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Calculation of oscillating boundary layers with the q-ζ turbulence model
... For further investigation and extension of the previous study, three models were selected. The employed models are the low Reynolds number k-model by Launder and Sharma (LS) (Wilcox, 2006), the k-ω-SST model in low Reynolds number mode by Menter et al. (2003)(MKW) and the q-ζ model with low Reynolds number damping functions by Dafa'Alla et al. (1996)(GD) . ...
This paper provides temperature and velocity distribution computations in heated annuli using RANS approach and employing three different turbulent viscosity models. In addition to comparison calculations an extensive sensitivity study was performed. The results show that the RANS approach and the turbulent viscosity models can be used for prediction of forced convection heat transfer to lead–bismuth-eutectic. However, the turbulent Prandtl number has to be carefully selected depending on the respective turbulence model.
... However, this requires a considerably higher near-wall resolution, and even with an adaptive procedure, the necessary number of cells is substantially higher than for the wall-function treatment. A potential for better near-wall accuracy can possibly be found in alternative formulations of the two-equation model (e.g., the q^z model [29,30]). ...
The érst two parts of this article (1, 2) describe the components of an automatic resolution control (ARC) algorithm for the é nite-volume method. While the performance of similar algorithms is well established for inviscid supersonic è ows and partly for laminar èows, the number of studies dealing with incompressible turbulent èows is very limited. In this article we shall apply the ARC to two turbulent è ow test cases using the k-e turbulence model in 2- and 3-D to examine the performance of the method. For completeness, both the high-Re model with wall functions and the low-Re variant will be included.
Competitive pressures and economic constraints are driving aircraft manufacturers towards an ever-increasing exploitation of CFD for design, optimisation and prediction of off-design conditions. Such exploitation is favoured by rapid advances in meshing technology, numerical algorithms, visualisation tools and computer hardware. In contrast, the predictive capabilities of mathematical models of turbulence are limited — indeed, are often poor in regions of complex strain — and improve only slowly. The intuitive nature of turbulence modelling, its strong reliance on calibration and validation and the extreme sensitivity of model performance to seemingly minor variations in modelling details and flow conditions all conspire to make turbulence modelling an especially challenging component of CFD, but one that is crucially important for the correct prediction of complex flows.
The turbulent boundary layer driven by a freestream velocity that varies sinusoidally in time around a zero mean is considered. The flow has a rich behavior including strong pressure gradients, inflection points, and reversal. A theory for the velocity and stress profiles at high Reynolds number is formulated. Well-resolved direct Navier-Stokes simulations are conducted over a narrow range of Reynolds numbers, and the results are compared with the theoretical predictions. The flow is also computed over a wide range of Reynolds numbers using a new algebraic turbulence model; the results are compared with the direct simulations and the theory.