New analytical formulations for calculation of dispersion parameters of Gaussian model using parallel CFD

Ebrahimi, Morteza; Jahangirian, A.

doi:10.1007/s10652-012-9260-5

Cited by 17 publications

(9 citation statements)

References 32 publications

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“…A first group of studies was aimed at estimating the most proper value for Sc t and at identifying the impact of Sc t on the numerical results [54,[58][59][60][61][62][63][64][65][66][67][68] (Table 3). Again, the terms "Exp" and "Num" mean the application of experimental and numerical methods, respectively, in each study.…”

Section: Atmosphere Systemsmentioning

confidence: 99%

“…Koeltzsch [66] Tracer transport in a boundary layer Exp − Sc t = 0.3-1, Sc t = Sc t (BL height) Flesch et al [63] Contaminant emission from soil Exp − Sc t = 0.6 Wilson [69] Concentration measurements above a wheat crop Exp − Sc t = 0.68 and 0.78 Tominaga and Stathopoulos [67] Review paper Exp/Num − Sc t = 0.2-1.3 Riddle et al [68] Pollutant dispersion in the built environment Exp/Num − Sc t = 0.3 and 0.7 Di Sabatino and Buccolieri [61] Pollutant dispersion in the built environment Exp/Num − Sc t = 0.4 and 0.7 Blocken et al [58] Pollutant dispersion in the built environment Exp/Num − Sc t = 0.3-1 Chavez et al [59] Pollutant dispersion in the built environment Exp/Num − Sc t = 0.1-0.7 Mokhtarzadeh-Dehghan et al [54] Pollutant dispersion in the built environment Exp/Num − Sc t = 0.4-2.5 as f(Ri*) Ebrahimi and Jahangirian [62] Pollutant dispersion in the built environment Exp/Num − Sc t = 0.7…”

Section: Environmental Flow Commentsmentioning

confidence: 99%

“…Ebrahimi and Jahangirian [62] presented a new analytical formulations for the calculation of the most effective parameters in the Gaussian plume dispersion model; for comparison, CFD simulations were carried out for single stack dispersion on a flat terrain surface. They used Sc t = 0.7.…”

Section: Environmental Flow Commentsmentioning

confidence: 99%

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On the Values for the Turbulent Schmidt Number in Environmental Flows

Gualtieri

Angeloudis

Bombardelli

et al. 2017

Fluids

172

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Computational Fluid Dynamics (CFD) has consolidated as a tool to provide understanding and quantitative information regarding many complex environmental flows. The accuracy and reliability of CFD modelling results oftentimes come under scrutiny because of issues in the implementation of and input data for those simulations. Regarding the input data, if an approach based on the Reynolds-Averaged Navier-Stokes (RANS) equations is applied, the turbulent scalar fluxes are generally estimated by assuming the standard gradient diffusion hypothesis (SGDH), which requires the definition of the turbulent Schmidt number, Sc t (the ratio of momentum diffusivity to mass diffusivity in the turbulent flow). However, no universally-accepted values of this parameter have been established or, more importantly, methodologies for its computation have been provided. This paper firstly presents a review of previous studies about Sc t in environmental flows, involving both water and air systems. Secondly, three case studies are presented where the key role of a correct parameterization of the turbulent Schmidt number is pointed out. These include: (1) transverse mixing in a shallow water flow; (2) tracer transport in a contact tank; and (3) sediment transport in suspension. An overall picture on the use of the Schmidt number in CFD emerges from the paper.

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Section: Atmosphere Systemsmentioning

confidence: 99%

Section: Environmental Flow Commentsmentioning

confidence: 99%

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On the Values for the Turbulent Schmidt Number in Environmental Flows

Gualtieri

Angeloudis

Bombardelli

et al. 2017

Fluids

172

View full text Add to dashboard Cite

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“…Based on comparisons between CFD and experimental data, Riddle et al (2004), Di Sabatino et al (2007) and Nakiboglu et al (2009) found a good agreement between measured and simulated concentrations for 0.4. Furthermore, Ebrahimi and Jahangirian (2013) found 0.7 to be the best value that led to the most accurate results using a RANS model. Recently, Gorlé et al (2010) and Longo et al (2019) proposed formulations for whose coefficients depend on turbulence characteristics and position.…”

Section: Introductionmentioning

confidence: 97%

Turbulent Schmidt Number Measurements Over Three-Dimensional Cubic Arrays

Bernardino

Monti

Leuzzi

et al. 2019

Boundary-Layer Meteorol

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We present turbulent Schmidt number ( ) estimations above threedimensional urban canopies, where is a property of the flow defined as the ratio of the eddy diffusivity of momentum ( ) to the eddy diffusivity of mass ( ). Despite the fact that modelling is of great interest, inter alia, for pollutant dispersion simulations conducted via computational fluid dynamics, no universal value is known. Simultaneous measurements of fluid velocity and mass concentration are carried out in a water channel for three staggered arrays of cubical obstacles corresponding to isolated flow, wake-interference, and skimming-flow regimes. A passive tracer is released from a continuous point source located at a height 1.67 , where H is the obstacle height. The results show an increase of with height above the canopy for all three arrays, with values at 2 ( 0.6) about double compared to that at . The observed agrees well with that modelled by using a simple formulation for based on expressions for and published in previous studies. Comparisons with other models found in the literature are also presented and discussed.

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“…This approach does not consider the effect of turbulence, particularly the turbulent scalar flux in lateral directions of the pathlines. Another approach based on the Gaussian plume model is frequently employed for estimating ground level concentrations from tall stack emissions [8,9]. An example application is the CRSTER model developed by the Environmental Protection Agency [10].…”

Section: Introductionmentioning

confidence: 99%

Estimation method to identify scalar point source in turbulent flow based on Taylor’s diffusion theory

2015

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We introduce a new approach to diffusion-source estimation for quick identification of the unknown source, based on Taylor's diffusion theory for turbulent transport of passive scalar from a fixed point source. In order to evaluate the method, we used planar laser-induced fluorescence to measure the concentration field of fluorescent dye in water flowing in a channel. We considered two kinds of datasets: basis data and observed data. The former is used to determine the basis functions characterizing the streamwise dependence of variances for three statistics: the mean concentration, root-mean-square (RMS) of fluctuations in the concentration, and RMS of the temporal gradient of the fluctuating concentration. Consistent with Taylor's theory, we found that the lateral distribution of each statistic was basically Gaussian, and their standard deviations increased as a function of the square root of the distance from the emitted point. Based on these facts, a basis function can be formulated and expected to be valid for estimation of unknown sources. Source estimation was performed with the observed data, which corresponded to limited available information about the concentration from an unknown point source. We confirmed a good prediction accuracy of the proposed method with an averaged bias as small as the turbulent integral scale. Better precision was achieved by employing several statistics simultaneously. In this case, the standard deviation of the estimated source position was assessed at 14 % of the mean distance between the source and measurement points, after 100 source-estimate trials with different datasets. The methodology tested in this paper is expected to be applicable more general and complex environmental diffusion issues involving anisotropic turbulent dispersion, and space-time variable mainstream systems; but its versatility in such systems is currently under investigation.

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New analytical formulations for calculation of dispersion parameters of Gaussian model using parallel CFD

Cited by 17 publications

References 32 publications

On the Values for the Turbulent Schmidt Number in Environmental Flows

On the Values for the Turbulent Schmidt Number in Environmental Flows

Turbulent Schmidt Number Measurements Over Three-Dimensional Cubic Arrays

Estimation method to identify scalar point source in turbulent flow based on Taylor’s diffusion theory

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