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Research Papers

Large-Eddy Simulation and Conjugate Heat Transfer Around a Low-Mach Turbine Blade

[+] Author and Article Information
Florent Duchaine

CERFACS,
42 Avenue G. Coriolis,
Toulouse 31057,France
e-mail: florent.duchaine@cerfacs.fr

Nicolas Maheu

e-mail: nicolas.maheu@coria.fr

Vincent Moureau

e-mail: vincent.moureau@coria.fr
CORIA, CNRS UMR6614,
Universitée et INSA de Rouen,
Saint-Etienne du Rouvray 76801,France

Guillaume Balarac

LEGI, CNRS UMR5519,
Université Joseph Fourier,
et Institut National Polytechnique de Grenoble,
Grenoble 38041,France
e-mail: guillaume.balarac@grenoble-inp.fr

Stéphane Moreau

Mechanical Engineering,
Université de Sherbrooke,
2500 Boulevard de l'Université,
Sherbrooke, QC J1K2R1, Canada
e-mail: stephane.smoreau@gmail.com

1Corresponding author.

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received June 7, 2013; final manuscript received July 8, 2013; published online October 23, 2013. Editor: Ronald Bunker.

J. Turbomach 136(5), 051015 (Oct 23, 2013) (11 pages) Paper No: TURBO-13-1092; doi: 10.1115/1.4025165 History: Received June 07, 2013; Revised July 08, 2013

Determination of heat loads is a key issue in the design of gas turbines. In order to optimize the cooling, an exact knowledge of the heat flux and temperature distributions on the airfoils surface is necessary. Heat transfer is influenced by various factors, like pressure distribution, wakes, surface curvature, secondary flow effects, surface roughness, free stream turbulence, and separation. Each of these phenomenons is a challenge for numerical simulations. Among numerical methods, large eddy simulations (LES) offers new design paths to diminish development costs of turbines through important reductions of the number of experimental tests. In this study, LES is coupled with a thermal solver in order to investigate the flow field and heat transfer around a highly loaded low pressure water-cooled turbine vane at moderate Reynolds number (150,000). The meshing strategy (hybrid grid with layers of prisms at the wall and tetrahedra elsewhere) combined with a high fidelity LES solver gives accurate predictions of the wall heat transfer coefficient for isothermal computations. Mesh convergence underlines the known result that wall-resolved LES requires discretizations for which y+ is of the order of one. The analysis of the flow field gives a comprehensive view of the main flow features responsible for heat transfer, mainly the separation bubble on the suction side that triggers transition to a turbulent boundary layer and the massive separation region on the pressure side. Conjugate heat transfer computation gives access to the temperature distribution in the blade, which is in good agreement with experimental measurements. Finally, given the uncertainty on the coolant water temperature provided by experimentalists, uncertainty quantification allows apprehension of the effect of this parameter on the temperature distribution.

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References

Figures

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Fig. 1

(a) Test section—blades with pressure taps #2 and #4, blade with thermocouples #3, (b) geometry of the cooled blade #3

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Fig. 2

(a) Sketch of the fluid computational domain and (b) detail of the corresponding unstructured mesh grid

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Fig. 3

Main flow features responsible of heat transfer characteristics: velocity field (up) and isosurface of Q-criterion (bottom). The simulation is done with mesh M4.

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Fig. 4

Mean temporal pressure distribution along the blade profile. The simulation is done with mesh M4.

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Fig. 5

(a) Y+ and (b) wall friction τw distributions along the blade profile for the four meshes

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Fig. 6

(a) Convective heat transfer coefficient h distribution along the blade profile for the four meshes and (b) evolutions of boundary layer thicknesses along the suction side of the blade

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Fig. 7

(a) Temperature distribution around the blade obtained by CHT and (b) comparison of convective heat transfer coefficient obtained with an isothermal computation (IsoT) and the coupled simulation (CPL)

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Fig. 8

Mean and 95% confident interval of the temperature distribution around the blade with respect to uncertainty in conductivity with a mean values of (a) λ¯s=7 W·m-1·K-1 and (b) λ¯s=6.5 W·m-1·K-1

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Fig. 9

Spatial distribution of RMS temperature in the blade with respect to uncertainty in convective conditions in (top (a)) hole #1, (top (b)) hole #4 and mean and 95% confident interval of the temperature distribution around the blade with respect to uncertainty in convective conditions in (bottom (a)) hole #1, (bottom (b)) hole #4

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Fig. 10

RMS temperature profiles around the blade obtained by UQ simulations done on separated convective temperature in holes compared to the full UQ in all holes (a) and mean and 95% confident interval of the temperature distribution around the blade with respect to uncertainty in convective conditions in all the holes (b)

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