Research Papers: Flows in Complex Systems

Vorticity Dynamics in Axial Compressor Flow Diagnosis and Design

[+] Author and Article Information
Yantao Yang1

Institute of Engineering Research,  Peking University, Beijing, 100871, P.R.C.

Hong Wu, Qiushi Li, Sheng Zhou2

Institute of Engineering Research,  Peking University, Beijing, 100871, P.R.C.

Jiezhi Wu1 n3

Institute of Engineering Research,  Peking University, Beijing, 100871, P.R.C.jzwu@mech.pku.edu.cn


Also at State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing, 100871, China.


Also at National Key Laboratory on Aero-engines School of Jet Propulsion, Beijing University of Aeronautics and Astronautics, Beijing, 100083, China.


Also at The University of Tennessee Space Institute, Tullahoma, TN 37388.

J. Fluids Eng 130(4), 041102 (Apr 03, 2008) (9 pages) doi:10.1115/1.2903814 History: Received November 29, 2006; Revised November 07, 2007; Published April 03, 2008

It is well recognized that vorticity and vortical structures appear inevitably in viscous compressor flows and have strong influence on the compressor performance. However, conventional analysis and design procedure cannot pinpoint the quantitative contribution of each individual vortical structure to the integrated performance of a compressor, such as the stagnation-pressure ratio and efficiency. We fill this gap by using the so-called derivative-moment transformation, which has been successfully applied to external aerodynamics. We show that the compressor performance is mainly controlled by the radial distribution of azimuthal vorticity, of which an optimization in the through-flow design stage leads to a simple Abel equation of the second kind. The satisfaction of the equation yields desired circulation distribution that optimizes the blade geometry. The advantage of this new procedure is demonstrated by numerical examples, including the posterior performance check by 3D Navier–Stokes simulation.

Copyright © 2008 by American Society of Mechanical Engineers
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Figure 1

The sketch of the pipe flow and the partition. The flow is along the z-axis and the vertical line denotes the cross plane S.

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Figure 2

A typical ∂s∕∂r distribution on the meridian plane of the compressor, obtained by a 3D rotor-only RANS simulation.

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Figure 3

3D computational mesh: (a) meridional plane mesh; (b) tip-section mesh

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Figure 4

Γ-distributions in TFD procedure. Nondimensionalized by rtipRT. (a) Initial Γ distribution; (b) optimal Γ distribution.

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Figure 5

Comparison of the radial distributions of Γ at the rotors’ trailing edges of 3D simulations. The ordinates are nondimensionalized by rtipRT, and the height of rotor blade. Solid lines, Design A; dashed lines: Design B.

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Figure 9

Term-by-term comparison of SPF of Rotors A and B. Notice the scales of ordinates. Solid lines, Design A; dashed lines, Design B. (a) Total SPF, (b) the ωθ term in Eq. 14, (c) −(∫Wr2ρuθuzωzdrdθ)∕2, (d) −(1∕4∫Wr2uz∣u∣2∂ρ∕∂rdrdθ)∕4, (e) −(∫Wr2ρΨdrdθ)∕2, (f) −(∫Wr2P*∂ur∕∂zdrdθ)∕2, and (g) Iisen.

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Figure 6

Comparison of the configuration of two rotors, with the flow coming from lower left corner. Red solid, Design A; blue dashed, Design B. (a) 9% blade-height sections; (b) tip sections.

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Figure 7

Comparison of the performance curves of two rotors: (a) Stagnation-pressure ratio; (b) efficiency

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Figure 8

ωθ distributions on the meridian plane of two rotors of 3D RANS, with the corresponding zoom-in plots. In the zoom-in plots, the unit lengthes of ordinates are ten times larger than those of the abscissas for clarity, and the color bands are larger than the whole field plots. The computational meshes are also shown by the gray lines. (a) Design A; (b) Design B.





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