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    Comparative Evaluation of an Eulerian CFD and Gaussian Plume Models Based on Prairie Grass Dispersion Experiment

    Source: Journal of Applied Meteorology and Climatology:;2008:;volume( 047 ):;issue: 003::page 888
    Author:
    Demael, E.
    ,
    Carissimo, B.
    DOI: 10.1175/2007JAMC1375.1
    Publisher: American Meteorological Society
    Abstract: A theoretical and statistical comparison of a three-dimensional computational fluid dynamics (CFD) model with two Gaussian plume models is proposed on the Prairie Grass data field experiment for neutral conditions, using both maximum arcwise concentrations and spatially paired observations. In theory, it is impossible to have the same near-source behavior with the Eulerian CFD code as with the Gaussian plume models. The former presents the inability to account for the dependence of the turbulent diffusivity to the distance from the source, contrary to plume models for which this dependence is fitted to observations. The study described herein looks at the practical implications of these theoretical differences by comparing the two different types of models on a flat-terrain case, a situation favoring Gaussian models. The results herein show that the Eulerian CFD model gives acceptable results, both for arc-maximum concentrations and spatially paired observations. Indeed, the statistical performances are above the criteria of ?good performance? commonly defined in literature. In general, the results for Eulerian code fall between those of a Gaussian model that has been fitted using the Prairie Grass dataset and those of one fitted with different datasets.
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      Comparative Evaluation of an Eulerian CFD and Gaussian Plume Models Based on Prairie Grass Dispersion Experiment

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4206480
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    contributor authorDemael, E.
    contributor authorCarissimo, B.
    date accessioned2017-06-09T16:17:55Z
    date available2017-06-09T16:17:55Z
    date copyright2008/03/01
    date issued2008
    identifier issn1558-8424
    identifier otherams-65273.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4206480
    description abstractA theoretical and statistical comparison of a three-dimensional computational fluid dynamics (CFD) model with two Gaussian plume models is proposed on the Prairie Grass data field experiment for neutral conditions, using both maximum arcwise concentrations and spatially paired observations. In theory, it is impossible to have the same near-source behavior with the Eulerian CFD code as with the Gaussian plume models. The former presents the inability to account for the dependence of the turbulent diffusivity to the distance from the source, contrary to plume models for which this dependence is fitted to observations. The study described herein looks at the practical implications of these theoretical differences by comparing the two different types of models on a flat-terrain case, a situation favoring Gaussian models. The results herein show that the Eulerian CFD model gives acceptable results, both for arc-maximum concentrations and spatially paired observations. Indeed, the statistical performances are above the criteria of ?good performance? commonly defined in literature. In general, the results for Eulerian code fall between those of a Gaussian model that has been fitted using the Prairie Grass dataset and those of one fitted with different datasets.
    publisherAmerican Meteorological Society
    titleComparative Evaluation of an Eulerian CFD and Gaussian Plume Models Based on Prairie Grass Dispersion Experiment
    typeJournal Paper
    journal volume47
    journal issue3
    journal titleJournal of Applied Meteorology and Climatology
    identifier doi10.1175/2007JAMC1375.1
    journal fristpage888
    journal lastpage900
    treeJournal of Applied Meteorology and Climatology:;2008:;volume( 047 ):;issue: 003
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian