. doi: 10.1016/j.buildenv.2015.07.020. Epub 2015 Jul 22.
Predicting the movement of contaminants in the indoor environment has applications in tracking airborne infectious disease, ventilation of gaseous contaminants, and the isolation of spaces during biological attacks. Markov matrices provide a convenient way to perform contaminant transport analysis. However, no standardized method exists for calculating these matrices. A methodology based on set theory is developed for calculating contaminant transport in real-time utilizing Markov matrices from CFD flow data (or discrete flow field data). The methodology provides a rigorous yet simple strategy for determining the number and size of the Markov states, the time step associated with the Markov matrix, and calculation of individual entries of the Markov matrix. The procedure is benchmarked against scalar transport of validated airflow fields in enclosed and ventilated spaces. The approach can be applied to any general airflow field, and is shown to calculate contaminant transport over 3000 times faster than solving the corresponding scalar transport partial differential equation. This near real-time methodology allows for the development of more robust sensing and control procedures of critical care environments (clean rooms and hospital wards), small enclosed spaces (like airplane cabins) and high traffic public areas (train stations and airports).
预测室内环境中污染物的移动在追踪空气传播传染病、气态污染物通风以及生物攻击期间的空间隔离方面具有应用价值。马尔可夫矩阵为进行污染物传输分析提供了一种便捷的方法。然而，目前尚无计算这些矩阵的标准化方法。本文开发了一种基于集合论的方法，用于利用计算流体动力学（CFD）流数据（或离散流场数据）中的马尔可夫矩阵实时计算污染物传输。该方法为确定马尔可夫状态的数量和大小、与马尔可夫矩阵相关的时间步长以及马尔可夫矩阵各个条目的计算提供了一种严谨而简单的策略。该程序以封闭和通风空间中经过验证的气流场的标量传输为基准进行测试。该方法可应用于任何一般气流场，并且经证明其计算污染物传输的速度比求解相应的标量传输偏微分方程快3000多倍。这种近实时方法有助于开发针对重症监护环境（洁净室和医院病房）、小型封闭空间（如飞机客舱）和高流量公共区域（火车站和机场）的更强大的传感和控制程序。
