Using the lattice-Boltzmann computational approach in conjunction with the Reynolds averaged Navier-Stokes （RANS） model, several turbulent flows and the transport and deposition of particles in different passages we...Using the lattice-Boltzmann computational approach in conjunction with the Reynolds averaged Navier-Stokes （RANS） model, several turbulent flows and the transport and deposition of particles in different passages were studied. The new lattice Boltzmann method （LBM） solved the RANS equations coupled with the standard and renormalization group k-E turbulence models. In particular, the LBM formulation was augmented by the addition of two transport equations for the probability distribution function of populations of k and 8. The discrete random walk model was used to generate the instanta- neous turbulence fluctuations. For turbulent channel flows, the analytical fits to the root mean-square velocity fluctuations obtained by the direct numerical simulation of the turbulent flow were used in the analysis. Attention was given to the proper evaluation of the wall normal turbulent velocity fluctuations particularly near the wall. The simulation results were compared with the available numerical simulation and experimental data. The new LBM-RANS model is shown to provide a reasonably accurate description of turbulent flows and particle transport and deposition at modest computational cost.展开更多
For surface runoff estimation in the Soil and Water Assessment Tool(SWAT)model,the curve number(CN)procedure is commonly adopted to calculate surface runoff by dynamically updating CN values based on antecedent soil m...For surface runoff estimation in the Soil and Water Assessment Tool(SWAT)model,the curve number(CN)procedure is commonly adopted to calculate surface runoff by dynamically updating CN values based on antecedent soil moisture condition(SCSI)in field.From SWAT2005 and onward,an alternative approach has become available to apply the CN method by relating the runoff potential to daily evapotranspiration(SCSII).While improved runoff prediction with SCSII has been reported in several case studies,few investigations have been made on its influence to water quality output or on the model uncertainty associated with the SCSII method.The objectives of the research were:(1)to quantify the improvements in hydrologic and water quality predictions obtained through different surface runoff estimation techniques;and(2)to examine how model uncertainty is affected by combining different surface runoff estimation techniques within SWAT using Bayesian model averaging(BMA).Applications of BMA provide an alternative approach to investigate the nature of structural uncertainty associated with both CN methods.Results showed that SCSII and BMA associated approaches exhibit improved performance in both discharge and total NO3 predictions compared to SCSI.In addition,the application of BMA has a positive effect on finding well performed solutions in the multi-dimensional parameter space,but the predictive uncertainty is not evidently reduced or enhanced.Therefore,we recommend additional future SWAT calibration/validation research with an emphasis on the impact of SCSII on the prediction of other pollutants.展开更多
文摘Using the lattice-Boltzmann computational approach in conjunction with the Reynolds averaged Navier-Stokes （RANS） model, several turbulent flows and the transport and deposition of particles in different passages were studied. The new lattice Boltzmann method （LBM） solved the RANS equations coupled with the standard and renormalization group k-E turbulence models. In particular, the LBM formulation was augmented by the addition of two transport equations for the probability distribution function of populations of k and 8. The discrete random walk model was used to generate the instanta- neous turbulence fluctuations. For turbulent channel flows, the analytical fits to the root mean-square velocity fluctuations obtained by the direct numerical simulation of the turbulent flow were used in the analysis. Attention was given to the proper evaluation of the wall normal turbulent velocity fluctuations particularly near the wall. The simulation results were compared with the available numerical simulation and experimental data. The new LBM-RANS model is shown to provide a reasonably accurate description of turbulent flows and particle transport and deposition at modest computational cost.
基金This study was supported in part by the US DA-National Institute of Food and Agriculture grants 2007-51130-03876,2009-51130-06038the Research Program for Agricultural Science&Technology Development(Project No.PJ008566)National Academy of Agricultural Science,Rural Development Administration,Republic of Korea,and the USDA-NRCS Conservation Effects Assessment Project(CEAP)-Wildlife and Cropland components.
文摘For surface runoff estimation in the Soil and Water Assessment Tool(SWAT)model,the curve number(CN)procedure is commonly adopted to calculate surface runoff by dynamically updating CN values based on antecedent soil moisture condition(SCSI)in field.From SWAT2005 and onward,an alternative approach has become available to apply the CN method by relating the runoff potential to daily evapotranspiration(SCSII).While improved runoff prediction with SCSII has been reported in several case studies,few investigations have been made on its influence to water quality output or on the model uncertainty associated with the SCSII method.The objectives of the research were:(1)to quantify the improvements in hydrologic and water quality predictions obtained through different surface runoff estimation techniques;and(2)to examine how model uncertainty is affected by combining different surface runoff estimation techniques within SWAT using Bayesian model averaging(BMA).Applications of BMA provide an alternative approach to investigate the nature of structural uncertainty associated with both CN methods.Results showed that SCSII and BMA associated approaches exhibit improved performance in both discharge and total NO3 predictions compared to SCSI.In addition,the application of BMA has a positive effect on finding well performed solutions in the multi-dimensional parameter space,but the predictive uncertainty is not evidently reduced or enhanced.Therefore,we recommend additional future SWAT calibration/validation research with an emphasis on the impact of SCSII on the prediction of other pollutants.
