Using Particle Image Velocimetry to Determine Turbulence Model Parameters

Miller, Nathan E.; Beresh, Steven J.

doi:10.2514/1.j059741

Cited by 3 publications

(2 citation statements)

References 39 publications

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“…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%

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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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Section: Previous Piv Experimentsmentioning

confidence: 99%

“…The details of this approach are lengthy and provided in Miller and Beresh [23], but they are briefly described here. The stereoscopic planar PIV data provided all six unique components of the Reynolds stress tensor and thus all components of the anisotropy tensor (right side of Eq.…”

Section: Variable C µ Rans Modelmentioning

confidence: 99%