On the potential of sensitized RANS approaches for unsteady turbulent wall-bounded flows
ORAL
Abstract
An improved Scale Adaptive Simulation (SAS) hybrid URANS/LES framework that leverages a sensitized RANS (i.e., k-ε-ξ-f) model to allow for departure-from-equilibrium dynamics is itroduced, primarily to enable the model to transition from URANS to scale resolving mode in attached/mildly separated flows, a known shortcoming of the classical SAS formulations.
Additionally, a transport equation for the length scale of energy-containing eddies is introduced that allows an appropriate transition of length scale from URANS to LES mode. The modified framework is shown to trigger a transition from URANS to LES in stable attached stationary turbulent channel flow and mitigate the log-layer mismatch problem to a great extent. When applied to flows that feature boundary layer separation, i.e., flow over a periodic hill and flow over the wall-mounted hump, the current hybrid framework delivers results in agreement with existing experimental data and comparable to high fidelity LES calculations.
Additionally, a transport equation for the length scale of energy-containing eddies is introduced that allows an appropriate transition of length scale from URANS to LES mode. The modified framework is shown to trigger a transition from URANS to LES in stable attached stationary turbulent channel flow and mitigate the log-layer mismatch problem to a great extent. When applied to flows that feature boundary layer separation, i.e., flow over a periodic hill and flow over the wall-mounted hump, the current hybrid framework delivers results in agreement with existing experimental data and comparable to high fidelity LES calculations.
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Publication: Saini, R., Brasseur, J. G., & Mehdizadeh, A. (2023). A foundational step towards understanding and improving the transition between URANS and LES in hybrid URANS-LES methodology. Computers & Fluids, 259, 105896.
Presenters
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AMIRFARHANG MEHDIZADEH
University of Missouri - Kansas City
Authors
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AMIRFARHANG MEHDIZADEH
University of Missouri - Kansas City
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Rohit Saini
University of Missouri Kansas City