Abstract
A new Partially Averaged Navier-Stokes (PANS) bridging model is derived from existing (k−ω) and (k−ε) PANS formulations. The model behaves like the PANS (k−ω) model near rigid walls and like the PANS (k−ε) model away from walls. The new model is tested using well-known benchmark problems; a backward-facing step representing wall-bounded flows, and a circular cylinder representing free shear flows. Our results are compared to existing experimental data and previous simulation results using PANS (k−ω) and PANS (k−ε). The comparisons show our model to be superior at predicting velocity profiles in both flows. In addition, Reynolds stress predictions are also shown to improve.
Highlights
The desire for inexpensive unsteady simulations of turbulent flows has led to the development of numerous modeling methods which aim to give accurate solutions at a reasonable computational cost.One of the earliest methods, the Unsteady Reynolds Averaged Navier-Stokes (URANS) or Very LargeEddy Simulation (VLES), is inexpensive but unable to resolve the random fluctuations associated with turbulent flows [1]
We propose a new Partially Averaged Navier-Stokes (PANS) model based on the (k − ω) and (k − ε) versions of PANS
A backward facing step is used for the wall-bounded flow and a circular cylinder is used for the free
Summary
The desire for inexpensive unsteady simulations of turbulent flows has led to the development of numerous modeling methods which aim to give accurate solutions at a reasonable computational cost. In DNS, the full Navier-Stokes equations are solved with no simplifying assumptions This comes at a very high computational cost and is limited to low Reynolds number flows. The scale separation operation proposed by Kolmogorov [3] is the foundation for a new method called Large Eddy Simulation (LES). In LES the smallest scales of motion are modeled, while the large scales are fully resolved [5,6,7] This space filtering operation reduces the computational cost when compared to DNS. A new family of methods has emerged These methods known as Hybrid RANS/LES take advantage of the vast knowledge developed in RANS to bridge the gap in the wavenumber space with LES, the often-used name of Bridging models [8,9].
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