Practical Challenges in the Calculation of Turbulent Viscosity from PIV Data

Beresh, Steven J.; Miller, Nathan E.; Smith, Barbara

doi:10.2514/6.2018-2987

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…One option could be to retrieve

μ_{t}

using Equation () using the PIV data. Nevertheless, Beresh et al 27 reported the challenges in retrieving

μ_{t}

from PIV data due to high‐frequency noise as well as random and biased errors in computing the strain rate of the mean‐velocity field.…”

Section: Resultsmentioning

confidence: 99%

“…As a result, the separation point is shifted upstream, and the shear layer is thicker than that on a smooth surface. Recently, the study of Beresh et al 27 showed that particle‐image‐velocimetry (PIV) data could be used to infer

μ_{t}

and, in turn, understand the prediction of the RANS equations. Zhang et al 28 performed PIV measurements on an airfoil with LE roughness and concluded that the turbulent kinetic energy is increased in the outer flow.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the extension of k−ω−SST corrections to predict flow separation on thick airfoils with leading‐edge roughness

Gutierrez¹,

Zamponi

Ragni

et al. 2023

Wind Energy

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Modern wind turbines employ thick airfoils in the outer region of the blade with strong adverse pressure gradients and high sensitivity to flow separation, which can be anticipated by leading‐edge roughness. However, Reynolds average Navier‐Stokes simulations currently overpredict the Reynolds shear stresses near the surface, and the flow separation is not correctly predicted. Hence, these methods are not representative enough to optimize the blade design to avoid flow separation, which becomes relevant for rough blades. While several eddy‐viscosity corrections in the k−ω−SST turbulence model have been previously studied to predict flow separation over smooth airfoils, the present study aims to extend their applicability to airfoils with leading‐edge roughness. Two corrections, whose effect on flow physics has not been empirically quantified, are addressed. Particle image velocimetry measurements have been performed on a 30% thick airfoil to quantify the impact of these corrections. The reduction of the eddy viscosity introduced by the corrections leads to a shift of the peak location of the Reynolds shear stresses away from the surface, which, in turn, promotes flow separation and improves the prediction of the mean velocity and the pressure‐coefficient distribution. Besides, the ratio between the main turbulent shear stress and turbulent kinetic energy is demonstrated to be lower than the standard value used in the k−ω−SST turbulence model at the boundary‐layer outer edge. Adjusting this ratio for an angle of attack of 0° decreases the error on the predicted lift and drag coefficients from 75% to 3% and from 58% to 39%, respectively.

show abstract

“…One option could be to retrieve

μ_{t}

using Equation () using the PIV data. Nevertheless, Beresh et al 27 reported the challenges in retrieving

μ_{t}

from PIV data due to high‐frequency noise as well as random and biased errors in computing the strain rate of the mean‐velocity field.…”

Section: Resultsmentioning

confidence: 99%

μ_{t}

Section: Introductionmentioning

confidence: 99%

On the extension of k−ω−SST corrections to predict flow separation on thick airfoils with leading‐edge roughness

Gutierrez¹,

Zamponi

Ragni

et al. 2023

Wind Energy

View full text Add to dashboard Cite

show abstract

“…A total of 1500 vector fields were acquired for the x-y plane, while 9600 independent fields were used for the y-z plane. Optimal PIV image interrogation techniques were necessary to determine eddy viscosity and dissipation rates with sufficient precision, which are detailed in Beresh et al [22] and Miller and Beresh [23].…”

Section: Previous Piv Experimentsmentioning

confidence: 99%

Creating Data-Driven Turbulence Models Using PIV

Beresh

Miller

Parish

et al. 2022

LXLASER

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Data-driven turbulence models have been created for a k-ε RANS model based on PIV data in a jet in crossflow. In the simpler of two implementations, the nominal value of the model coefficient Cµ was replaced with an optimized value calibrated to the PIV results. Despite being based on only four flow cases of a canonical configuration, the optimized model demonstrated superior performance over 48 flow cases of increasing complexity. A second, more sophisticated data-driven model has been created by mapping a spatially variable Cµ to flow state variables using machine learning of experimentally measured flow field properties of the turbulence. This second model has been implemented in a production RANS code but requires further improvements before it can return results deviating from either the nominal or calibrated Cµ models.

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Using Particle Image Velocimetry to Determine Turbulence Model Parameters

Miller

Beresh

2021

AIAA Journal

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Practical Challenges in the Calculation of Turbulent Viscosity from PIV Data

Cited by 5 publications

References 23 publications

On the extension of k−ω−SST corrections to predict flow separation on thick airfoils with leading‐edge roughness

On the extension of k−ω−SST corrections to predict flow separation on thick airfoils with leading‐edge roughness

Creating Data-Driven Turbulence Models Using PIV

Using Particle Image Velocimetry to Determine Turbulence Model Parameters

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