Relevant bibliographies by topics / Gaussian plume model

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Academic literature on the topic 'Gaussian plume model'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Gaussian plume model"

1

Scase, M. M., and R. E. Hewitt. "Unsteady turbulent plume models." Journal of Fluid Mechanics 697 (March 12, 2012): 455–80. http://dx.doi.org/10.1017/jfm.2012.77.

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AbstractFour existing integral models of unsteady turbulent plumes are revisited. We demonstrate that none of these published models is ideal for general descriptions of unsteady behaviour and put forward a modified model. We show that the most recent (top-hat) plume model (Scase et al. J. Fluid Mech., vol. 563, 2006, p. 443), and the earlier (Gaussian) plume models (Delichatsios J. Fluid Mech., vol. 93, 1979, p. 241; Yu Trans. ASME, vol. 112, 1990, p.186), are all ill-posed. This ill-posedness arises from the downstream growth of short-scale waves, which have an unbounded downstream growth rate. We show that both the top-hat and the Gaussian (Yu) models can be regularized, rendering them well-posed, by the inclusion of a velocity diffusion term. The effect of including this diffusive mechanism is to include a vertical structure in the model that can be interpreted as representing the vertical extent of an eddy. The effects of this additional mechanism are small for steady applications, and cases where the plume forcing can be considered to follow a power law (both of which have been studied extensively). However, the inclusion of diffusion is shown to be crucial to the general initial-value problem for unsteady models.
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2

Korsakissok, Irène, and Vivien Mallet. "Comparative Study of Gaussian Dispersion Formulas within the Polyphemus Platform: Evaluation with Prairie Grass and Kincaid Experiments." Journal of Applied Meteorology and Climatology 48, no. 12 (2009): 2459–73. http://dx.doi.org/10.1175/2009jamc2160.1.

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Abstract This paper details a number of existing formulations used in Gaussian models in a clear and usable way, and provides a comparison within a single framework—the Gaussian plume and puff models of the air quality modeling system Polyphemus. The emphasis is made on the comparison between 1) the parameterizations to compute the standard deviations and 2) the plume rise schemes. The Gaussian formulas are first described and theoretically compared. Their evaluation is then ensured by comparison with the observations as well as with several well-known Gaussian and computational fluid dynamics model performances. The model results compare well to the other Gaussian models for two of the three parameterizations for standard deviations, Briggs’s and similarity theory, while Doury’s shows a tendency to underestimate the concentrations because of a large horizontal spread. The results with the Kincaid experiment point out the sensitivity to the plume rise scheme and the importance of an accurate modeling of the plume interactions with the inversion layer. Using three parameterizations for the standard deviations and the same number of plume rise schemes, the authors were able to highlight a large variability in the model outputs.
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3

Carrascal, M. D., M. Puigcerver, and P. Puig. "Sensitivity of Gaussian plume model to dispersion specifications." Theoretical and Applied Climatology 48, no. 2-3 (1993): 147–57. http://dx.doi.org/10.1007/bf00864921.

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4

Li, Jia Yang, Feng Guo, and Ya Nan Li. "Diffusion Model of Atmospheric Fine Particles PM2.5 under the Direction of the Wind." Applied Mechanics and Materials 587-589 (July 2014): 780–83. http://dx.doi.org/10.4028/www.scientific.net/amm.587-589.780.

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The causes of atmospheric fine particles PM2.5 are complicated and are influenced by multiple factors conditions. Considering the analysis of the diffusion problem under the direction of the wind change, this paper proposes a gray theory and a new way of combining Gaussian plume model, namely the MGM - Gaussian plume diffusion model. Draw the diffusion area of PM2.5 under the influence of the wind.
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5

Demael, E., and B. Carissimo. "Comparative Evaluation of an Eulerian CFD and Gaussian Plume Models Based on Prairie Grass Dispersion Experiment." Journal of Applied Meteorology and Climatology 47, no. 3 (2008): 888–900. http://dx.doi.org/10.1175/2007jamc1375.1.

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Abstract A theoretical and statistical comparison of a three-dimensional computational fluid dynamics (CFD) model with two Gaussian plume models is proposed on the Prairie Grass data field experiment for neutral conditions, using both maximum arcwise concentrations and spatially paired observations. In theory, it is impossible to have the same near-source behavior with the Eulerian CFD code as with the Gaussian plume models. The former presents the inability to account for the dependence of the turbulent diffusivity to the distance from the source, contrary to plume models for which this dependence is fitted to observations. The study described herein looks at the practical implications of these theoretical differences by comparing the two different types of models on a flat-terrain case, a situation favoring Gaussian models. The results herein show that the Eulerian CFD model gives acceptable results, both for arc-maximum concentrations and spatially paired observations. Indeed, the statistical performances are above the criteria of “good performance” commonly defined in literature. In general, the results for Eulerian code fall between those of a Gaussian model that has been fitted using the Prairie Grass dataset and those of one fitted with different datasets.
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6

LIMA NETO, IRAN E., and PRISCILA A. B. PARENTE. "Influence of mass transfer on bubble plume hydrodynamics." Anais da Academia Brasileira de Ciências 88, no. 1 (2016): 411–22. http://dx.doi.org/10.1590/0001-3765201520140453.

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ABSTRACT This paper presents an integral model to evaluate the impact of gas transfer on the hydrodynamics of bubble plumes. The model is based on the Gaussian type self-similarity and functional relationships for the entrainment coefficient and factor of momentum amplification due to turbulence. The impact of mass transfer on bubble plume hydrodynamics is investigated considering different bubble sizes, gas flow rates and water depths. The results revealed a relevant impact when fine bubbles are considered, even for moderate water depths. Additionally, model simulations indicate that for weak bubble plumes (i.e., with relatively low flow rates and large depths and slip velocities), both dissolution and turbulence can affect plume hydrodynamics, which demonstrates the importance of taking the momentum amplification factor relationship into account. For deeper water conditions, simulations of bubble dissolution/decompression using the present model and classical models available in the literature resulted in a very good agreement for both aeration and oxygenation processes. Sensitivity analysis showed that the water depth, followed by the bubble size and the flow rate are the most important parameters that affect plume hydrodynamics. Lastly, dimensionless correlations are proposed to assess the impact of mass transfer on plume hydrodynamics, including both the aeration and oxygenation modes.
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7

Craske, John, and Maarten van Reeuwijk. "Generalised unsteady plume theory." Journal of Fluid Mechanics 792 (March 9, 2016): 1013–52. http://dx.doi.org/10.1017/jfm.2016.72.

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We develop a generalised unsteady plume theory and compare it with a new direct numerical simulation (DNS) dataset for an ensemble of statistically unsteady turbulent plumes. The theoretical framework described in this paper generalises previous models and exposes several fundamental aspects of the physics of unsteady plumes. The framework allows one to understand how the structure of the governing integral equations depends on the assumptions one makes about the radial dependence of the longitudinal velocity, turbulence and pressure. Consequently, the ill-posed models identified by Scase & Hewitt (J. Fluid Mech., vol. 697, 2012, pp. 455–480) are shown to be the result of a non-physical assumption regarding the velocity profile. The framework reveals that these ill-posed unsteady plume models are degenerate cases amongst a comparatively large set of well-posed models that can be derived from the generalised unsteady plume equations that we obtain. Drawing on the results of DNS of a plume subjected to an instantaneous step change in its source buoyancy flux, we use the framework in a diagnostic capacity to investigate the properties of the resulting travelling wave. In general, the governing integral equations are hyperbolic, becoming parabolic in the limiting case of a ‘top-hat’ model, and the travelling wave can be classified as lazy, pure or forced according to the particular assumptions that are invoked to close the integral equations. Guided by observations from the DNS data, we use the framework in a prognostic capacity to develop a relatively simple, accurate and well-posed model of unsteady plumes that is based on the assumption of a Gaussian velocity profile. An analytical solution is presented for a pure straight-sided plume that is consistent with the key features observed from the DNS.
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8

Mallia, Derek, Adam Kochanski, Shawn Urbanski, and John Lin. "Optimizing Smoke and Plume Rise Modeling Approaches at Local Scales." Atmosphere 9, no. 5 (2018): 166. http://dx.doi.org/10.3390/atmos9050166.

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Heating from wildfires adds buoyancy to the overlying air, often producing plumes that vertically distribute fire emissions throughout the atmospheric column over the fire. The height of the rising wildfire plume is a complex function of the size of the wildfire, fire heat flux, plume geometry, and atmospheric conditions, which can make simulating plume rises difficult with coarser-scale atmospheric models. To determine the altitude of fire emission injection, several plume rise parameterizations have been developed in an effort estimate the height of the wildfire plume rise. Previous work has indicated the performance of these plume rise parameterizations has generally been mixed when validated against satellite observations. However, it is often difficult to evaluate the performance of plume rise parameterizations due to the significant uncertainties associated with fire input parameters such as fire heat fluxes and area. In order to reduce the uncertainties of fire input parameters, we applied an atmospheric modeling framework with different plume rise parameterizations to a well constrained prescribed burn, as part of the RxCADRE field experiment. Initial results found that the model was unable to reasonably replicate downwind smoke for cases when fire emissions were emitted at the surface and released at the top of the plume. However, when fire emissions were distributed below the plume top following a Gaussian distribution, model results were significantly improved.
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9

Karol, I. L., Y. E. Ozolin, and E. V. Rozanov. "Box and Gaussian plume models of the exhaust composition evolution of subsonic transport aircraft in- and out of the flight corridor." Annales Geophysicae 15, no. 1 (1997): 88–96. http://dx.doi.org/10.1007/s00585-997-0088-0.

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Abstract. A box and a Gaussian plume model including gas-phase photochemistry and with plume dispersion parameters estimated from the few available plume observations are proposed and used for evaluation of photochemical transformations of exhausts from a single subsonic transport aircraft. The effects of concentration inhomogeneities in the plume cross section on the photochemical sources and sinks in the plume are analyzed for various groups of compounds. The influence of these inhomogeneities on the rate and on the mass of ambient air entrainment into the plume are studied also by comparing the box and the Gaussian plume model simulations during the first hours of their "life''. Due to the enterance of HOx and NOx from ambient air into the plume with rates varying from the wind shear and turbulence conditions, the rate of emitted NOx oxidation in the plume is dependent on these and also on the background concentration levels of HOx and NOx.
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10

Sanchez, M. L., J. L. Sanchez, and M. C. Ramos. "A statistical approach for improving the Gaussian plume model." Atmospheric Research 21, no. 1 (1987): 75–82. http://dx.doi.org/10.1016/0169-8095(87)90018-4.

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