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Peiyong Wang - One of the best experts on this subject based on the ideXlab platform.
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Model Constant C of Turbulent NO Reaction Time Model
Case Studies in Thermal Engineering, 2017Co-Authors: Yuexinzhu Lan, Peiyong WangAbstract:Abstract The NO concentration of turbulent jet flames with different fuel, jet velocity, and jet diameter are simulated with the turbulent NO reaction time Model. The predicted NO profile has very good agreement with the experimental data. Each flame has an optimal Model Constant C value. By comparing the flames with different operation condition and fuel, it is found out that the C value is insensitive to the operation condition such as jet velocity and jet diameter. The C value is mainly determined by fuel, its diffusion and reaction characteristics. The faster the fuel and air mix and react, the larger the C value. The fuel/N2 mass diffusion coefficient is chosen to represent fuel's diffusion characteristic, and the flame speed of stoichiomatric fuel/air mixture is chosen to represent fuel's reaction characteristic. From the data of the six flames, accurate correlations among Model Constant C, mass diffusion coefficient, and flame speed are established.
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The Model Constant A of the eddy dissipation Model
Progress in Computational Fluid Dynamics An International Journal, 2016Co-Authors: Peiyong WangAbstract:The eddy dissipation Model has been used extensively in turbulent combustion Modelling because of its simplicity, good convergence, and reasonable accuracy. The Model Constant A with the standard value 4 has been used in numerous CFD applications without verification and validation. A systematic investigation of the Model Constant A has been carried out here. Eleven turbulent diffusion flames including round jet flames and opposed jet flames are CFD simulated and the simulation results are compared with experimental data. It is revealed that the flame temperature predicted with A = 4 deviates from experimental data severely. An optimal A value can be found to match experimental data for each flame; this value depends on fuel, chemistry, and the turbulence strength of combustion flow field; it varies between 0.77 and 25 for the 11 flames. A correlation of optimal A value with turbulent Reynolds number is presented; a modification to the original eddy dissipation Model is also presented and validated.
Paul A. Durbin - One of the best experts on this subject based on the ideXlab platform.
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On the dynamic computation of the Model Constant in delayed detached eddy simulation
Physics of Fluids, 2015Co-Authors: Zifei Yin, K.r. Reddy, Paul A. DurbinAbstract:The current work puts forth an implementation of a dynamic procedure to locally compute the value of the Model Constant CDES, as used in the eddy simulation branch of Delayed Detached Eddy Simulation (DDES). Former DDES formulations [P. R. Spalart et al., “A new version of detached-eddy simulation, resistant to ambiguous grid densities,” Theor. Comput. Fluid Dyn. 20, 181 (2006); M. S. Gritskevich et al., “Development of DDES and IDDES formulations for the k- ω shear stress transport Model,” Flow, Turbul. Combust. 88, 431 (2012)] are not conducive to the implementation of a dynamic procedure due to uncertainty as to what form the eddy viscosity expression takes in the eddy simulation branch. However, a recent, alternate formulation [K. R. Reddy et al., “A DDES Model with a Smagorinsky-type eddy viscosity formulation and log-layer mismatch correction,” Int. J. Heat Fluid Flow 50, 103 (2014)] casts the eddy viscosity in a form that is similar to the Smagorinsky, LES (Large Eddy Simulation) sub-grid viscosity...
Benoit Fiorina - One of the best experts on this subject based on the ideXlab platform.
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The influence of combustion SGS sub-Models on the resolved flame propagation. Application to the LES of the Cambridge stratified flames.
Proceedings of the Combustion Institute, 2015Co-Authors: Renaud Mercier, Thomas Schmitt, Denis Veynante, Benoit FiorinaAbstract:In Large Eddy Simulation (LES) of premixed and stratified combustion, the overall prediction of the flame consumption speed depends on various subgrid scale (SGS) subModels such as the flame wrinkling, the fuel stratification or heat losses. The objective of this study is to investigate the LES sensitivity to the subModeling strategies. Different heat losses and SGS flame wrinkling Models are presented in the context of the Filtered TAbulated Chemistry for LES (F-TACLES) formulation. LES of the non-adiabatic non-swirling bluff-body stabilized Cambridge flames (SwB burner) are presented. In this complex configuration, both flame brush and flow dynamics are influenced by flame consumption speed subModels. First, accounting for heat losses impacts the prediction of both velocity and temperature of the inner recirculation zone (IRZ). Second, Model Constants involved into SGS wrinkling subModels have a great impact on the mean flame brush position. The non-adiabatic formulation combined with a dynamic estimation of the SGS wrinkling Model Constant appears to be a very attractive approach and gives a very good prediction of both the mean flame location and the IRZ flow dynamics.
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the influence of combustion sgs subModels on the resolved flame propagation application to the les of the cambridge stratified flames
Proceedings of the Combustion Institute, 2015Co-Authors: Renaud Mercier, Thomas Schmitt, Denis Veynante, Benoit FiorinaAbstract:Abstract In Large Eddy Simulation (LES) of premixed and stratified combustion, the overall prediction of the flame consumption speed depends on various subgrid scale (SGS) subModels such as the flame wrinkling, the fuel stratification or heat losses. The objective of this study is to investigate the LES sensitivity to the subModeling strategies. Different heat losses and SGS flame wrinkling Models are presented in the context of the Filtered TAbulated Chemistry for LES (F-TACLES) formulation. LES of the non-adiabatic non-swirling bluff-body stabilized Cambridge flames (SwB burner) are presented. In this complex configuration, both flame brush and flow dynamics are influenced by flame consumption speed subModels. First, accounting for heat losses impacts the prediction of both velocity and temperature of the inner recirculation zone (IRZ). Second, Model Constants involved into SGS wrinkling subModels have a great impact on the mean flame brush position. The non-adiabatic formulation combined with a dynamic estimation of the SGS wrinkling Model Constant appears to be a very attractive approach and gives a very good prediction of both the mean flame location and the IRZ flow dynamics.
Ahmet Konuralp Elicin - One of the best experts on this subject based on the ideXlab platform.
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mathematical Modelling of solar tunnel drying of thin layer organic tomato
Journal of Food Engineering, 2006Co-Authors: Kamil Sacilik, Rahmi Keskin, Ahmet Konuralp ElicinAbstract:The thin layer solar drying experiments of organic tomato using solar tunnel dryer were conducted under the ecological conditions of Ankara, Turkey. During the experiments, organic tomatoes were dried to the final moisture content of 11.50 from 93.35% w.b. in four days of drying in the solar tunnel dryer as compared to five days of drying in the open sun drying. Experimental drying curves showed only a falling drying rate period. A non-linear regression procedure was used to fit 10 different thin layer mathematical Models available in literature to the experimental drying curves. The Models were compared using the coefficient of determination, mean relative percent error, root mean square error and the reduced chi-square. The approximation of diffusion Model has shown a better fit to the experimental drying data as compared to other Models. The effect of the drying temperature and relative humidity on the drying Model Constant and coefficients were also determined. Samples dried in the solar tunnel dryer were completely protected from insects, rain and dusts, and the dried samples were of high quality in terms of colour and hygienic. This system can be used for drying various agricultural products. Also, it is simple in construction and can be constructed at a low cost with locally obtainable materials.
P. Sagaut - One of the best experts on this subject based on the ideXlab platform.
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Is the Smagorinsky coefficient sensitive to uncertainty in the form of the energy spectrum?
Physics of Fluids, 2011Co-Authors: M. Meldi, Didier Lucor, P. SagautAbstract:We investigate the influence of uncertainties in the shape of the energy spectrum over the Smagorinsky [“General circulation experiments with the primitive equations. I: The basic experiment,” Mon. Weather Rev. 91(3), 99 (1963)] subgrid scale Model Constant CS : the analysis is carried out by a stochastic approach based on generalized polynomialchaos. The free parameters in the considered energy spectrum functional forms are Modeled as random variables over bounded supports: two Models of the energy spectrum are investigated, namely, the functional form proposed by Pope [Turbulent Flows (Cambridge University Press, Cambridge, 2000)] and by Meyers and Meneveau [“A functional form for the energy spectrum parametrizing bottleneck and intermittency effects,” Phys. Fluids 20(6), 065109 (2008)]. The Smagorinsky Model coefficient, computed from the algebraic relation presented in a recent work by Meyers and Sagaut [“On the Model coefficients for the standard and the variational multi-scale Smagorinsky Model,” J. Fluid Mech. 569, 287 (2006)], is considered as a stochastic process and is described by numerical tools streaming from the probability theory. The uncertainties are introduced in the free parameters shaping the energy spectrum in correspondence to the large and the small scales, respectively. The predicted Model Constant is weakly sensitive to the shape of the energy spectrum when large scales uncertainty is considered: if the large-eddy simulation(LES) filter cut is performed in the inertial range, a significant probability to recover values lower in magnitude than the asymptotic Lilly-Smagorinsky Model Constant is recovered. Furthermore, the predicted Model Constant occurrences cluster in a compact range of values: the correspondent probability density function rapidly drops to zero approaching the extremes values of the range, which show a significant sensitivity to the LES filter width. The sensitivity of the Model Constant to uncertainties propagated in the small scales of the energy spectrum is noticeable and a wide range of possible Smagorinsky Model Constant values is observed, if the LES filter cut is performed close to the dissipation region.