The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform
Michael E Mueller - One of the best experts on this subject based on the ideXlab platform.
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Joint Probability Density function models for multiscalar turbulent mixing
Combustion and Flame, 2018Co-Authors: Bruce A Perry, Michael E MuellerAbstract:Abstract Modeling multicomponent turbulent mixing is essential for simulations of turbulent combustion, which is controlled by mixing of fuel, oxidizer, combustion products, and intermediate species. One challenge is to find functions that can reproduce the Joint Probability Density function (PDF) of scalar mixing states using only a small number of parameters. Even for mixing with only two independent scalars, several statistical distributions, including the Dirichlet, Connor–Mosimann (CM), five-parameter bivariate beta (BVB5), and statistically-most-likely distributions, have previously been proposed for this purpose, with minimal physical justification. This work uses the concept of statistical neutrality to relate these distributions to each other, relate the distributions to physical mixing configurations, and develop a systematic approach to model selection. This approach is validated by comparing the ability of these distributions to reproduce the evolution of the scalar PDF from Direct Numerical Simulations of three-component passive scalar mixing in isotropic turbulence with 11 different initial conditions that are representative of a wide range of mixing conditions of interest. The approach correctly identifies whether the Dirichlet, CM, and BVB5 distributions, which are increasingly complex bivariate generalizations of the beta distribution, can accurately model the Joint PDFs, but knowledge of the mixing configuration is required to select the appropriate distribution. The statistically-most-likely distribution is generally less accurate than the appropriate bivariate beta distribution but still gives reasonable predictions and does not require knowledge of the mixing configuration, so it is a suitable model when no single mixing configuration can be identified.
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Joint Probability Density function models for multiscalar turbulent mixing
Combustion and Flame, 2018Co-Authors: Bruce A Perry, Michael E MuellerAbstract:Abstract Modeling multicomponent turbulent mixing is essential for simulations of turbulent combustion, which is controlled by mixing of fuel, oxidizer, combustion products, and intermediate species. One challenge is to find functions that can reproduce the Joint Probability Density function (PDF) of scalar mixing states using only a small number of parameters. Even for mixing with only two independent scalars, several statistical distributions, including the Dirichlet, Connor–Mosimann (CM), five-parameter bivariate beta (BVB5), and statistically-most-likely distributions, have previously been proposed for this purpose, with minimal physical justification. This work uses the concept of statistical neutrality to relate these distributions to each other, relate the distributions to physical mixing configurations, and develop a systematic approach to model selection. This approach is validated by comparing the ability of these distributions to reproduce the evolution of the scalar PDF from Direct Numerical Simulations of three-component passive scalar mixing in isotropic turbulence with 11 different initial conditions that are representative of a wide range of mixing conditions of interest. The approach correctly identifies whether the Dirichlet, CM, and BVB5 distributions, which are increasingly complex bivariate generalizations of the beta distribution, can accurately model the Joint PDFs, but knowledge of the mixing configuration is required to select the appropriate distribution. The statistically-most-likely distribution is generally less accurate than the appropriate bivariate beta distribution but still gives reasonable predictions and does not require knowledge of the mixing configuration, so it is a suitable model when no single mixing configuration can be identified.
Chin-hoh Moeng - One of the best experts on this subject based on the ideXlab platform.
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Parameterizing turbulent diffusion through the Joint Probability Density
Boundary-Layer Meteorology, 1992Co-Authors: John C. Wyngaard, Chin-hoh MoengAbstract:The “convective mass flux” parameterization often used in meteorological modeling expresses the vertical flux of a transported scalar as proportional to the product of the difference in mean values of the scalar in updrafts and downdrafts and their characteristic velocity. The proportionality factor is a constant to be specified. We show that this proportionality factor also appears in the “relaxed eddy accumulation” technique of Businger and Oncley. That associates the surface-layer flux of a scalar with the product of the standard deviation of vertical velocity and the mean concentration difference between updrafts and downdrafts. We show that this constant ( b ) is determined uniquely by the Joint Probability Density (jpd) of vertical velocity and the scalar. Using large-eddy simulation, we generate this jpd for a conservative scalar diffusing through a convective boundary layer. It has quite different forms in “top-down” and “bottom-up” diffusion geometries. The bottom-up jpd is fairly well represented by a Jointly Gaussian form and implies b ~ 0.6, in good agreement with the surface-layer value reported by Businger and Oncley. The top-down jpd is strikingly non-Gaussian and gives b ~ 0.47. Updrafts carry the bulk of the scalar flux - 70% in the bottom-up case, 60% in the top-down case.
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Parameterizing turbulent diffusion through the Joint Probability Density
Boundary-Layer Meteorology, 1992Co-Authors: John C. Wyngaard, Chin-hoh MoengAbstract:The “convective mass flux” parameterization often used in meteorological modeling expresses the vertical flux of a transported scalar as proportional to the product of the difference in mean values of the scalar in updrafts and downdrafts and their characteristic velocity. The proportionality factor is a constant to be specified. We show that this proportionality factor also appears in the “relaxed eddy accumulation” technique of Businger and Oncley. That associates the surface-layer flux of a scalar with the product of the standard deviation of vertical velocity and the mean concentration difference between updrafts and downdrafts.
C K Chan - One of the best experts on this subject based on the ideXlab platform.
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presumed Joint Probability Density function model for turbulent combustion
Fuel, 2003Co-Authors: H Q Zhang, C K ChanAbstract:Abstract A presumed Joint Probability Density function (pdf) model of turbulent combustion is proposed in this paper. The turbulent fluctuations of reactant concentrations and temperature are described using a presumed Joint pdf of three-dimensional Gaussian distribution based on first and second-order moments of reactant concentration and temperature. Mean reaction rates in both premixed and diffusion combustion are obtained by mean of integration under the presumed Joint pdf. This model is applied to predict turbulent premixed combustion of sudden-expansion flow and turbulent jet diffusion methane/air flame. For turbulent premixed combustion, the predicted results of temperature distribution and maximum temperature using the proposed model agree better with the experiment than that using the conventional eddy-breakup (EBU)–Arrhenius model. For the turbulent jet diffusion methane/air flame, the predicted results of velocity, temperature and species concentrations using the proposed model, the Arrhenius, EBU–Arrhenius, and laminar flamelet models are compared with experiment data. Results obtained with the presumed pdf model and that obtained by the laminar flamelet model both agree well with experiments, while results using the other models have a significant difference. The presumed Joint pdf model is used to predict the NO formation process, which also agrees well with the experiment data. A unified turbulent combustion model, in which both effects of turbulent diffusion and chemical dynamics are considered, is established for both premixed and diffusion combustion, especially for the process of NO formation.
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Presumed Joint Probability Density function model for turbulent combustion
Fuel, 2003Co-Authors: Z.m. Guo, H Q Zhang, C K Chan, W.y. LinAbstract:Abstract A presumed Joint Probability Density function (pdf) model of turbulent combustion is proposed in this paper. The turbulent fluctuations of reactant concentrations and temperature are described using a presumed Joint pdf of three-dimensional Gaussian distribution based on first and second-order moments of reactant concentration and temperature. Mean reaction rates in both premixed and diffusion combustion are obtained by mean of integration under the presumed Joint pdf. This model is applied to predict turbulent premixed combustion of sudden-expansion flow and turbulent jet diffusion methane/air flame. For turbulent premixed combustion, the predicted results of temperature distribution and maximum temperature using the proposed model agree better with the experiment than that using the conventional eddy-breakup (EBU)–Arrhenius model. For the turbulent jet diffusion methane/air flame, the predicted results of velocity, temperature and species concentrations using the proposed model, the Arrhenius, EBU–Arrhenius, and laminar flamelet models are compared with experiment data. Results obtained with the presumed pdf model and that obtained by the laminar flamelet model both agree well with experiments, while results using the other models have a significant difference. The presumed Joint pdf model is used to predict the NO formation process, which also agrees well with the experiment data. A unified turbulent combustion model, in which both effects of turbulent diffusion and chemical dynamics are considered, is established for both premixed and diffusion combustion, especially for the process of NO formation.
Bruce A Perry - One of the best experts on this subject based on the ideXlab platform.
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Joint Probability Density function models for multiscalar turbulent mixing
Combustion and Flame, 2018Co-Authors: Bruce A Perry, Michael E MuellerAbstract:Abstract Modeling multicomponent turbulent mixing is essential for simulations of turbulent combustion, which is controlled by mixing of fuel, oxidizer, combustion products, and intermediate species. One challenge is to find functions that can reproduce the Joint Probability Density function (PDF) of scalar mixing states using only a small number of parameters. Even for mixing with only two independent scalars, several statistical distributions, including the Dirichlet, Connor–Mosimann (CM), five-parameter bivariate beta (BVB5), and statistically-most-likely distributions, have previously been proposed for this purpose, with minimal physical justification. This work uses the concept of statistical neutrality to relate these distributions to each other, relate the distributions to physical mixing configurations, and develop a systematic approach to model selection. This approach is validated by comparing the ability of these distributions to reproduce the evolution of the scalar PDF from Direct Numerical Simulations of three-component passive scalar mixing in isotropic turbulence with 11 different initial conditions that are representative of a wide range of mixing conditions of interest. The approach correctly identifies whether the Dirichlet, CM, and BVB5 distributions, which are increasingly complex bivariate generalizations of the beta distribution, can accurately model the Joint PDFs, but knowledge of the mixing configuration is required to select the appropriate distribution. The statistically-most-likely distribution is generally less accurate than the appropriate bivariate beta distribution but still gives reasonable predictions and does not require knowledge of the mixing configuration, so it is a suitable model when no single mixing configuration can be identified.
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Joint Probability Density function models for multiscalar turbulent mixing
Combustion and Flame, 2018Co-Authors: Bruce A Perry, Michael E MuellerAbstract:Abstract Modeling multicomponent turbulent mixing is essential for simulations of turbulent combustion, which is controlled by mixing of fuel, oxidizer, combustion products, and intermediate species. One challenge is to find functions that can reproduce the Joint Probability Density function (PDF) of scalar mixing states using only a small number of parameters. Even for mixing with only two independent scalars, several statistical distributions, including the Dirichlet, Connor–Mosimann (CM), five-parameter bivariate beta (BVB5), and statistically-most-likely distributions, have previously been proposed for this purpose, with minimal physical justification. This work uses the concept of statistical neutrality to relate these distributions to each other, relate the distributions to physical mixing configurations, and develop a systematic approach to model selection. This approach is validated by comparing the ability of these distributions to reproduce the evolution of the scalar PDF from Direct Numerical Simulations of three-component passive scalar mixing in isotropic turbulence with 11 different initial conditions that are representative of a wide range of mixing conditions of interest. The approach correctly identifies whether the Dirichlet, CM, and BVB5 distributions, which are increasingly complex bivariate generalizations of the beta distribution, can accurately model the Joint PDFs, but knowledge of the mixing configuration is required to select the appropriate distribution. The statistically-most-likely distribution is generally less accurate than the appropriate bivariate beta distribution but still gives reasonable predictions and does not require knowledge of the mixing configuration, so it is a suitable model when no single mixing configuration can be identified.
Celia Bueno - One of the best experts on this subject based on the ideXlab platform.
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a Joint Probability Density function of wind speed and direction for wind energy analysis
Energy Conversion and Management, 2008Co-Authors: Jose A Carta, Penelope Ramirez, Celia BuenoAbstract:Abstract A very flexible Joint Probability Density function of wind speed and direction is presented in this paper for use in wind energy analysis. A method that enables angular–linear distributions to be obtained with specified marginal distributions has been used for this purpose. For the marginal distribution of wind speed we use a singly truncated from below Normal–Weibull mixture distribution. The marginal distribution of wind direction comprises a finite mixture of von Mises distributions. The proposed model is applied in this paper to wind direction and wind speed hourly data recorded at several weather stations located in the Canary Islands (Spain). The suitability of the distributions is judged from the coefficient of determination R 2 . The conclusions reached are that the Joint distribution proposed in this paper: (a) can represent unimodal, bimodal and bitangential wind speed frequency distributions, (b) takes into account the frequency of null winds, (c) represents the wind direction regimes in zones with several modes or prevailing wind directions, (d) takes into account the correlation between wind speeds and its directions. It can therefore be used in several tasks involved in the evaluation process of the wind resources available at a potential site. We also conclude that, in the case of the Canary Islands, the proposed model provides better fits in all the cases analysed than those obtained with the models used in the specialised literature on wind energy.
