Bulletin of the American Physical Society
74th Annual Meeting of the APS Division of Fluid Dynamics
Volume 66, Number 17
Sunday–Tuesday, November 21–23, 2021; Phoenix Convention Center, Phoenix, Arizona
Session F11: Turbulence: Modeling & Simulations III: LES and Stochastic Models
5:25 PM–6:43 PM,
Sunday, November 21, 2021
Room: North 125 AB
Chair: David Lignell, Brigham Young University
Abstract: F11.00006 : Two-Level Simulation of Transition to Turbulence in Wall-Bounded Flows*
6:30 PM–6:43 PM
Presenter:
Mickael Young
(University of Tennessee at Chattanooga)
Authors:
Mickael Young
(University of Tennessee at Chattanooga)
Reetesh Ranjan
(University of Tennessee at Chattanooga)
The two-level simulation (TLS) model is a multi-scale modeling strategy, which was originally developed for the simulation of high Reynolds number (Re) turbulent flows. Compared to the large-eddy simulation (LES) strategy, where the effects of the unresolved small-scales of motion on the resolved large-scales of motion are modeled, in the TLS model, both large- and small-scales are explicitly solved. Furthermore, the TLS model does not employ the notion of spatial filtering and eddy viscosity, and therefore, it does not suffer from the limitations associated with the use of these concepts. Past studies have shown unique physics-based modeling capabilities of the TLS model for simulation of high Re fully developed turbulent flows, and recently, its ability to capture features of laminar-to-turbulent transition within the three-dimensional Taylor-Green vortex flow has also been demonstrated. The present study focuses on further evaluation of the TLS model by employing it as a near-wall model while performing LES of transitional wall-bounded flows. The results from the simulation of transition within the periodic channel flow at Re = 8000 are examined to understand the behavior of the large- and small-scale dynamics of the flow field during the temporal transition process.
*Funding: Center of Excellence in Applied Computational Science and Engineering, University of Tennessee at ChattanoogaComputational Resources: NSF Award #1925603
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