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Calibration of a Near-Wall Differential Reynolds Stress Model Using the Updated Direct Numerical Simulation Data and Its Assessment
by
Sabelnikov, Vladimir
, Anisimov, Kirill
, Troshin, Alexei
, Usov, Lev
in
Anisotropy
/ Approximation
/ boundary layer
/ Calibration
/ channel flow
/ differential Reynolds stress model
/ LES
/ near-wall turbulence
/ Numerical analysis
/ RANS
/ Reynolds number
/ Simulation
/ Simulation methods
/ Turbulence models
/ Velocity
/ Viscosity
2023
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Calibration of a Near-Wall Differential Reynolds Stress Model Using the Updated Direct Numerical Simulation Data and Its Assessment
by
Sabelnikov, Vladimir
, Anisimov, Kirill
, Troshin, Alexei
, Usov, Lev
in
Anisotropy
/ Approximation
/ boundary layer
/ Calibration
/ channel flow
/ differential Reynolds stress model
/ LES
/ near-wall turbulence
/ Numerical analysis
/ RANS
/ Reynolds number
/ Simulation
/ Simulation methods
/ Turbulence models
/ Velocity
/ Viscosity
2023
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Calibration of a Near-Wall Differential Reynolds Stress Model Using the Updated Direct Numerical Simulation Data and Its Assessment
by
Sabelnikov, Vladimir
, Anisimov, Kirill
, Troshin, Alexei
, Usov, Lev
in
Anisotropy
/ Approximation
/ boundary layer
/ Calibration
/ channel flow
/ differential Reynolds stress model
/ LES
/ near-wall turbulence
/ Numerical analysis
/ RANS
/ Reynolds number
/ Simulation
/ Simulation methods
/ Turbulence models
/ Velocity
/ Viscosity
2023
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Calibration of a Near-Wall Differential Reynolds Stress Model Using the Updated Direct Numerical Simulation Data and Its Assessment
Journal Article
Calibration of a Near-Wall Differential Reynolds Stress Model Using the Updated Direct Numerical Simulation Data and Its Assessment
2023
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Overview
In the article, a differential Reynolds stress model is recalibrated using turbulent channel flow direct numerical simulation data in the range of friction Reynolds numbers 550–5200. The calibration aims to produce a RANS sublayer model for use within the hybrid RANS/LES framework. The model is designed to capture the average field of a thin near-wall part of a boundary layer as accurately as possible. An a posteriori procedure is employed in which one-dimensional channel flow calculations are performed for all variations of the model coefficients at each stage of the optimization procedure. The coefficients are initialized with their original values and then optimized by minimizing the appropriately chosen norm. An improved representation of the mean velocity profile and peak Reynolds stress values is demonstrated. Both models—baseline and recalibrated—are implemented in an in-house CFD code, and several simulations, including a channel flow, a flat plate boundary layer and a boundary layer separation from a rounded step, are performed. The latter benchmark flow is also simulated in hybrid RANS/LES mode. The updated model is compared to the original one, demonstrating improvements over the baseline model in the cases it was designed for.
Publisher
MDPI AG
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