Simulation of short-range diffusion experiment in low-wind convective conditions

Sharan, Maithili; Yadav, Anil Kumar; Modani, Manish

doi:10.1016/s1352-2310(02)00162-0

Cited by 19 publications

(8 citation statements)

References 14 publications

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“…Parametrização 2: [20], [21] e Sharan et al ( 2002) [22] propuseram as seguintes formulações para os coeficientes de difusão:…”

Section: Parametrizações Da Turbulênciaunclassified

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Simulação tridimensional da dispersão de poluentes considerando o termo de assimetria e diferentes coeficientes de difusão

Cardoso da Silveira,

Buske,

Sperotto de Quadros

et al. 2023

Sci. Plena

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O objetivo desse trabalho é avaliar a dispersão de poluentes na atmosfera, considerando a equação de advecção-difusão transiente tridimensional. O modelo, incluindo a difusão longitudinal e o fechamento não local da turbulência, é resolvido utilizando técnicas de transformadas integrais. Para a obtenção dos resultados e avaliação do modelo foram utilizados dados do experimento difusivo IIT Delhi para condições de vento fraco e duas parametrizações distintas para os coeficientes de difusão. O modelo simulou de forma satisfatória a concentração de poluentes quando comparado com os dados experimentais. O modelo apresentou melhor desempenho com os coeficientes de difusão que possuem maior informação da turbulência e considerado o termo de fechamento não local da turbulência.

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“…Parametrização 2: [20], [21] e Sharan et al ( 2002) [22] propuseram as seguintes formulações para os coeficientes de difusão:…”

Section: Parametrizações Da Turbulênciaunclassified

“…Para validar o modelo sob condições atmosféricas instáveis, foram utilizados dados de vento fraco reportados no experimento IIT Delhi (Índia) [20][21][22].…”

Section: Dados Experimentaisunclassified

Simulação tridimensional da dispersão de poluentes considerando o termo de assimetria e diferentes coeficientes de difusão

Cardoso da Silveira,

Buske,

Sperotto de Quadros

et al. 2023

Sci. Plena

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“…Sharan et al [1996aSharan et al [ , 1996bSharan et al [ , 2002 report measurements from a short-range diffusion experiment conducted in light winds at Indian Institute of Technology (IIT), Delhi (India), during February 1991. Field data.…”

Section: Surface Releasesmentioning

confidence: 99%

“…The values of u * , L, and h were taken from the work of Sharan et al [1996bSharan et al [ , 2002. The values of u * , L, and h were taken from the work of Sharan et al [1996bSharan et al [ , 2002.…”

Section: Surface Releasesmentioning

confidence: 99%

“…The wind speeds calculated using the diabatic wind profile at 4 and 15 m agl for the eight runs correlate very well with those observed (not shown), but are underestimated by about 15%. Sharan et al [2002] use the similarity relations σ u = 2.07u * , σ v = 2u * , and σ w = 1.3u * in their modeling based on sonic anemometer measurements at the IIT site. The model underestimates the data by more than a factor of two.…”

Section: Surface Releasesmentioning

confidence: 99%

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Lagrangian Particle Modeling of Dispersion in Light Winds

Luhar¹

2013

Lagrangian Modeling of the Atmosphere

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Typically, average wind speeds of 2 m s À1 or less are treated as light winds. Conventional modelling techniques, such as K-theory and Gaussian plume models, are inadequate at describing upwind diffusion and horizontal plume meander that occur in light winds. We apply the Lagrangian particle technique to model mean dispersion in light winds under both stable and unstable atmospheric stratifications. In stable conditions, we examine two formulations in the horizontal plane: one in which the Langrangian velocity autocorrelation is oscillatory (and which is known to lead to significant reduction of dispersion and spiralling particle trajectories) and, the other, a traditional one with an exponential autocorrelation. The former preforms slightly better for the same values of the Lagrangian time scales, turbulent kinetic energy (TKE) and its dissipation rate. Regardless of which formulation is used, the time scales and TKE must include the low-frequency meander in the horizontal. The use of conventional similarity parameterisation of horizontal turbulence leads to a very narrow plume with little upwind diffusion compared to the data. The Lagrangian modelling in unstable conditions indicates that the time elapsed since the start of the release is an important parameter, which implies that the assumption of steady state used in some models may pose problems. As winds become lighter, the maximum ground-level concentration decreases, but the ground area impacted by a given concentration is enlarged. Model application in a weakly convective surface layer shows that the neglect of turbulent velocity covariance leads to an overestimation of concentration by about 35%. LAGRANGIAN PARTICLE MODELING OF DISPERSION

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An Analytical Study for the Dispersion of Pollutants in a Finite Layer under Low Wind Conditions

Sharan¹,

Modani²

Pageoph Topical Volumes

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Simulation of short-range diffusion experiment in low-wind convective conditions

Cited by 19 publications

References 14 publications

Simulação tridimensional da dispersão de poluentes considerando o termo de assimetria e diferentes coeficientes de difusão

Simulação tridimensional da dispersão de poluentes considerando o termo de assimetria e diferentes coeficientes de difusão

Lagrangian Particle Modeling of Dispersion in Light Winds

An Analytical Study for the Dispersion of Pollutants in a Finite Layer under Low Wind Conditions

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