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Contaminant transport at large Courant numbers using Markov matrices

机译：使用马尔可夫矩阵的大库仑数污染物传输

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摘要

Volatile organic compounds, particulate matter, airborne infectious disease, and harmful chemical or biological agents are examples of gaseous and particulate contaminants affecting human health in indoor environments. Fast and accurate methods are needed for detection, predictive transport, and contaminant source identification. Markov matrices have shown promise for these applications. However, current (Lagrangian and flux based) Markov methods are limited to small time steps and steady-flow fields. We extend the application of Markov matrices by developing a methodology based on Eulerian approaches. This allows construction of Markov matrices with time steps corresponding to very large Courant numbers. We generalize this framework for steady and transient flow fields with constant and time varying contaminant sources. We illustrate this methodology using three published flow fields. The Markov methods show excellent agreement with conventional PDE methods and are up to 100 times faster than the PDE methods. These methods show promise for developing real-time evacuation and containment strategies, demand response control and estimation of contaminant fields of potential harmful particulate or gaseous contaminants in the indoor environment. (c) 2016 Elsevier Ltd. All rights reserved.
机译：挥发性有机化合物，颗粒物，空气传播的传染病以及有害的化学或生物制剂是影响室内环境中人类健康的气态和颗粒污染物的例子。需要快速，准确的方法进行检测，预测性运输和污染源识别。马尔可夫矩阵对这些应用显示出了希望。但是，当前（基于拉格朗日和通量的）马尔可夫方法仅限于较小的时间步长和稳定流场。通过开发基于欧拉方法的方法，我们扩展了马尔可夫矩阵的应用。这允许构造具有对应于非常大的库兰特数的时间步长的马尔可夫矩阵。我们针对具有恒定和随时间变化的污染源的稳定和瞬态流场推广了该框架。我们使用三个已发布的流场说明了该方法。马尔可夫方法与常规PDE方法显示出极好的一致性，并且比PDE方法快100倍。这些方法显示出开发实时疏散和围堵策略，需求响应控制以及室内环境中潜在有害颗粒物或气态污染物的污染物场估计的希望。 （c）2016 Elsevier Ltd.保留所有权利。

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