A double-averaged Navier-Stokes k – ω turbulence model for wall flows over rough surfaces with heat transfer

2021 ◽  
pp. 1-22
Author(s):  
F. Chedevergne
Keyword(s):  
Heat Transfer ◽  
Turbulence Model ◽  
Rough Surfaces ◽  
Navier Stokes ◽  
Wall Flows

Computational Fluid Dynamics Modeling of Mixed Convection Flows in Building Enclosures

2013 ◽  
Author(s):  
Alexander Kayne ◽  
Ramesh Agarwal
Keyword(s):  
Heat Transfer ◽  
Fluid Dynamics ◽  
Experimental Data ◽  
Computational Fluid Dynamics ◽  
Mixed Convection ◽  
Turbulence Model ◽  
Numerical Algorithm ◽  
Navier Stokes ◽  
Cfd Simulations ◽  
Flow And Heat Transfer

In recent years Computational Fluid Dynamics (CFD) simulations are increasingly used to model the air circulation and temperature environment inside the rooms of residential and office buildings to gain insight into the relative energy consumptions of various HVAC systems for cooling/heating for climate control and thermal comfort. This requires accurate simulation of turbulent flow and heat transfer for various types of ventilation systems using the Reynolds-Averaged Navier-Stokes (RANS) equations of fluid dynamics. Large Eddy Simulation (LES) or Direct Numerical Simulation (DNS) of Navier-Stokes equations is computationally intensive and expensive for simulations of this kind. As a result, vast majority of CFD simulations employ RANS equations in conjunction with a turbulence model. In order to assess the modeling requirements (mesh, numerical algorithm, turbulence model etc.) for accurate simulations, it is critical to validate the calculations against the experimental data. For this purpose, we use three well known benchmark validation cases, one for natural convection in 2D closed vertical cavity, second for forced convection in a 2D rectangular cavity and the third for mixed convection in a 2D square cavity. The simulations are performed on a number of meshes of different density using a number of turbulence models. It is found that k-epsilon two-equation turbulence model with a second-order algorithm on a reasonable mesh gives the best results. This information is then used to determine the modeling requirements (mesh, numerical algorithm, turbulence model etc.) for flows in 3D enclosures with different ventilation systems. In particular two cases are considered for which the experimental data is available. These cases are (1) air flow and heat transfer in a naturally ventilated room and (2) airflow and temperature distribution in an atrium. Good agreement with the experimental data and computations of other investigators is obtained.

Investigation of MHD RANS Modeling Base on DNS Database Under the Advanced Blanket Design Conditions Utilized Molten Salt

2014 ◽  
Author(s):  
Naoki Osawa ◽  
Yoshinobu Yamamoto ◽  
Tomoaki Kunugi
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Turbulence Model ◽  
Turbulent Channel Flow ◽  
Turbulent Heat Transfer ◽  
Navier Stokes ◽  
Turbulent Heat ◽  
Mhd Turbulence ◽  
Rans Modeling ◽  
Heat Transfer Degradation

In this study, validations of Reynolds Averaged Navier-Stokes Simulation (RANS) based on Kenjeres & Hanjalic MHD turbulence model (Int. J. Heat & Fluid Flow, 21, 2000) coupled with the low-Reynolds number k-epsilon model have been conducted with the usage of Direct Numerical Simulation (DNS) database. DNS database of turbulent channel flow imposed wall-normal magnetic field on, are established in condition of bulk Reynolds number 40000, Hartmann number 24, and Prandtl number 5. As the results, the Nagano & Shimada model (Trans. JSME series B. 59, 1993) coupled with Kenjeres & Hanjalic MHD turbulence model has the better availability compared with Myong & Kasagi model (Int. Fluid Eng, 109, 1990) in estimation of the heat transfer degradation in MHD turbulent heat transfer.

Effects of 3-D Local Unsteadiness on Heat Transfer Prediction in Turbulated Passages

2003 ◽  
Author(s):  
Pamela A. McDowell ◽  
William D. York ◽  
D. Keith Walters ◽  
James H. Leylek
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Turbulence Model ◽  
Three Dimensional ◽  
Reynolds Numbers ◽  
Navier Stokes ◽  
Successful Outcome ◽  
Small Scale ◽  
Cross Sectional ◽  
Fully Implicit

A newly developed unsteady turbulence model was used to predict heat transfer in a turbulated passage typical of turbine airfoil cooling applications. Comparison of fullyconverged computational solutions to experimental measurements reveal that accurate prediction of heat transfer coefficient requires the effects of local small-scale unsteadiness to be captured. Validation was accomplished through comparison of the time- and area-averaged Nusselt number on the passage wall between adjacent ribs with experimental data from the open literature. The straight channel had a square cross-sectional area with multiple rows of staggered and rounded-edge ribs on opposite walls that were orthogonal to the flow. Simulations were run for Reynolds numbers of 5500, 16500, and 25000. Computational solutions were obtained on a multi-block, multi-topology, unstructured, and adaptive grid, using a pressure-correction based, fully-implicit Navier-Stokes solver. The computational results include two-dimensional (2-D) and three-dimensional (3-D) steady and unsteady simulations with viscous sublayers resolved (y+ ≤ 1) on all the walls in every case. Turbulence closure was obtained using a new turbulence model developed in-house for the unsteady simulations, and a realizable k-ε turbulence model was used for the steady simulations. The results obtained from the unsteady simulations show greatly improved agreement with the experimental data, especially at realistically high Reynolds numbers. The key 3-D physics mechanisms responsible for the successful outcome include: (1) shear layer roll-up over the turbulators; (2) recirculation zones both upstream and downstream of the rib faces; and (3) reattachment regions between each rib pair. Results from the unsteady case are superior to those of the steady because they capture the aforementioned mechanisms, and therefore more accurately predict the heat transfer.

Unsteady Heat Transfer in Stator–Rotor Interaction by Two-Equation Turbulence Model

1999 ◽  
Vol 121 (3) ◽  
pp. 436-447 ◽  
Author(s):  
V. Michelassi ◽  
F. Martelli ◽  
R. De´nos ◽  
T. Arts ◽  
C. H. Sieverding
Keyword(s):  
Heat Transfer ◽  
Turbulence Model ◽  
Rotor Blade ◽  
Two Dimensions ◽  
Operating Conditions ◽  
Navier Stokes ◽  
Turbine Stage ◽  
Pressure Losses ◽  
Transonic Turbine ◽  
Variable Operating Conditions

A transonic turbine stage is computed by means of an unsteady Navier–Stokes solver. A two-equation turbulence model is coupled to a transition model based on integral parameters and an extra transport equation. The transonic stage is modeled in two dimensions with a variable span height for the rotor row. The analysis of the transonic turbine stage with stator trailing edge coolant ejection is carried out to compute the unsteady pressure and heat transfer distribution on the rotor blade under variable operating conditions. The stator coolant ejection allows the total pressure losses to be reduced, although no significant effects on the rotor heat transfer are found both in the computer simulation and the measurements. The results compare favorably with experiments in terms of both pressure distribution and heat transfer around the rotor blade.

A Comparison of Approximate Versus Exact Geometrical Representations of Roughness for CFD Calculations of cf and St

2008 ◽  
Vol 130 (2) ◽  
Author(s):  
J. P. Bons ◽  
S. T. McClain ◽  
Z. J. Wang ◽  
X. Chi ◽  
T. I. Shih
Keyword(s):  
Heat Transfer ◽  
Boundary Layer ◽  
Rough Surface ◽  
Turbulence Model ◽  
Rough Surfaces ◽  
Element Model ◽  
Discrete Element ◽  
Discrete Element Model ◽  
Flat Part ◽  
Roughness Elements

Skin friction (cf) and heat transfer (St) predictions were made for a turbulent boundary layer over randomly rough surfaces at Reynolds number of 1×106. The rough surfaces are scaled models of actual gas turbine blade surfaces that have experienced degradation after service. Two different approximations are used to characterize the roughness in the computational model: the discrete element model and full 3D discretization of the surface. The discrete element method considers the total aerodynamic drag on a rough surface to be the sum of shear drag on the flat part of the surface and the form drag on the individual roughness elements. The total heat transfer from a rough surface is the sum of convection on the flat part of the surface and the convection from each of the roughness elements. Correlations are used to model the roughness element drag and heat transfer, thus avoiding the complexity of gridding the irregular rough surface. The discrete element roughness representation was incorporated into a two-dimensional, finite difference boundary layer code with a mixing length turbulence model. The second prediction method employs a viscous adaptive Cartesian grid approach to fully resolve the three-dimensional roughness geometry. This significantly reduces the grid requirement compared to a structured grid. The flow prediction is made using a finite-volume Navier-Stokes solver capable of handling arbitrary grids with the Spalart-Allmaras (S‐A) turbulence model. Comparisons are made to experimentally measured values of cf and St for two unique roughness characterizations. The two methods predict cf to within ±8% and St within ±17%, the RANS code yielding slightly better agreement. In both cases, agreement with the experimental data is less favorable for the surface with larger roughness features. The RANS simulation requires a two to three order of magnitude increase in computational time compared to the DEM method and is not as readily adapted to a wide variety of roughness characterizations. The RANS simulation is capable of analyzing surfaces composed primarily of roughness valleys (rather than peaks), a feature that DEM does not have in its present formulation. Several basic assumptions employed by the discrete element model are evaluated using the 3D RANS flow predictions, namely: establishment of the midheight for application of the smooth wall boundary condition; cD and Nu relations employed for roughness elements; and flow three dimensionality over and around roughness elements.

Numerical Comparisons of Heat Transfer From a Single Jet Emanating From a Slot Nozzle Impinging on an Isothermal Plate

2019 ◽  
Author(s):  
Cristian Tibabisco ◽  
Salvador Vargas-Díaz ◽  
Samir A. Salamah
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Stagnation Point ◽  
Turbulence Model ◽  
Turbulence Models ◽  
Thermal Design ◽  
Navier Stokes ◽  
Reynolds Average Navier Stokes ◽  
Single Jet ◽  
Reynolds Average

Abstract Impingement jets are used in different cooling applications where it is required to remove large amounts of heat. Heat transfer in the stagnation point for a single jet impinging on an isothermal plate is investigated with four turbulence models. Two models are RANS (Reynolds Average Navier-Stokes): Transition SST and Transition κ–κl–ω. The other two models are URANS (Unsteady Reynolds Average Navier-Stokes): SAS and DES-SST. This paper explores the best turbulence model for thermal design and cooling purposes. Results are validated with experimental data reported by Gardon & Akfirat. These four turbulence models are available in the commercial CFD software package ANSYS FLUENT 18.1. Special attention is paid to the heat transfer in the impingement region through evaluation of Nusselt number in the stagnation point. Different dimensionless nozzle-to-plate distances are considered in this work (z/b = 14 to z/b = 40), and two different Reynolds numbers are used Re = 11,000 y Re = 22,000. Three turbulence models are within reasonable accuracy (10%) of the experimental data, but some turbulence models have problems with convergence and grid independence, especially the URANS models. Based on these results, the best turbulence model for applications in heating and cooling systems where impingement heat transfer is critical is the Transition κ–κl–ω.

scholarly journals Navier-Stokes Predictions of Transition, Loss and Heat Transfer in a Turbine Cascade

1987 ◽  
Author(s):  
N. T. Birch
Keyword(s):  
Heat Transfer ◽  
Turbulence Model ◽  
Leading Edge ◽  
Solution Procedure ◽  
Navier Stokes ◽  
Turbine Cascade ◽  
Suction Surface ◽  
Pressure Surface ◽  
Transitional Boundary Layer ◽  
Data Correlations

The loss and heat transfer of a turbine cascade are strongly influenced by the location and extent of the transitional boundary layer. In this paper, two approaches are adopted to predict the onset and extent of transition within a 2-D explicit Navier-Stokes solution procedure. In the first, transition is predicted by coupling transition data correlations with an algebraic turbulence model. In the second, a low Reynolds Number one-equation turbulence model is used. Comparison is made with the turbine cascade data of Nicholson et al. (1982). This indicates that the first model gives good predictions of suction surface behaviour but poor predictions on the pressure surface. The model is also difficult to apply in a N-S method. The second model gives good predictions of pressure surface behaviour but consistently predicts transition near the leading edge on the suction surface. The latter is attributed to a Mach Number over-speed and leading edge effects.

A Comparison of Approximate vs. Exact Geometrical Representations of Roughness for CFD Calculations of CF and ST

2005 ◽  
Author(s):  
J. P. Bons ◽  
S. T. McClain ◽  
Z. J. Wang ◽  
X. Chi ◽  
T. I. Shih
Keyword(s):  
Heat Transfer ◽  
Boundary Layer ◽  
Rough Surface ◽  
Turbulence Model ◽  
Rough Surfaces ◽  
Element Model ◽  
Discrete Element ◽  
Discrete Element Model ◽  
Flat Part ◽  
Roughness Elements

Skin friction (cf) and heat transfer (St) predictions were made for a turbulent boundary layer over randomly rough surfaces at Reynolds number of 1 × 106. The rough surfaces are scaled models of actual gas turbine blade surfaces that have experienced degradation after service. Two different approximations are used to characterize the roughness in the computational model: the discrete element model and full 3-D discretization of the surface. The discrete element method considers the total aerodynamic drag on a rough surface to be the sum of shear drag on the flat part of the surface and the form drag on the individual roughness elements. The total heat transfer from a rough surface is the sum of convection on the flat part of the surface and the convection from each of the roughness elements. Correlations are used to model the roughness element drag and heat transfer thus avoiding the complexity of gridding the irregular rough surface. The discrete element roughness representation was incorporated into a two-dimensional, finite difference boundary layer code with a mixing length turbulence model. The second prediction method employs a viscous adaptive Cartesian grid approach to fully resolve the three-dimensional roughness geometry. This significantly reduces the grid requirement compared to a structured grid. The flow prediction is made using a finite-volume Navier-Stokes solver capable of handling arbitrary grids with the Spalart-Allmaras (S-A) turbulence model. Comparisons are made to experimentally measured values of cf and St for two unique roughness characterizations. The two methods predict cf to within ±8% and St within ±17%, the RANS code yielding slightly better agreement. In both cases, agreement with the experimental data is less favorable for the surface with larger roughness features. The RANS simulation requires a two to three order of magnitude increase in computational time compared to the DEM method and is not as readily adapted to a wide variety of roughness characterizations. The RANS simulation is capable of analyzing surfaces composed primarily of roughness valleys (rather than peaks), a feature that DEM does not have in its present formulation. Several basic assumptions employed by the discrete element model are evaluated using the 3D RANS flow predictions, namely: establishment of the mid-height for application of the smooth wall boundary condition, cD and Nu relations employed for roughness elements, and flow three-dimensionality over and around roughness elements.

Application of the v2-f Turbulence Model to Turbomachinery Configurations

2007 ◽  
Author(s):  
B. Tartinville ◽  
E. Lorrain ◽  
Ch. Hirsch
Keyword(s):  
Heat Transfer ◽  
Turbulence Model ◽  
Turbulence Models ◽  
Critical Issue ◽  
Secondary Flows ◽  
Navier Stokes ◽  
Test Cases ◽  
Laminar To Turbulent Transition ◽  
Turbulent Transition ◽  
Flow Features

Enhancing the representation of turbulence processes is a critical issue for CFD codes devoted to turbomachinery industry. Though more complex methods are available, Reynolds-Averaged Navier-Stokes models are widely used in the daily design process. Therefore, there is a constant need for improving eddy-viscosity based turbulence models. The present work aims at investigating the capabilities of the v2-f turbulence model for turbomachinery applications. Three types of flow features that are of importance for such applications are investigated in details: heat transfer, secondary flows, and laminar to turbulent transition. From a series of test cases, it appears that the v2-f turbulence model is specially adapted for heat transfer applications and shows potentialities in predicting laminar to turbulent transition.

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