A PDF micromixing model of dispersion for atmospheric flow. Part I: development of the model, application to homogeneous turbulence and to neutral boundary layer

Cassiani, Massimo; Franzese, Pasquale; Giostra, U.

doi:10.1016/j.atmosenv.2004.11.020

Cited by 84 publications

(123 citation statements)

References 44 publications

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“…The turbulent mixing timescale, T eddy , was found by setting it equal to mechanical turbulence timescale T vel = k/ǫ. This is valid for homogeneous turbulence with no scalar gradient and while it is not necessarily valid away from these conditions it has been found that the ratio stays close to unity in many other situations (Cassiani et al, 2005). This assumption has previously been used to accurately predict the variance of a passive tracer in a laboratory plume (Garmory et al, 2006).…”

Section: Methodsmentioning

confidence: 98%

Simulations of the dispersion of reactive pollutants in a street canyon, considering different chemical mechanisms and micromixing

Garmory

Kim

Britter

et al. 2009

Atmospheric Environment

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Section: Methodsmentioning

confidence: 98%

Simulations of the dispersion of reactive pollutants in a street canyon, considering different chemical mechanisms and micromixing

Garmory

Kim

Britter

et al. 2009

Atmospheric Environment

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“…In this context it is natural, therefore, to consider the sub-grid emission heterogeneity as represented by a PDF and to use these methods to convert it to an average concentration and all its statistical moments. Some applications of PDF methods to atmospheric dispersion and chemistry have recently appeared in the literature using both the Lagrangian particle approach (Cassiani et al, 2005a(Cassiani et al, , b, c, 2007aLuhar and Sawford, 2005;Sawford, 2006;Dixon and Tomlin, 2007;Bakosi et al, 2009) and the stochastic fields method approach (Garmory et al, 2006(Garmory et al, , 2008. However, all of these works focused on a single source or few sources treated in great detail to improve our understanding of the turbulent dispersion processes and turbulence chemistry interaction.…”

Section: The Approachmentioning

confidence: 99%

Stochastic fields method for sub-grid scale emission heterogeneity in mesoscale atmospheric dispersion models

et al. 2010

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Abstract. The stochastic fields method for turbulent reacting flows has been applied to the issue of sub-grid scale emission heterogeneity in a mesoscale model. This method is a solution technique for the probability density function (PDF) transport equation and can be seen as a straightforward extension of currently used mesoscale dispersion models. It has been implemented in an existing mesoscale model and the results are compared with Large-Eddy Simulation (LES) data devised to test specifically the effect of sub-grid scale emission heterogeneity on boundary layer concentration fluctuations. The sub-grid scale emission variability is assimilated in the model as a PDF of the emissions. The stochastic fields method shows excellent agreement with the LES data without adjustment of the constants used in the mesoscale model. The stochastic fields method is a stochastic solution of the transport equations for the concentration PDF of dispersing scalars, therefore it possesses the ability to handle chemistry of any complexity without the need to introduce additional closures for the high order statistics of chemical species. This study shows for the first time the feasibility of applying this method to mesoscale chemical transport models.

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“…The lower value of 3 and the proposed universality of c 0 suggested in Du (1997) were questioned by Reynolds (1998) and therefore it is of interest to explore the impact of uncertainties in this parameter. The possible range for describing the mixing timescale is based on Cassiani et al (2005) who reviewed the range of values used in the literature in Table 1 of their paper. The range for is estimated to represent the uncertainty in the effect of near field mixing on the initial source width.…”

Section: Model Validation and Uncertainties For Brown And Bilger Plumementioning

confidence: 99%

The effects of parametric uncertainties in simulations of a reactive plume using a Lagrangian stochastic model

Ziehn

Dixon

Tomlin

2009

Atmospheric Environment

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Abstract:A combined Lagrangian stochastic model with micro mixing and chemical sub-models is used to investigate a reactive plume of nitrogen oxides (NO x ) released into a turbulent grid flow doped with ozone (O 3 ). Sensitivities to the model input parameters are explored for high NO x model scenarios. A wind tunnel experiment is used to provide the simulation conditions for the first case study where photolysis reactions are not included and the main uncertainties occur in the parameters defining the turbulence scales, the source size and the reaction rate of NO (nitric oxide) with O 3 . Using nominal values of the parameters from previous studies, the model gives a good representation of the radial profile of the conserved scalar [NO x ] compared to the experiments, although the width of the simulated profile is slightly smaller, especially at longer distances from the source. For this scenario, the Lagrangian velocity structure function coefficient has the largest impact on simulated [NO x ] profiles. At the next stage photolysis reactions are included in a chemical scheme consisting of eight reactions between species NO, O, O 3 and NO 2 . The high dimensional model representation (HMDR) method is used to investigate the effects of uncertainties in the various model inputs resulting from the parameterisation of important physical and chemical processes in the reactive plume model, on the simulation of primary and secondary chemical species concentrations. Both independent and interactive effects of the parameters are studied. In total 22 parameters are assumed to be uncertain, among them the turbulence parameters, temperature dependant rate parameters, photolysis rates, temperature, fraction of NO in total NO x at the source and background concentration of O 3 . Only uncertainties in the mixing time scale coefficient and the structure function coefficient are responsible for the variance in the [NO x ] radial profile. On the other hand, the variance in the [O 3 ] profile is caused by parameters describing both physical and chemical processes.

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A PDF micromixing model of dispersion for atmospheric flow. Part I: development of the model, application to homogeneous turbulence and to neutral boundary layer

Cited by 84 publications

References 44 publications

Simulations of the dispersion of reactive pollutants in a street canyon, considering different chemical mechanisms and micromixing

Simulations of the dispersion of reactive pollutants in a street canyon, considering different chemical mechanisms and micromixing

Stochastic fields method for sub-grid scale emission heterogeneity in mesoscale atmospheric dispersion models

The effects of parametric uncertainties in simulations of a reactive plume using a Lagrangian stochastic model

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