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Research Papers

Three-Dimensional Design and Optimization of a Transonic Rotor in Axial Flow Compressors

[+] Author and Article Information
Hidetaka Okui

Mitsubishi Heavy Industries, LTD,
2-2-1 Shinhama Arai-Cho Takasago,
Hyogo, 676-8686Japan
e-mail: hidetaka_okui@mhi.co.jp

Tom Verstraete

e-mail: tom.verstraete@vki.ac.be

R. A. Van den Braembussche

e-mail: vdb@vki.ac.be

Zuheyr Alsalihi

e-mail: alsalihi@vki.ac.be
Turbomachinery and Propulsion Department,
von Karman Institute for Fluid Dynamics,
Waterloose steenweg 72,
1640 Sint-Genesius-Rode, Belgium

1Corresponding author.

Contributed by the International Gas Turbine Institute (IGTI) Division of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received December 18, 2011; final manuscript received January 30, 2012; published online March 25, 2013. Editor: David Wisler.

J. Turbomach 135(3), 031009 (Mar 25, 2013) (11 pages) Paper No: TURBO-11-1261; doi: 10.1115/1.4006668 History: Received December 18, 2011; Revised January 30, 2012

This paper presents a 3-D optimization of a moderately loaded transonic compressor rotor by means of a multiobjective optimization system. The latter makes use of a differential evolutionary algorithm in combination with an Artificial Neural Network and a 3D Navier-Stokes solver. Operating it on a cluster of 30 processors enabled the evaluation of the off-design performance and the exploration of a large design space composed of the camber line and spanwise distribution of sweep and chord length. Objectives were an increase of efficiency at unchanged stall margin by controlling the shock waves and off-design performance curve. First designs of single blade rows allowed a better understanding of the impact of the different design parameters. Forward sweep with unchanged camber improved the peak efficiency by only 0.3% with the same stall margin. Backward sweep with an optimized S shaped camber line improved the efficiency by 0.6% at unchanged stall margin. It is explained how the camber line control can introduce the same effect as forward sweep and compensate the expected negative effects of backward sweep. The best results (0.7% increase in efficiency and unchanged stall margin) have been obtained by a stage optimization that allows also a spanwise redistribution of the rotor flow and an increase of loading by extra flow turning. The latter compensates the loading shift induced by the backward sweep in order to reduce the inlet Mach number at the downstream stator hub.

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References

Figures

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Fig. 1

Optimization algorithm

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Fig. 2

Computational mesh

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Fig. 3

Parameterization of camberline

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Fig. 4

Parameterization of sweep and chord length

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Fig. 11

(a) Resultant geometry of the second optimization: spanwise variation of sweep. (b) Resultant geometry of the second optimization: spanwise variation of chord length.

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Fig. 12

(a) Chordwise camber line distribution (90%Ht). (b) Difference of peak camber location.

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Fig. 13

(a) Compressor map: speed line. (b) Compressor map: efficiency.

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Fig. 10

(a) Spanwise distribution of inlet axial velocity. (b) Spanwise distribution of efficiency at rotor exit.

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Fig. 9

Comparison of entropy contours at 99% height section

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Fig. 8

Static pressure contour on blade to blade surface

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Fig. 7

Meridional view of static pressure contour on suction surface

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Fig. 6

(a) Compressor map: speed line. (b) Compressor map: efficiency.

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Fig. 5

(a) Resultant geometry of the first optimization: spanwise variation of sweep. (b) Resultant geometry of the first optimization: spanwise variation of chord length.

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Fig. 14

(a) Superimposed tip Mach number contours (baseline versus backward sweep with optimized camber). (b) Superimposed tip Mach number contours (baseline versus backward sweep with baseline camber).

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Fig. 15

(a) Compressor map: speed line. (b) Compressor map: efficiency.

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Fig. 16

Stator inlet and outlet Mach number (comparison of single row optimization geometries)

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Fig. 17

Difference of rotor exit flow angle

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Fig. 18

Stator inlet and outlet Mach number (comparison of single row and stage optimization geometries)

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Fig. 19

Static pressure contours at 90% height

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