Abstract

The blended blade and endwall (BBEW) contouring technology can adjust the dihedral angle between suction surface and endwall, thus reducing corner separation in compressors. Generally, the design of BBEW relies on the experiences, the effective design results may not be the optimal result. In this paper, an optimization approach based on the genetic algorithm (GA) for feature selection and parameter optimization of support vector machine (SVM) is used to obtain the optimal BBEW parameters in a compressor cascade. Based on the sensitivity analysis of the results, it is found that the maximum blended width and the axial position of the maximum blended width are the two most important design parameters. The experimental results show that the optimal BBEW cascade can stretch the spanwise area of the high loss region, and reduce the maximum value in it. The numerical studies were conducted to analyze the flow mechanism. It is shown that the BBEW cascade has a transverse pressure difference at the axial position of the maximum blended width, and magnitude of the pressure difference in proportion to the maximum blended width. The transverse pressure difference removes the low-energy fluid from the corner to the main flow, thus improving the corner separation.

References

1.
Lei
,
V. M.
, and
Spakovszky
,
Z. S.
,
2008
, “
A Criterion for Axial Compressor Hub-Corner Stall
,”
ASME J. Turbomach.
,
130
(
3
), pp.
475
486
.10.1115/1.2775492
2.
Denton
,
J. D.
,
1993
, “
Loss Mechanisms in Turbomachinery
,”
ASME J. Turbomach.
,
115
(
4
), pp.
621
656
.10.1115/1.2929299
3.
Gbadebo
,
S. A.
,
Cumpsty
,
N. A.
, and
Hynes
,
T. P.
,
2005
, “
Three-Dimensional Separations in Axial Compressors
,”
ASME J. Turbomach.
,
127
(
2
), pp.
331
339
.10.1115/1.1811093
4.
Langston
,
L. S.
,
Nice
,
M. L.
, and
Hooper
,
R. M.
,
1977
, “
Three-Dimensional Flow Within a Turbine Cascade Passage
,”
ASME J. Eng. Gas Turbines Power
,
99
(
1
), pp.
21
28
.10.1115/1.3446247
5.
Gallimore
,
S. J.
,
Bolger
,
J. J.
,
Cumpsty
,
N. A.
,
Taylor
,
M. J.
,
Wright
,
P. I.
, and
Place
,
J. M. M.
,
2002
, “
The Use of Sweep and Dihedral in Multistage Axial Flow Compressor Blading—Part I: University Research and Methods Development
,”
ASME J. Turbomach.
,
124
(
4
), pp.
521
532
.10.1115/1.1507333
6.
Gallimore
,
S. J.
,
Bolger
,
J. J.
,
Cumpsty
,
N. A.
,
Taylor
,
M. J.
,
Wright
,
P. I.
, and
Place
,
J. M. M.
,
2002
, “
The Use of Sweep and Dihedral in Multistage Axial Flow Compressor Blading—Part II: Low and High-Speed Designs and Test Verification
,”
ASME J. Turbomach.
,
124
(
4
), pp.
533
541
.10.1115/1.1507334
7.
Ji
,
L. C.
,
Shao
,
W. W.
,
Chen
,
J.
, and
Yi
,
W. L.
,
2007
, “
A Model for Describing the Influences of SUC-EW Dihedral Angle on Corner Separation
,”
ASME
Paper No. GT2007-27618. 10.1115/GT2007-27618
8.
Yi
,
W. L.
, and
Ji
,
L. C.
,
2018
, “
Experimental Investigation on the Performance of Compressor Cascade Using Blended Blade End Wall Contouring Technology
,”
Proc. Inst. Mech. Eng., Part G
,
232
(
15
), pp.
2833
2844
.10.1177/0954410017720470
9.
Li
,
J. B.
,
Li
,
X.
,
Ji
,
L. C.
,
Yi
,
W. L.
, and
Zhou
,
L.
,
2019
, “
Use of Blended Blade and End Wall Method in Compressor Cascades: Definition and Mechanism Comparisons
,”
Aerosp. Sci. Technol.
,
92
, pp.
738
749
.10.1016/j.ast.2019.06.045
10.
Wang
,
D. X.
, and
He
,
L.
,
2010
, “
Adjoint Aerodynamic Design Optimization for Blades in Multistage Turbomachines—Part I: Methodology and Verification
,”
ASME J. Turbomach.
,
132
(
2
), p.
021011
.10.1115/1.3072498
11.
Wang
,
D. X.
, and
He
,
L.
,
2010
, “
Adjoint Aerodynamic Design Optimization for Blades in Multistage Turbomachines—Part II: Validation and Application
,”
ASME J. Turbomach.
,
132
(
2
), p.
021012
.10.1115/1.3103928
12.
Goldberg
,
D. E.
,
1994
, “
Genetic and Evolutionary Algorithms Come of Age
,”
Commun. ACM
,
37
(
3
), pp.
113
120
.10.1145/175247.175259
13.
Shi
,
Y.
,
2001
, “
Particle Swarm Optimization: Developments, Applications and Resources
,”
Evol. Comput.
,
1
, pp.
81
86
.10.1109/CEC.2001.934374
14.
Kirkpatrick
,
S.
,
Gelatt
,
C. D.
, and
Vecchi
,
M. P.
,
1983
, “
Optimization by Simulated Annealing
,”
Science
,
220
(
4598
), pp.
671
680
.10.1126/science.220.4598.671
15.
Meireles
,
M. R. G.
,
Almeida
,
P. E. M.
, and
Simoes
,
M. G.
,
2003
, “
A Comprehensive Review for Industrial Applicability of Artificial Neural Networks
,”
IEEE Trans. Ind. Electron.
,
50
(
3
), pp.
585
601
.10.1109/TIE.2003.812470
16.
Simpson
,
T. W.
,
Mauery
,
T. M.
,
Korte
,
J. J.
, and
Mistree
,
F.
,
2001
, “
Kriging Models for Global Approximation in Simulation-Based Multidisciplinary Design Optimization
,”
AIAA J.
,
39
(
12
), pp.
2233
2241
.10.2514/2.1234
17.
Cortes
,
C.
, and
Vapnik
,
V.
,
1995
, “
Support-Vector Networks
,”
Mach. Learn.
,
20
(
3
), pp.
273
297
.10.1007/BF00994018
18.
Lin
,
P. T.
,
2001
, “
Support Vector Regression: Systematic Design and Performance Analysis
,” Ph.D. thesis,
National Taiwan University
, Taibei, China.
19.
Gu
,
J.
,
Zhu
,
M.
, and
Jiang
,
L.
,
2011
, “
Housing Price Forecasting Based on Genetic Algorithm and Support Vector Machine
,”
Expert Syst. Appl.
,
38
(
4
), pp.
3383
3386
.10.1016/j.eswa.2010.08.123
20.
Li
,
X. Z.
,
Kong
,
J. M.
, and
Wang
,
Z. Y.
,
2012
, “
Landslide Displacement Prediction Based on Combining Method With Optimal Weight
,”
Nat. Hazards
,
61
(
2
), pp.
635
646
.10.1007/s11069-011-0051-y
21.
Cristianini
,
N.
, and
Taylor
,
J. S.
,
2000
,
An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods
,
Cambridge University Press
,
Cambridge, UK
.
22.
Park
,
J. S.
,
1994
, “
Optimal Latin-Hypercube Designs for Computer Experiments
,”
J. Stat. Plann. Inference
,
39
(
1
), pp.
95
111
.10.1016/0378-3758(94)90115-5
23.
Roache
,
P. J.
,
1997
, “
Quantification of Uncertainty in Computational Fluid Dynamics
,”
Annu. Rev. Fluid Mech.
,
29
(
1
), pp.
123
160
.10.1146/annurev.fluid.29.1.123
24.
Shinkyu
,
J.
,
Kazuhisa
,
C.
, and
Shigeru
,
O.
,
2005
, “
Data Mining for Aerodynamic Design Space
,”
J. Aerosp. Comput. Inf. Commun.
,
2
(
11
), pp.
452
469
.10.2514/1.17308
You do not currently have access to this content.