A Stochastic Single-Particle Lagrangian Model for the Concentration Fluctuations in a Plume Dispersing Inside an Urban Canopy

Manor, Alon

doi:10.1007/s10546-013-9871-5

Cited by 21 publications

(31 citation statements)

References 20 publications

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“…Milliez and Carissimo, 2008;Yee, 2009). Lastly, recent studies have proposed to extend the use of single-particle Lagrangian models (Thomson, 1987) in heuristic ways, to account for the evolution of the second moment of the concentration fluctuations (Cassiani, 2013;Manor, 2014;Kaplan, 2014). This is the case of the volumetric particle approach (VPA) model that can be viewed as a simplification of a "traditional" Lagrangian PDF micromixing method (Cassiani, 2013).…”

Section: Cassiani (2013)mentioning

confidence: 99%

Dispersion of a Passive Scalar Fluctuating Plume in a Turbulent Boundary Layer. Part III: Stochastic Modelling

Marro

Salizzoni

Soulhac

et al. 2018

Boundary-Layer Meteorol

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We analyze the reliability of the Lagrangian stochastic micromixing method in predicting higher-order statistics of the passive scalar concentration induced by an elevated source (of varying diameter) placed in a turbulent boundary layer. To that purpose we analyze two different modelling approaches by testing their results against the wind-tunnel measurements discussed in Part I (Nironi et al., Boundary-Layer Meteorology, 2015, Vol.156, 415-446). The first is a probability density function (PDF) micromixing model that simulates the effects of the molecular diffusivity on the concentration fluctuations by taking into account the background particles. The second is a new model, named VPΓ , conceived in order to minimize the computational costs. This is based on the volumetric particle approach providing estimates of the first two concentration moments with no need for the simulation of the background particles. In this second approach, higher-order moments are computed based on the estimates of these two moments and under the assumption that the concentration PDF is a Gamma distribution. The comparisons concern the spatial distribution of the first four moments of the concentration and the evolution of the PDF along the plume centreline. The novelty of this work is twofold: i) we perform a systematic comparison of the results of micro-mixing Lagrangian models against experiments providing profiles of the first four moments of the concentration within an inhomogeneous and anisotropic turbulent flow, and ii) we show the reliability of the VPΓ model as an operational tool for the prediction of the PDF of the concentration.

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Section: Cassiani (2013)mentioning

confidence: 99%

Dispersion of a Passive Scalar Fluctuating Plume in a Turbulent Boundary Layer. Part III: Stochastic Modelling

Marro

Salizzoni

Soulhac

et al. 2018

Boundary-Layer Meteorol

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“…The spatial distribution of Q(x, t) essentially dictates a very large number of variance sources, which may pose a considerable computationally load. However, as verified by Manor (2014) [21], for a localized source, a small fraction (about 3%) of the potential sources are enough to produce almost all (97%) of the total effect. Thus, only these sources are identified and used.…”

Section: Virtual Variance Sourcesmentioning

confidence: 87%

“…The approach of virtual variance sources (VVS), suggested by Manor (2014) [21], is based on the steady state governing equation for the concentration variance…”

Section: Virtual Variance Sourcesmentioning

confidence: 99%

“…Thomson (1990) [7] and Borgas and Sawford (1994) [8] proposed the two-particle model, Pope (1985) and Cassiani et al (2005Cassiani et al ( , 2007, Leuzzi et al (2012) [8][9][10][11][12] the pdf method, while the fluctuating plume model was suggested by Yee et al (1994), Yee and Wilson (2000), Luhar et al (2000), Franzese (2003), Mortarini et al (2009), Ferrero et al (2013), Bisignano et al (2014), Marro et al(2015) [13][14][15][16][17][18][19][20]. Manor (2014) [21] used a Lagrangian particle model to simulate the concentration variance dispersion and Ferrero et al (2017) [22] suggested a formulation for the variance dissipation time scale to be used in the same model. This parameterization has been used by Oettl and Ferrero (2017) [6] and Ferrero and Oettl (2019) [23] to simulate odor dispersion in field campaigns.…”

Section: Introductionmentioning

confidence: 99%

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Concentration Fluctuations and Odor Dispersion in Lagrangian Models

et al. 2019

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In this paper, a review of the Lagrangian stochastic models developed in the last decades for the simulation of the concentration–fluctuation dispersion is presented. The main approaches available in the literature are described and their ability in reproducing the higher order moments of the probability density function is discussed. Then, the Lagrangian approaches for evaluating of the odor annoyance are presented. It is worth to notice that, while Lagrangian stochastic models for mean concentrations are well-known and their ability in correctly reproducing the observation is well assessed, concerning concentration fluctuations the approaches are often new and unknown for most of the scientific community.

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“…According to Irwin and coauthors [2], the dispersion of pollutants in the air has a deterministic part and a stochastic More sophisticated techniques such as Large Eddy Simulation (LES) [12], and one-particle Lagrangian models, such as developed by Thomson [15], Franzese [16], Mortarini et al [17] and Manor [18], should render better performance than AERMOD in complex situations, like reduced timescales and in the presence of obstacles.…”

Section: Introductionmentioning

confidence: 99%

An analysis of error propagation in AERMOD lateral dispersion using Round Hill II and Uttenweiller experiments in reduced averaging times

Hoinaski

Franco

Lisboa

2016

Environmental Technology

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Dispersion modelling was proved by researchers that most part of the models, including the regulatory models recommended by the Environmental Protection Agency of the United States (AERMOD and CALPUFF), do not have the ability to predict under complex situations. This article presents a novel evaluation of the propagation of errors in lateral dispersion coefficient of AERMOD with emphasis on estimate of average times under 10 min. The sources of uncertainty evaluated were parameterizations of lateral dispersion ([Formula: see text]), standard deviation of lateral wind speed ([Formula: see text]) and processing of obstacle effect. The model's performance was tested in two field tracer experiments: Round Hill II and Uttenweiller. The results show that error propagation from the estimate of [Formula: see text] directly affects the determination of [Formula: see text], especially in Round Hill II experiment conditions. After average times are reduced, errors arise in the parameterization of [Formula: see text], even after observation assimilations of [Formula: see text], exposing errors on Lagrangian Time Scale parameterization. The assessment of the model in the presence of obstacles shows that the implementation of a plume rise model enhancement algorithm can improve the performance of the AERMOD model. However, these improvements are small when the obstacles have a complex geometry, such as Uttenweiller.

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A Stochastic Single-Particle Lagrangian Model for the Concentration Fluctuations in a Plume Dispersing Inside an Urban Canopy

Cited by 21 publications

References 20 publications

Dispersion of a Passive Scalar Fluctuating Plume in a Turbulent Boundary Layer. Part III: Stochastic Modelling

Dispersion of a Passive Scalar Fluctuating Plume in a Turbulent Boundary Layer. Part III: Stochastic Modelling

Concentration Fluctuations and Odor Dispersion in Lagrangian Models

An analysis of error propagation in AERMOD lateral dispersion using Round Hill II and Uttenweiller experiments in reduced averaging times

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