An efficient algorithm for scalar PDF modelling in incompressible turbulent flow; numerical analysis with evaluation of IEM and IECM micro-mixing models

Cassiani, Massimo; Radicchi, A.; Albertson, J. D.; Giostra, U.

doi:10.1016/j.jcp.2006.09.023

Cited by 29 publications

(33 citation statements)

References 37 publications

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“…In general, consistency between the Eulerian and Lagrangian density fields is important if chemistry and mixing are included in a Lagrangian framework and it is fundamental if dynamically active scalars are involved in hybrid Eulerian-Lagrangian simulations (e.g. Muradoglu et al, 2001;Heinz, 2003;Cassiani et al, 2007;Popov and Pope, 2014;Grewe et al, 2014). We note that, for applications that do not require continuous particle trajectories, particles re-initialisation procedures may be included to re-establish consistency between the Eulerian and Lagrangian density fields.…”

Section: Flexpart-noresm/cam Vs Flexpart-ecmwf and The Vertical Wellmentioning

confidence: 99%

The offline Lagrangian particle model FLEXPART–NorESM/CAM (v1): model description and comparisons with the online NorESM transport scheme and with the reference FLEXPART model

et al. 2016

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Abstract. The offline FLEXible PARTicle (FLEXPART) stochastic dispersion model is currently a community model used by many scientists. Here, an alternative FLEXPART model version has been developed and tailored to use with the meteorological output data generated by the CMIP5-version of the Norwegian Earth System Model (NorESM1-M). The atmospheric component of the NorESM1-M is based on the Community Atmosphere Model (CAM4); hence, this FLEXPART version could be widely applicable and it provides a new advanced tool to directly analyse and diagnose atmospheric transport properties of the state-of-theart climate model NorESM in a reliable way. The adaptation of FLEXPART to NorESM required new routines to read meteorological fields, new post-processing routines to obtain the vertical velocity in the FLEXPART coordinate system, and other changes. These are described in detail in this paper. To validate the model, several tests were performed that offered the possibility to investigate some aspects of offline global dispersion modelling. First, a comprehensive comparison was made between the tracer transport from several point sources around the globe calculated online by the transport scheme embedded in CAM4 and the FLEXPART model applied offline on output data. The comparison allowed investigating several aspects of the transport schemes including the approximation introduced by using an offline dispersion model with the need to transform the vertical coordinate system, the influence on the model results of the sub-grid-scale parameterisations of convection and boundary layer height and the possible advantage entailed in using a numerically non-diffusive Lagrangian particle solver. Subsequently, a comparison between the reference FLEX-PART model and the FLEXPART-NorESM/CAM version was performed to compare the well-mixed state of the atmosphere in a 1-year global simulation. The two model versions use different methods to obtain the vertical velocity but no significant difference in the results was found. However, for both model versions there was some degradation in the well-mixed state after 1 year of simulation with the buildup of a bias and an increased scatter. Finally, the capability of the new combined modelling system in producing realistic, backward-in-time transport statistics was evaluated calculating the average footprint over a 5-year period for several measurement locations and by comparing the results with those obtained with the reference FLEXPART model driven by re-analysis fields. This comparison confirmed the effectiveness of the combined modelling system FLEXPART with NorESM in producing realistic transport statistics.

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Section: Flexpart-noresm/cam Vs Flexpart-ecmwf and The Vertical Wellmentioning

confidence: 99%

The offline Lagrangian particle model FLEXPART–NorESM/CAM (v1): model description and comparisons with the online NorESM transport scheme and with the reference FLEXPART model

et al. 2016

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“…3-5. Full details of the algorithm, including the analysis of the convergence to a significant solution and the analysis of the statistical and discretization errors, can be found in Cassiani et al (2006). We recall that a significant ensemble of solutions of Eqs.…”

Section: Numerical Algorithmmentioning

confidence: 99%

“…The velocity space has been discretized with nine velocity classes for each velocity component, where each class covers approximately the same area under the velocity pdf. Extensive tests have been conducted to evaluate the effect of the number of velocity classes on the simulation results and a full explanation is given in Cassiani et al (2006).…”

Section: The Gridmentioning

confidence: 99%

“…We point out that the SE procedure is executed at the time of crossing the border of adjacent blocks; the influence of the SE procedure on the model results has been investigated in Cassiani et al (2006). It is worth noting that with this structured approach we avoid any computationally expensive a posteriori treatments of the particle mass.…”

Section: Particle Splitting-erasingmentioning

confidence: 99%

See 1 more Smart Citation

Modelling of concentration fluctuations in canopy turbulence

Cassiani

Radicchi

Albertson

2006

Boundary-Layer Meteorol

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The knowledge of the concentration probability density function (pdf) is of importance in a number of practical applications, and a Lagrangian stochastic (LS) pdf model has been developed to predict statistics and concentration pdf generated by continuous releases of non-reactive and reactive substances in canopy generated turbulence. Turbulent dispersion is modelled using a LS model including the effects of wind shear and along-wind turbulence. The dissipation of concentration fluctuations associated with turbulence and molecular diffusivity is simulated by an Interaction by Exchange with the Conditional Mean (IECM) micromixing model. A general procedure to obtain the micromixing time scale needed in the IECM model useful in non-homogeneous conditions and for single and multiple scalar sources has been developed. An efficient algorithm based on a nested grid approach with particle splitting, merging techniques and time averaging has been used, thus allowing the calculation for cases of practical interest. The model has been tested against wind-tunnel experiments of single line and multiple line releases in a canopy layer. The approach accounted for chemical reactions in a straightforward manner with no closure assumptions, but here the validation is limited to non-reacting scalars.

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“…A full set of modeling methods, known as PDF methods for turbulent reacting flows (e.g. Pope 1985Pope , 2000Dopazo et al, 1997;Valiño, 1998;Fox, 2003;Heinz, 2003;Cassiani et al, 2005aCassiani et al, , 2007a, exists that provides a way to compute the evolution of the concentration PDF and all its moments within a turbulent flow. 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.…”

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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An efficient algorithm for scalar PDF modelling in incompressible turbulent flow; numerical analysis with evaluation of IEM and IECM micro-mixing models

Cited by 29 publications

References 37 publications

The offline Lagrangian particle model FLEXPART–NorESM/CAM (v1): model description and comparisons with the online NorESM transport scheme and with the reference FLEXPART model

The offline Lagrangian particle model FLEXPART–NorESM/CAM (v1): model description and comparisons with the online NorESM transport scheme and with the reference FLEXPART model

Modelling of concentration fluctuations in canopy turbulence

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

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