Modeling boundary layer flows over rough surfaces using a modified Chien k-epsilon turbulence model

1996 ◽  
Author(s):  
J. Lee
Keyword(s):  
Boundary Layer ◽  
Turbulence Model ◽  
Rough Surfaces ◽  
Boundary Layer Flows

Modeling the Space-Time Correlations in the Wake Region of a Turbulent Boundary Layer

2000 ◽  
Author(s):  
Joseph R. Gavin ◽  
Gerald C. Lauchle
Keyword(s):  
Boundary Layer ◽  
Turbulence Model ◽  
Small Scale ◽  
Boundary Layer Flows ◽  
Correlation Field ◽  
Time Correlations ◽  
Point Correlation ◽  
Local Mean ◽  
High Reynolds ◽  
General Studies

Abstract An empirical turbulence model has been developed for boundary layer flows. The goal is to simulate the statistical behavior of turbulent velocity fluctuations in both space and time using the two-point correlation field. The new model is based on physical concepts from earlier measurements and flow visualizations. In particular, it is useful to think of packets of turbulent fluid that are angled to towards the wall and convect with a velocity similar to the local mean. However, the actual behaviors which exist are complicated and sometimes quite subtle (but physically important). For instance, measurements show that the correlation field is only strongly peaked at zero time delay. This is interpreted in wavenumber space as a rapid decorrelation of the small scale eddies, and is modeled in a way that captures the transition. The new turbulence model has been calibrated using recent measurements and is now available for general studies. Efforts are underway to refine the model, improve its theoretical basis, and confirm its application to high Reynolds number flows.

An Improved K-ω Model for High Speed Flows

2012 ◽  
Vol 629 ◽  
pp. 593-600
Author(s):  
Jing Yuan Liu ◽  
Zeng Hui Zhao
Keyword(s):  
Boundary Layer ◽  
Turbulence Model ◽  
High Speed ◽  
Correction Term ◽  
Plate Boundary ◽  
Experimental Results ◽  
Tvd Scheme ◽  
Boundary Layer Flows ◽  
Calculation Results ◽  
Improved Model

An improved K-ω model, which allows for compressible corrections, is proposed in this paper. Numerical scheme was established utilizing the improved Total Variation Diminishing (TVD) scheme and applying implicit scheme to the negative source terms of the turbulence model. Hypersonic flat-plate boundary-layer flows and hypersonic compression ramp flows marked with separation, reattachment and shock/boundary layer interactions are then computed. Comparisons between the computational results, the experimental results and the semi-empirical formulations show that the compressible correction term of the K-ω turbulence model is a pressure-dilatation correlation. In addition, for flow with separation and without separation, calculation results of wall pressures, friction coefficients and wall heat transfer rate distributions using the improved model and established scheme agree better with the experimental results than that using the original K-ω model.

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.

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.

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