Turbulent Schmidt numbers for CFD analysis with various types of flowfield

Tominaga, Yoshihide; Stathopoulos, T.

doi:10.1016/j.atmosenv.2007.06.054

Cited by 406 publications

(195 citation statements)

References 31 publications

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“…The relation involves a dimensionless parameter known as the turbulent Schmidt number (Sc t = ν t /D t ). Variations in the value of Sc t are known to have a large influence on the concentration field (Tominaga and Stathopoulos, 2007;Blocken et al, 2008). In this study, three values of Sc t are used: 0.3, 0.5 and 0.7, which are in the range of those used in previous studies (e.g.…”

Section: The Rans Standard K-ε Modelmentioning

confidence: 99%

CFD simulation of near-field pollutant dispersion on a high-resolution grid: A case study by LES and RANS for a building group in downtown Montreal

Gousseau¹,

Blocken²,

Stathopoulos³

et al. 2011

Atmospheric Environment

300

148

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Turbulence modeling and validation by experiments are key issues in the simulation of micro-scale atmospheric dispersion. This study evaluates the performance of two different modeling approaches (RANS standard k-ε and LES) applied to pollutant dispersion in an actual urban environment: downtown Montreal. The focus of the study is on near-field dispersion, i.e. both on the prediction of pollutant concentrations in the surrounding streets (for pedestrian outdoor air quality) and on building surfaces (for ventilation system inlets and indoor air quality). The high-resolution CFD simulations are performed for neutral atmospheric conditions and are validated by detailed wind-tunnel experiments. A suitable resolution of the computational grid is determined by grid-sensitivity analysis. It is shown that the performance of the standard k-ε model strongly depends on the turbulent Schmidt number, whose optimum value is case-dependent and a priori unknown. In contrast, LES with the dynamic subgrid-scale model shows a better performance without requiring any parameter input to solve the dispersion equation.

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Section: The Rans Standard K-ε Modelmentioning

confidence: 99%

CFD simulation of near-field pollutant dispersion on a high-resolution grid: A case study by LES and RANS for a building group in downtown Montreal

Gousseau¹,

Blocken²,

Stathopoulos³

et al. 2011

Atmospheric Environment

300

148

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show abstract

“…It is noted that this is consistent with results for methane . This may be somewhat surprising given the significant difference between the laminar Sc values for hydrogen and methane which are 0.2 and 0.99, respectively (Tominaga and Stathopoulos, 2007) Further assessment of the simulations is provided in Fig. 11 which shows how the turbulence model and value of S ct impact the results for radial and axial injection separately.…”

Section: Axial Injectionmentioning

confidence: 99%

“…In addition, four different turbulent Schmidt (S ct ) numbers are used in numerical modeling to test the sensitivity of numerical results to different S ct as an important parameter in mixing for turbulence models (Yeung et al, 2002;Tominaga and Stathopoulos, 2007). A list of the numerical models discussed in this paper is provided in Table 2.…”

Section: Numerical Approachmentioning

confidence: 99%

Statistical Evaluation of Rans Simulations Compared To Experiments for A Model Premixer

Akbari

McDonell

Samuelsen

2013

Engineering Applications of Computational Fluid Mechanics

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This paper investigates the ability of computationally time efficient CFD models to predict mixing of hydrogen and air in a model premixer with a complex 3D geometry. Axial and radial injection of hydrogen into swirling and non-swirling air streams at standard and elevated pressures and temperatures conditions are considered. Statistical analysis is applied to the results of comparison between experimental measurements and numerical simulations. The analysis of variance suggests that, for non-swirling flows, reasonable numerical predictions can be made if appropriate modeling parameters, such as turbulence model and turbulent Schmidt number, are considered.Comparing the results at standard conditions to those at elevated pressures and temperatures suggests that the conclusions at standard conditions may hold if appropriate scaling parameters are utilized.

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“…For turbulent flows, the mass diffusion is influenced mostly by the turbulent transport. This is determined by the turbulent Schmidt number, and measures the ratio of momentum and mass diffusivity (Goldberg, Palaniswamy, Batten, & Gupta, 2010;Reynolds, 1975;Tominaga & Stathopoulos, 2007;Yeung, Xu, & Sreenivasan, 2002). The turbulent Schmidt number is relatively insensitive to the molecular fluid properties, thus there is little reason to alter the default value of Sc t = 0.7 for any species (ANSYS R Fluent R , 2012).…”

Section: Species Transportmentioning

confidence: 99%

CFD simulations of polymer devolatilization in steam contactors

Gabor

Shindle

Chandy

2017

Engineering Applications of Computational Fluid Mechanics

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Removal of volatiles and other unwanted products in a polymer mix is a critical step in polymer manufacturing. This process serves many purposes, such as improvement in the physical and chemical properties of the polymer, elimination of odours, recovery of monomers and solvents, and fulfilment of environmental regulations. In this study, the mixture of polymer and solvent, or cement, is mixed with superheated steam which causes the unwanted solvent to evaporate. As the cement droplets lose solvent and dry out, they are ejected as cement 'crumb' from the mixing device. This process is modelled using computational fluid dynamics (CFD) to gain insight into the complex phenomena. The model simulates the heat transfer and phase changes associated with cement and steam, via 3D axisymmetric calculations. Using an Eulerian-Lagrangian approach, the superheated steam is modelled as the continuous phase and tracked in an Eulerian frame, while the cement droplets are treated using a Lagrangian tracking method, thus as a whole allowing us to track the particle sizes, temperatures, and solvent content. Furthermore, a parametric study is carried out to analyse the effect of initial polymer temperature on final polymer product. The simulation is carried out with three different initial polymer temperatures and the resulting solvent concentration, and the size of the cement particles between the three cases are compared. By increasing the cement operating temperature, the solvent concentration in the cement crumb is significantly lower, and the final cement crumb sizes shows a slight decrease, which indicates better production performance. Indeed, full 3D CFD calculations, presented to verify the axisymmetric assumption, show differences in the particle statistics, which could have implications for design considerations. Such studies can provide further insights into control parameters that can potentially enhance the efficiency of such a manufacturing process. ARTICLE HISTORY

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Turbulent Schmidt numbers for CFD analysis with various types of flowfield

Cited by 406 publications

References 31 publications

CFD simulation of near-field pollutant dispersion on a high-resolution grid: A case study by LES and RANS for a building group in downtown Montreal

CFD simulation of near-field pollutant dispersion on a high-resolution grid: A case study by LES and RANS for a building group in downtown Montreal

Statistical Evaluation of Rans Simulations Compared To Experiments for A Model Premixer

CFD simulations of polymer devolatilization in steam contactors

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