Employing Taylor and Heisenberg subfilter viscosities to simulate turbulent statistics in LES models

Degrazia, Gervásio Annes; Rizza, Umberto; Puhales, Franciano Scremin; Welter, Guilherme Sausen; Acevedo, Otávio C.; Maldaner, Silvana

doi:10.1016/j.physa.2011.09.015

Cited by 5 publications

(9 citation statements)

References 37 publications

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“…6d) becomes more important. These patterns are in agreement with those found in some experiments where these quantities have been sampled across the entire convective boundary layer (CBL; Caughey and Palmer 1979;Caughey 1984) as well as with results from large-eddy simulations (Mason 1989;Degrazia et al 2012). The variances of the turbulent wind components are key parameters in air dispersion models, but they are very rarely observed at levels higher than a few meters above the surface.…”

Section: The Effect Of the Power Plant On The Local Atmospheric Boundsupporting

confidence: 89%

Monitoring the Micrometeorology of a Coastal Site next to a Thermal Power Plant from the Surface to 140 m

Acevedo

Degrazia

Puhales

et al. 2018

Bulletin of the American Meteorological Society

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Section: The Effect Of the Power Plant On The Local Atmospheric Boundsupporting

confidence: 89%

Monitoring the Micrometeorology of a Coastal Site next to a Thermal Power Plant from the Surface to 140 m

Acevedo

Degrazia

Puhales

et al. 2018

Bulletin of the American Meteorological Society

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“…The values for σ u 2 and σ v 2 calculated from LES spectral data are in agreement with the horizontal diffusion data in a water-tank experiment [16,17]. Furthermore, the differences between the numerical constants in Eqs.…”

Section: Derivation Of Velocity Variances Obtained Of Les and Observasupporting

confidence: 81%

“…(2) presents the vertical profile of the local time scales obtained from Eqs. (17) and (18). It can be seen that both formulations present an identical form.…”

Section: Derivation Of Turbulent Time Scales Obtained Of Les and Obsementioning

confidence: 83%

“…(12,14,15,16,17,18,19,20) to simulate the ground-level cross wind integrated concentrations of contaminants released from a low continuous source in a CBL. Lagrangian Stochastic particle models are numerical tools for the study of the atmospheric turbulent diffusion process.…”

Section: Employment Of Time Scales and Velocity Variances Obtained Ofmentioning

confidence: 99%

“…LAMBDA model, employing the Eqs. (12,15,17,19) derived from LES spectral data, shows a good performance, with correlation coefficient ( R ) near to 1 and normalized mean square error (NMSE), fractional standard deviation (FS) and fractional bias (FB) near to zero. Fig.…”

Section: Is the Von Karmanmentioning

confidence: 99%

See 2 more Smart Citations

Derivation of the Turbulent Time Scales and Velocity Variances from LES Spectral Data: Application in a Lagrangian Stochastic Dispersion Model

Maldaner¹,

Degrazia²,

Rizza³

et al. 2014

TOASCJ

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Turbulent time scales and velocity variances for a convective boundary layer are derived from large eddy simulation spectral data. Spectral peak frequencies obtained from LES data are used directly in expressions that allow establishing such times scales and velocity variances. These turbulent parameters were compared with those provided by experimental turbulence data. The comparison employing a stochastic dispersion model and observed concentration data shows that both parameterizations reproduce adequately the contaminant dispersion process in a convective boundary layer.

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Asymptotic solution for turbulent variances: application to convergence of averages and particle dispersion

Degrazia,

Stefanello,

Costa

et al. 2024

Academia Environmental Sciences and Sustainability

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An alternative solution to the asymptotic Taylor statistical diffusion theorem (variance of particle dispersion) and the asymptotic variance quantifying convergence of averages is presented. The solution approach is to identify a representation of the Dirac delta function for large times in the same integral occurring in both variances. This particular function acts as an effective filter and provides a way to obtain an analytical solution. Considering these asymptotic dispersion parameters in a Gaussian diffusion model, analysis of the simulated concentration results shows that the model reproduces well the experimental ground-level concentration data. The variance between the temporal and ensemble averages is evaluated using turbulent observational data collected in a convective boundary layer. The results show that the difference between the temporal and ensemble means is of the order of 5% for an average time window of 1800 s. The present development can be used in a variety of situations involving different types of turbulence.

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Employing Taylor and Heisenberg subfilter viscosities to simulate turbulent statistics in LES models

Cited by 5 publications

References 37 publications

Monitoring the Micrometeorology of a Coastal Site next to a Thermal Power Plant from the Surface to 140 m

Monitoring the Micrometeorology of a Coastal Site next to a Thermal Power Plant from the Surface to 140 m

Derivation of the Turbulent Time Scales and Velocity Variances from LES Spectral Data: Application in a Lagrangian Stochastic Dispersion Model

Asymptotic solution for turbulent variances: application to convergence of averages and particle dispersion

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