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Research Papers

Investigation of the Accuracy of RANS Models to Predict the Flow Through a Low-Pressure Turbine

[+] Author and Article Information
R. Pichler

Department of Mechanical Engineering,
University of Melbourne,
Victoria 3010, Australia
e-mail: richard.pichler@unimelb.edu.au

R. D. Sandberg

Department of Mechanical Engineering,
University of Melbourne,
Victoria 3010, Australia

V. Michelassi

General Electric,
Florence 50127, Italy

R. Bhaskaran

General Electric,
Niskayuna, NY 12309

1Corresponding author.

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received March 28, 2016; final manuscript received April 26, 2016; published online June 22, 2016. Editor: Kenneth C. Hall.

J. Turbomach 138(12), 121009 (Jun 22, 2016) (12 pages) Paper No: TURBO-16-1076; doi: 10.1115/1.4033507 History: Received March 28, 2016; Revised April 26, 2016

In the present paper, direct numerical simulation (DNS) data of a low-pressure turbine (LPT) are investigated in light of turbulence modeling. Many compressible turbulence models use Favre-averaged transport equations of the conservative variables and turbulent kinetic energy (TKE) along with other modeling equations. First, a general discussion on the turbulence modeling error propagation prescribed by transport equations is presented, leading to the terms that are considered to be of interest for turbulence model improvement. In order to give turbulence modelers means of validating their models, the terms appearing in the Favre-averaged momentum equations are presented along pitchwise profiles at three axial positions. These three positions have been chosen such that they represent regions with different flow characteristics. General trends indicate that terms related with thermodynamic fluctuations and Favre fluctuations are small and can be neglected for most of the flow field. The largest errors arise close to the trailing edge (TE) region where vortex shedding occurs. Finally, linear models and the scope for their improvement are discussed in terms of a priori testing. Using locally optimized turbulence viscosities, the improvement potential of widely used models is shown. On the other hand, this study also highlights the danger of pure local optimization.

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References

Figures

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Fig. 1

Validation of wake losses [31]

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Fig. 2

Measurement position in the wake

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Fig. 3

Pressure coefficient about blade (a), wall-shear stress about blade (b), and wake loss at x = 1.26 (c)

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Fig. 4

Favre-averaged profiles of primitive variables at location x = 0.9: streamwise velocity (a), pitchwise velocity (b), temperature (c), and pressure (d)

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Fig. 5

Wake profile of turbulence modeling quantities at x = 0.9 for the transport equation of Favre-averaged momentum (a), RS (b), Favre-averaged total energy (c), and Favre-averaged TKE (d)

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Fig. 6

Favre-averaged profiles of primitive variables at location x = 1.1: streamwise velocity (a), pitchwise velocity (b), temperature (c), and pressure (d)

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Fig. 7

Wake profile of turbulence modeling quantities at x = 1.1 for the transport equation of Favre-averaged momentum (a), RS (b), Favre-averaged total energy (c), and Favre-averaged TKE (d)

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Fig. 8

Favre-averaged profiles of primitive variables at location x = 0.8: streamwise velocity (a), pitchwise velocity (b), temperature (c), and pressure (d)

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Fig. 9

Wake profile of turbulence modeling quantities at x = 0.8 for the transport equation of Favre-averaged momentum (a), RS (b), Favre-averaged total energy (c), and Favre-averaged TKE (d)

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Fig. 10

RS decomposition in isotropic and anisotropic part at x = 0.9

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Fig. 11

Anisotropic RS in wake-aligned coordinates (a), anisotropic strain in wake-aligned coordinates (b), and turbulence viscosities for the models investigated in this work (c) at a streamwise position of x = 0.9

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Fig. 12

Comparison of RS from DNS (a) with modeled stresses using a linear model with μt,kϵ (b), μt,opt (c), and μt,sh (d) for the four RS components in the wake at x = 0.9

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Fig. 13

Error quantification for the three linear models discussed in terms of RS error (a), error in the transport equation of Favre-averaged momentum (b) and error in the transport equation of total energy, and TKE (c) for pitchwise profiles at x = 0.9

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Fig. 14

Error quantification for the three linear models discussed in terms of RS error (a), error in the transport equation of Favre-averaged momentum (b) and error in the transport equation of total energy, and TKE (c) for pitchwise profiles at x = 1.1

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