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Research Papers

Uncertainty Analysis and Data-Driven Model Advances for a Jet-in-Crossflow

[+] Author and Article Information
Julia Ling

Thermal/Fluid Science and Engineering,
Sandia National Laboratories,
Livermore, CA 94551
e-mail: jling@sandia.gov

Anthony Ruiz, Guilhem Lacaze, Joseph Oefelein

Reacting Flow Research,
Sandia National Laboratories,
Livermore, CA 94551

1Corresponding author.

2Present address: Laboratoires Industriels Pichot, Le Brugeron 63880, France.

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received July 27, 2016; final manuscript received August 4, 2016; published online October 4, 2016. Editor: Kenneth Hall.

J. Turbomach 139(2), 021008 (Oct 04, 2016) (9 pages) Paper No: TURBO-16-1175; doi: 10.1115/1.4034556 History: Received July 27, 2016; Revised August 04, 2016

For film cooling of combustor linings and turbine blades, it is critical to be able to accurately model jets-in-crossflow. Current Reynolds-averaged Navier–Stokes (RANS) models often give unsatisfactory predictions in these flows, due in large part to model form error, which cannot be resolved through calibration or tuning of model coefficients. The Boussinesq hypothesis, upon which most two-equation RANS models rely, posits the existence of a non-negative scalar eddy viscosity, which gives a linear relation between the Reynolds stresses and the mean strain rate. This model is rigorously analyzed in the context of a jet-in-crossflow using the high-fidelity large eddy simulation data of Ruiz et al. (2015, “Flow Topologies and Turbulence Scales in a Jet-in-Cross-Flow,” Phys. Fluids, 27(4), p. 045101), as well as RANS k–ϵ results for the same flow. It is shown that the RANS models fail to accurately represent the Reynolds stress anisotropy in the injection hole, along the wall, and on the lee side of the jet. Machine learning methods are developed to provide improved predictions of the Reynolds stress anisotropy in this flow.

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References

Figures

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Fig. 1

Schematic of LES flow configuration showing instantaneous isosurfaces of the Q-criterion

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Fig. 2

Time-averaged profiles of the velocity magnitude in the jet center plane (z/d = 0). Reference data labeled “Exp” were extracted from the experiments of Su and Mungal [21].

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Fig. 3

Zoomed-in view of near-injection region of mesh used for RANS simulations

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Fig. 4

Contours of x-velocity ((a) and (b)) and turbulent kinetic energy ((c) and (d)) as predicted by LES ((a) and (c)) and RANS ((b) and (d)). Isocontour lines super-imposed in gray. The velocity and turbulent kinetic energy have been nondimensionalized with the mean free stream velocity U. Contours are shown in a spanwise plane at z = 0.25d: (a) x-velocity, LES, (b) x-velocity, RANS, (c) k, LES, and (d) k, RANS.

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Fig. 5

Contours of eddy viscosity, nondimensionalized by U and d. Regions of negative eddy viscosity are outlined with solid lines.

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Fig. 6

Schematic of the barycentric map

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Fig. 7

Scatter plot of Reynolds stress anisotropy predictions on a barycentric map for (a) LES, (b) RANS, and (c) random forest. Points shaded by x-coordinate are normalized by hole diameter d.

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Fig. 8

Contours of the second anisotropy invariant IIa from (a) LES, (b) RANS, and (c) random forest predictions in a plane at z = 0.25d

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Fig. 9

Error in random forest predictions of xB for varying random forest ensemble sizes

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