Modeling the eddy transport of momentum and heat: Comparison with direct measurements in free atmosphere
2
Citation
14
Reference
10
Related Paper
Citation Trend
Keywords:
Turbulence Modeling
Momentum (technical analysis)
Eddy diffusion
Reynolds stress
Based on the dynamic equation of structure function of resolved scale turbulence a subgrid eddy viscosity model is established for anisotropy turbulence. Since the correct energy transfer between resolved and unresolved scale turbulence is involved in the model the subgrid eddy viscosity model is capable to predict anisotropic turbulence successfully, e.g. rotating turbulence and plane shear turbulence.
Turbulence Modeling
Large-Eddy Simulation
Cite
Citations (0)
The paper discusses the state-of-the-art of turbulence modeling and presents possible ways for improvement in the future turbulence models. Based on a set of turbulence closure postulations, a variation of second-order turbulence models, such as the Reynolds stress model (RSM), the algebraic stress model (k-ε-A), and the eddy viscosity model (k-ε-E), are obtained. Examples of prediction made are free shear flows, cavity flows, and flows past an off-set channel. Although a complete turbulence model does not exist at the present time, some prediction capability has been achieved by the secondorder turbulence model. The incompleteness of turbulence modeling may be attributed to the inadequacy of isotropic dissipation and single turbulent scale postulations. Use of multiple turbulence scale concepts, including use of fractal dimension of turbulent eddies may improve turbulence prediction.
Turbulence Modeling
Reynolds decomposition
Reynolds stress
Closure (psychology)
Eddy
Cite
Citations (2)
This paper summarizes application of the turbulence model to study the characteristic of flow field and the pattern of mass mixing diffusion transportation. The research present situation and new development trend on the turbulence model are analysed in detail. Moreover, the viewpoint on environmental hydraulics problem solved by turbulence model is put forward, and the traits of zero-equation turbulence model, one-equation turbulence model, two-equation k-a turbulence model and the revised anisotropic k-a turbulence model, the Reynolds stress model, the algebraic Reynolds stress model, the low-Reynolds flow model and the senior simulation of turbulence flow are discussed.
Turbulence Modeling
Reynolds stress
Reynolds decomposition
Hydraulics
Convection–diffusion equation
Cite
Citations (0)
Turbulent flow in a stirred vessel is distinguished by the domination of the swirl velocity component, which makes the flow turbulence highly anisotropic. As a result, conventional turbulence models based on the eddy viscosity hypothesis provide poor predictions for not only turbulence characteristics, but also velocity distributions. Many suggestions to modify the conventional turbulence models that take into account the effect of flow swirl have been made. Even though a reasonable distribution of the velocity field can be obtained with the modified k-ε turbulence model proposed by Launder, Priddin and Sharma (1977), the approach fails to provide a good prediction for the turbulence characteristics, especially at the core region. The Reynolds stress transport model based on a second-order closure scheme seems to be able to provide much better prediction results without any ad hoc modification. However, the computational complexity with this model is considerable. The ordinary algebraic stress model, which stems from the Reynolds stress transport model, was found not working for the swirling flows in a stirred vessel. In this work, a modification to the algebraic stress model is proposed, which takes into account additional terms arising from the production terms of the Reynolds stress transport equations during the transformation of these equations from Cartesian to cylindrical coordinate system. Comparisons of the prediction results are made with available experimental data and with the results obtained by means of the differential Reynolds stress turbulence model.
Reynolds stress
Turbulence Modeling
Reynolds decomposition
Cite
Citations (5)
The eddy viscosity turbulence models were applied to predict the flows through a cascade, and the prediction performances of turbulence models were assessed by comparing with the experimental results for a controlled diffusion(CD) compressor blade. The original k-ω turbulence model and k-ω shear stress transport(SST) turbulence model were used as two-equation turbulence model which were enhanced for a low Reynolds number flow and the Baldwin-Lomax turbulence model was used as algebraic turbulence model. Farve averaged Navier-Stokes equations in a two-dimensional, curvilinear coordinate system were solved by an implicit, cell-centered finite-volume computer code. The turbulence quantities are obtained by lagging when the mean flow equations have been updated. The numerical analysis was made to the flows of CD compressor blade in a cascade at three different incidence angles (40, 43.4, 46 degrees). We found the reversion in the prediction performance of original k-ω turbulence model and k-ω SST turbulence model when the incidence angle increased. And the algebraic Baldwin-Lomax turbulence model showed inferiority to two-equation turbulence models.
Turbulence Modeling
Reynolds decomposition
Reynolds stress
Cite
Citations (0)
Preface 1.Introduction to Turbulence 1.1.Historical View 1.2.Navier-Stokes(N-S) Equations:Validity for Turbulence 1.3.Averaging Processes 1.4.Averaged Incompressible Turbulence Equations 1.5.Turbulence Closure Problem 1.6.Summary 2.Second-Order Closure Turbulence Model 2.1.Turbulence Model Postulations 2.2.Modeling of uiuj, k, E, and uiO Equations 2.3.Summary of the Second-Order Turbulence Model 2.4.Determination of Turbulence Model Constants 2.5.Summary and Conclusion 3.Discussions of Turbulence Models 3.1.Variation of Second-Order Turbulence Models 3.2.Turbulent Flow Predictions:One(Free-Shear Flows) 3.3.Problem Function 3.4.Two-Scale Second-Order Turbulence Model 4.Near-Wall Turbulence 4.1.Introduction 4.2.Wall Functions 4.3.Low-Reynolds-Number Turbulence Models 4.4.Two-Layer Model 4.5.Direct Numerical Simulation(DNS) 4.6.Turbulent Flow Predictions:Two(Wall-Shear Flows) 4.7.Other Near-Wall Turbulence Models 4.8.Summary 5.Applications of Turbulence Models 5.1.Introduction 5.2.Turbulent Flow Predictions:Three(Two-Dimensional Separated Flows) 5.3.Turbulent Flow Past Disc Type Valves 5.4.Third-Order Closure Model 5.5.Three-Dimensional Flows 5.6.Turbulence Flow Predictions:Four(Three-dimensional Flows) 5.7.Anistrophic Turbulence Models 5.8.Conclusion 6.Turbulent Buoyant Flows 6.1.Introduction 6.2.Equation of State 6.3.Boussinesq Approximation 6.4.Averaged Turbulence Equations 6.5.Turbulent Transport Equations 6.6.Turbulence Modeling of Turbulent Buoyant Flows 6.7.Summary of the Turbulence Model 6.8.Turbulent Flow Predictions:Five (Buoyant Flows) 6.9.Two-Scale Turbulence Review Quotes Concept 7.Closure Bibliography Index
Turbulence Modeling
Reynolds decomposition
Kolmogorov microscales
Closure (psychology)
Closure problem
Cite
Citations (168)
Reynolds averaged Navier–Stokes (RANS) turbulence models are usually concerned with modeling the Reynolds stress tensor. An alternative approach to RANS turbulence modeling is described where the primary modeled quantities are the scalar and vector potentials of the turbulent body force—the divergence of the Reynolds stress tensor. This approach is shown to have a number of attractive properties, most important of which is the ability to model nonequilibrium turbulence situations accurately at a cost and complexity comparable to the widely used two-equation models such as k-e. Like Reynolds stress transport equation models, the proposed model does not require a hypothesized constitutive relation between the turbulence and the mean flow variables. This allows nonequilibrium turbulence to be modeled effectively. However, unlike Reynolds stress transport equation models, the proposed system of partial differential equations is much simpler to model and compute. It involves fewer variables, no realizability c...
Reynolds stress
Turbulence Modeling
Reynolds decomposition
Realizability
Cite
Citations (0)
A new subgrid eddy-viscosity model is proposed in this paper. Full details of the derivation of the model are given with the assumption of homogeneous turbulence. The formulation of the model is based on the dynamic equation of the structure function of resolved scale turbulence. By means of the local volume average, the effect of the anisotropy is taken into account in the generalized Kolmogorov equation, which represents the equilibrium energy transfer in the inertial subrange. Since the proposed model is formulated directly from the filtered Navier–Stokes equation, the resulting subgrid eddy viscosity has the feature that it can be adopted in various turbulent flows without any adjustments of model coefficient. The proposed model predicts the major statistical properties of rotating turbulence perfectly at fairly low-turbulence Rossby numbers whereas subgrid models, which do not consider anisotropic effects in turbulence energy transfer, cannot predict this typical anisotropic turbulence correctly. The model is also tested in plane wall turbulence, i.e. plane Couette flow and channel flow, and the major statistical properties are in better agreement with those predicted by DNS results than the predictions by the Smagorinsky, the dynamic Smagorinsky and the recent Cui–Zhou–Zhang–Shao models.
Turbulence Modeling
Large-Eddy Simulation
Cite
Citations (28)
An overview of the second-order closure turbulence models is presented in this paper. Models studied include the k-ε-eddy viscosity model, k-ε-nonlinear Reynolds stress model, differential Reynolds stress model, k-ε algebraic stress model, near-wall second-order closure model, low-Reynolds number model, two-layer model, and multiscale model, which cover the efforts of scientists and engineers over the past 50 years. However, at the present time, there exists no unified turbulence model. Each model applies successfully to some turbulent flows, while it predicts unsatisfactory results for other flows, especially for flows that are very different from those for which the models were calibrated. To improve the prediction accuracy and the applicability of the existing turbulence models, modifying, or even remodeling, the ε equation, the dissipation rate of turbulent kinetic energy, and pressure-strain terms of the Reynolds stress, uiuj¯, equations is necessary.
Reynolds stress
Turbulence Modeling
Closure (psychology)
Reynolds decomposition
Cite
Citations (34)
Turbulence Modeling
Reynolds stress
Closure (psychology)
Magnetic Reynolds number
Reynolds decomposition
Cite
Citations (0)