Contaminant transport at large Courant numbers using Markov matrices

dc.contributor.author Passalacqua, Alberto
dc.contributor.author Vaidya, Umesh
dc.contributor.author Fontanini, Anthony
dc.contributor.author Vaidya, Umesh
dc.contributor.author Passalacqua, Alberto
dc.contributor.author Ganapathysubramanian, Baskar
dc.contributor.author Ganapathysubramanian, Baskar
dc.contributor.department Mechanical Engineering
dc.contributor.department Electrical and Computer Engineering
dc.date 2018-02-18T13:14:43.000
dc.date.accessioned 2020-06-30T06:03:56Z
dc.date.available 2020-06-30T06:03:56Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 2016
dc.date.embargo 2018-02-01
dc.date.issued 2017-02-01
dc.description.abstract <p>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.</p>
dc.description.comments <p>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." <em>Building and Environment</em> 112 (2017): 1-16. DOI:<a href="http://dx.doi.org/10.1016/j.buildenv.2016.11.007" target="_blank">10.1016/j.buildenv.2016.11.007</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/me_pubs/212/
dc.identifier.articleid 1213
dc.identifier.contextkey 10253511
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath me_pubs/212
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/55069
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/me_pubs/212/2017_Ganapathysubramanian_ContaminantTransport.pdf|||Fri Jan 14 22:35:34 UTC 2022
dc.source.uri 10.1016/j.buildenv.2016.11.007
dc.subject.disciplines Applied Mechanics
dc.subject.disciplines Electrical and Computer Engineering
dc.subject.disciplines Mechanical Engineering
dc.subject.keywords Markov matrix
dc.subject.keywords Contaminant transport
dc.subject.keywords Computational fluid dynamics (CFD)
dc.subject.keywords Advection diffusion
dc.subject.keywords Indoor air quality
dc.title Contaminant transport at large Courant numbers using Markov matrices
dc.type article
dc.type.genre article
dspace.entity.type Publication
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