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TECHNICAL PAPERS

A Computational Fluid Dynamics Study of Transitional Flows in Low-Pressure Turbines Under a Wide Range of Operating Conditions

[+] Author and Article Information
Y. B. Suzen

Department of Mechanical Engineering and Applied Mechanics, North Dakota State University, Fargo, ND 58105

P. G. Huang

Mechanical and Materials Engineering Department, Wright State University, Dayton, OH 45435

D. E. Ashpis

 NASA Glenn Research Center at Lewis Field, Cleveland, OH 44135

R. J. Volino

Department of Mechanical Engineering, United States Naval Academy, Annapolis, MD 21402-5042

T. C. Corke, F. O. Thomas, J. Huang

Department of Aerospace and Mechanical Engineering, Center for Flow Physics and Control, University of Notre Dame, Notre Dame, IN 46556

J. P. Lake

Special Projects Flight Commander, 586th FLTS/DON, Holloman AFB, NM 88330

P. I. King

Department of Aeronautics and Astronautics, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433

J. Turbomach 129(3), 527-541 (Feb 13, 2006) (15 pages) doi:10.1115/1.2218888 History: Received February 14, 2004; Revised February 13, 2006

A transport equation for the intermittency factor is employed to predict the transitional flows in low-pressure turbines. The intermittent behavior of the transitional flows is taken into account and incorporated into computations by modifying the eddy viscosity, μt, with the intermittency factor, γ. Turbulent quantities are predicted by using Menter’s two-equation turbulence model (SST). The intermittency factor is obtained from a transport equation model which can produce both the experimentally observed streamwise variation of intermittency and a realistic profile in the cross stream direction. The model had been previously validated against low-pressure turbine experiments with success. In this paper, the model is applied to predictions of three sets of recent low-pressure turbine experiments on the Pack B blade to further validate its predicting capabilities under various flow conditions. Comparisons of computational results with experimental data are provided. Overall, good agreement between the experimental data and computational results is obtained. The new model has been shown to have the capability of accurately predicting transitional flows under a wide range of low-pressure turbine conditions.

Copyright © 2007 by AIAA. Reprinted by permission.
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References

Figures

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Figure 5

Schematic of the test section for experiments of Volino (24)

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Figure 6

Multiblock grid used for computations of experiments of Lake (3,22) and FSTI=0.08% experiments of Huang (23)

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Figure 7

Comparison of computed pressure coefficient with experiments of Lake (3,22)

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Figure 8

Comparison of computed total pressure loss coefficients with experiments of Lake (3,22) and Huang (23)

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Figure 9

Comparison of separation, reattachment, and transition locations for experiments of Lake (3,22)

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Figure 4

Comparison of computed and experimental decay of turbulence for experiments of Huang (23), with grid 3

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Figure 3

Comparison of computed and experimental decay of turbulence for experiments of Huang (23), with grid 0

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Figure 2

Experimental setup used by Lake (3,22)

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Figure 1

P&W Pack B blade cascade details

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Figure 20

Comparison of computed pressure coefficients with experiments of Huang (23) for FSTI=1.6% cases

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Figure 21

Thirty-one zone multiblock grid used for computation of experiments of Volino (24)

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Figure 22

Comparison of computed pressure coefficient distributions with experiments of Volino (24), FSTI=0.5%

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Figure 25

Comparison of computed velocity profiles with experiments of Volino (24), Re=20,581, FSTI=0.5%

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Figure 26

Comparison of computed velocity profiles with experiments of Volino (24), Re=10,291, FSTI=0.5%

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Figure 10

Comparison of computed pressure coefficients with experiments of Huang (23) for FSTI=0.08% cases

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Figure 11

Comparison of separation, reattachment, and transition locations for experiments of Huang (23)

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Figure 12

Comparison of computed velocity profiles with experiments of Huang (23), Re=100,000, FSTI=0.08% case

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Figure 13

Comparison of computed velocity profiles with experiments of Huang (23), Re=75,000, FSTI=0.08% case

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Figure 14

Comparison of computed velocity profiles with experiments of Huang (23), Re=50,000, FSTI=0.08% case

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Figure 15

Grid used for computation of experiments of Huang (23) with FSTI=1.6% and 2.85%

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Figure 16

Comparison of computed pressure coefficients with experiments of Huang (23) for FSTI=2.85% cases

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Figure 17

Comparison of computed velocity profiles with experiments of Huang (23), Re=100,000, FSTI=2.85% case

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Figure 18

Comparison of computed velocity profiles with experiments of Huang (23), Re=75,000, FSTI=2.85% case

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Figure 19

Comparison of computed velocity profiles with experiments of Huang (23), Re=50,000, FSTI=2.85% case

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Figure 23

Comparison of computed velocity profiles with experiments of Volino (24), Re=82,324, FSTI=0.5%

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Figure 24

Comparison of computed velocity profiles with experiments of Volino (24), Re=41,162, FSTI=0.5%

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