Campus Units

Electrical and Computer Engineering, Mechanical Engineering

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

2-1-2017

Journal or Book Title

Building and Environment

Volume

112

First Page

1

Last Page

16

DOI

10.1016/j.buildenv.2016.11.007

Abstract

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.

Comments

This is a manuscript of an article published as Fontanini, Anthony D., Umesh Vaidya, Alberto Passalacqua, and Baskar Ganapathysubramanian. "Contaminant transport at large Courant numbers using Markov matrices." Building and Environment 112 (2017): 1-16. DOI:10.1016/j.buildenv.2016.11.007. Posted with permission.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

Elsevier Ltd.

Language

en

File Format

application/pdf

Available for download on Thursday, February 01, 2018

Published Version

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