Abstract

The advanced design of a centrifugal compressor with high efficiency and wide operating range is a challenging task due to the complex flow field arising from the three-dimensional geometry, especially for the high-speed, highly loaded centrifugal compressor stage, which typically has a relatively narrow operating range. A great effort has been undertaken recently to solve the time-costly three-dimensional design problem with the assistance of a metamodel. Some effort has been done to gain insight into the design space with the assistance of the data mining method. However, the published works lack any study that systematically performs the data mining between the performance and three-dimensional geometry data due to two unsolved issues, i.e., lack of reliable systematic data mining model and unresolved high-dimensional data problem in the centrifugal compressor community. To tackle these issues, a systematic metamodel-driven data mining (MDDM) model including six general modules (i.e., problem understanding, data understating, metamodeling, data set preparation, knowledge discovery, and deployment) has been proposed and implemented to the knowledge discovery of the well-known Radiver high-speed centrifugal compressor stage. Particular attention has been paid to develop the design principle of operating range extension for the examined high-speed stage. Four specific data mining techniques, i.e., descriptive statistics, self-organization map, k–d tree, and Sobol index, were used for the statistical, correlation, cluster, and sensitivity analysis. The results showed the performance improvement probabilities, the trade-off relationships between efficiency and pressure ratio/operating range, and the characteristic variation of the performance. Specifically, the wide operating range design subspace and the narrow operating range design subspace were split away from the whole design space. In these subspaces, the two most sensitive geometry parameters that controlled the meridional curvature made a large contribution to the stage performance, especially for the meridional curvature at the shroud side near the impeller outlet. The appropriate variation ranges of the two sensitive geometry parameters were recommended, and the flow mechanism behind them was clarified. The statistical results showed that over 90% of the design stages in the recommended variation ranges had a wide operating range. A design case was chosen randomly in the recommended range to verify the performance improvement via computational fluid dynamics (CFD) simulations. The outcomes of this work are particularly relevant for the advanced design of compressors with high efficiency and a wide operating range for flexibility.

References

1.
IEA
,
2019
, “
Global Energy and CO2 Status Report 2019
,” https://www.iea.org/reports/global-energy-co2-status-report-2019
2.
Tiainen
,
J.
,
Jaatinen-Varri
,
A.
,
Gronman
,
A.
,
Fischer
,
T.
, and
Backman
,
J.
,
2018
, “
Loss Development Analysis of a Micro-scale Centrifugal Compressor
,”
Energy Convers. Manage.
,
166
(
11
), pp.
297
307
.
3.
United Nations Framework Convention on Climate Change
,
2015
, “
Report of the Conference of the Parties on COP 21
.”
4.
Galloway
,
L.
,
Rusch
,
D.
,
Spence
,
S.
,
Vogel
,
K.
,
Hunziker
,
R.
, and
Kim
,
S. I.
,
2018
, “
An Investigation of Centrifugal Compressor Stability Enhancement Using a Novel Vaned Diffuser Recirculation Technique
,”
ASME J. Turbomach.
,
140
(
12
), p.
121009
.
5.
Ju
,
Y. P.
,
Zhang
,
C. H.
, and
Chi
,
X. L.
,
2012
, “
Optimization of Centrifugal Impellers for Uniform Discharge Flow and Wide Operating Range
,”
J. Propul. Power
,
28
(
5
), pp.
888
899
.
6.
Everitt
,
J. N.
, and
Spakovszky
,
Z. S.
,
2012
, “
An Investigation of Stall Inception in Centrifugal Compressor Vaned Diffuser
,”
ASME J. Turbomach.
,
135
(
1
), p.
011025
.
7.
Fujisawa
,
N.
,
Inui
,
T.
, and
Ohta
,
Y.
,
2019
, “
Evolution Process of Diffuser Stall in a Centrifugal Compressor With Vaned Diffuser
,”
ASME J. Turbomach.
,
141
(
4
), p.
041009
.
8.
Krain
,
H.
,
2005
, “
Review of Centrifugal Compressor’s Application and Development
,”
ASME J. Turbomach.
,
127
(
1
), pp.
25
34
.
9.
Stodola
,
A. B.
,
1924
,
Dampf Und Gasturbinen
,
Springer
,
Berlin, Heidelberg
.
10.
Rodgers
,
C.
,
2005
, “
Flow Ranges of 8.0:1 Pressure Ratio Centrifugal Compressors for Aviation Applications
,” ASME Paper No. GT2005-68041.
11.
Japikse
,
D.
,
1996
,
Centrifugal Compressor Design and Performance
,
Concepts ETI
,
Wilder, VT
.
12.
Wu
,
C. H.
,
1952
, “
A General Theory of Three-Dimensional Flow in Subsonic and Supersonic Turbomachines of Axial-, Radial- and Mixed-Flow Types
,”
Paper No. NACA-TN-2604
.
13.
Moore
,
J. G.
,
1985
, “
An Elliptic Calculation Procedure for 3-D Viscous Flow
,”
3D Computational Techniques Applied to Internal Flows in Propulsion Systems, Agard Lecture Series No. 140, NATO, Paris
.
14.
Liu
,
A.
,
Ju
,
Y. P.
, and
Zhang
,
C. H.
,
2018
, “
Parallel Simulation of Aerodynamic Instabilities in Transonic Axial Compressor Rotor
,”
J. Propul. Power
,
34
(
6
), pp.
1561
1573
.
15.
Li
,
Z.
,
Ju
,
Y. P.
, and
Zhang
,
C. H.
,
2019
, “
Parallel Large-Eddy Simulation of Subsonic and Transonic Flows With Transition in Compressor Cascade
,”
J. Propul. Power
,
35
(
6
), pp.
1163
1174
.
16.
Wang
,
G. G.
, and
Shan
,
S.
,
2007
, “
Review of Metamodeling Techniques in Support of Engineering Design Optimization
,”
ASME J. Mech. Des.
,
129
(
4
), pp.
370
380
.
17.
Pierret
,
S.
, and
Van den Braembussche
,
R. A.
,
1999
, “
Turbomachinery Blade Design Using a Navier-Stokes Solver and Artificial Neural Network
,”
ASME J. Turbomach.
,
121
(
2
), pp.
326
332
.
18.
Pierret
,
S.
,
Demeulenaere
,
A.
,
Gouverneur
,
B.
,
Hirsch
,
C.
, and
Van den Braembussche
,
R. A.
,
2000
, “
Designing Turbomachinery Blades with the Function Approximation Concept and the Navier–Stokes Equations
,”
Proceedings of the 8th Symposium on Multidisciplinary Analysis and Optimization
,
Long Beach, CA
,
Sept. 6–8
, p.
4879
.
19.
Verstraete
,
T.
,
Alsalihi
,
Z.
, and
Van den Braembussche
,
R. A.
,
2010
, “
Multidisciplinary Optimization of a Radial Compressor for Microgas Turbine Applications
,”
ASME J. Turbomach.
,
132
(
3
), p.
031004
.
20.
Van den Braembussche
,
R. A.
,
2019
,
Design and Analysis of Centrifugal Compressors
,
Wiley
,
New York
.
21.
Ju
,
Y. P.
, and
Zhang
,
C. H.
,
2010
, “
Multi-objective Optimization Design Method for Tandem Compressor Cascade at Design and Off Design Conditions
,” ASME Paper No. GT2010-22655.
22.
Ju
,
Y. P.
,
Qin
,
R. H.
,
Kipouros
,
T.
,
Parks
,
G.
, and
Zhang
,
C. H.
,
2016
, “
A High-Dimensional Design Optimisation Method for Centrifugal Impellers
,”
Proc. Inst. Mech. Eng. Part A J. Power Energy
,
230
(
3
), pp.
272
288
.
23.
Ju
,
Y. P.
, and
Zhang
,
C. H.
,
2014
, “
Design Optimization and Experimental Study of Tandem Impeller for Centrifugal Compressor
,”
J. Propul. Power
,
30
(
6
), pp.
1490
1501
.
24.
Qin
,
R. H.
,
Ju
,
Y. P.
,
Galloway
,
L.
,
Spence
,
S.
, and
Zhang
,
C. H.
,
2020
, “
High Dimensional Matching Optimization of Impeller-Vaned Diffuser Interaction for a Centrifugal Compressor Stage
,”
ASME J. Turbomach.
,
142
(
12
), p.
121004
.
25.
Simpson
,
T.
,
Toropov
,
V.
,
Balabanov
,
V.
, and
Viana
,
F.
,
2008
, “
Design and Analysis of Computer Experiments in Multidisciplinary Design Optimization: A Review of How Far We Have Come—or Not
,” AIAA 2008-5802.
26.
Li
,
Z. H.
, and
Zheng
,
X. Q.
,
2017
, “
Review of Design Optimization Methods for Turbomachinery Aerodynamics
,”
Prog. Aerosp. Sci.
,
93
(
5
), pp.
1
23
.
27.
Bandaru
,
S.
,
Ng
,
A. H. C.
, and
Deb
,
K.
,
2017
, “
Data Mining Methods for Knowledge Discovery in Multi-Objective Optimization: Part A—Survey
,”
Expert Syst. Appl.
,
70
, pp.
139
159
.
28.
Baert
,
L.
,
Cheriere
,
E.
,
Sainvitu
,
C.
,
Lepot
,
I.
,
Nouvellon
,
A.
, and
Leonardon
,
V.
,
2020
, “
Aerodynamic Optimization of the Low-Pressure Turbine Module: Exploiting Surrogate Models in a High-Dimensional Design Space
,”
ASME J. Turbomach.
,
142
(
3
), p.
031005
.
29.
Song
,
L. M.
,
Guo
,
Z. D.
,
Li
,
J.
, and
Feng
,
Z. P.
,
2016
, “
Research on Metamodel-Based Global Design Optimization and Data Mining Methods
,”
ASME J. Eng. Gas Turbines Power
,
138
(
9
), p.
092604
.
30.
Guo
,
Z. D.
,
Song
,
L. M.
,
Zhou
,
Z. M.
,
Li
,
J.
, and
Feng
,
Z. P.
,
2015
, “
Multi-Objective Aerodynamic Optimization Design and Data Mining of a High Pressure Ratio Centrifugal Impeller
,”
ASME J. Eng. Gas Turbines Power
,
137
(
9
), p.
092602
.
31.
Li
,
X. J.
,
Zhao
,
Y. J.
, and
Liu
,
Z. X.
,
2019
, “
A Novel Global Optimization Algorithm and Data-Mining Methods for Turbomachinery Design
,”
Struct. Multidiscipl. Optim.
,
60
(
2
), pp.
581
612
.
32.
Ju
,
Y. P.
,
Parks
,
G.
, and
Zhang
,
C. H.
,
2017
, “
A Bisection-Sampling-Based Support Vector Regression-High-Dimensional Model Representation Metamodeling Technique for High-Dimensional Problems
,”
Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci.
,
231
(
12
), pp.
2173
2186
.
33.
Wirth
,
R.
,
2000
, “
CRISP-DM : Towards a Standard Process Model for Data Mining
,”
Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining
,
Manchester, UK
,
Apr. 11–13
, pp.
29
39
.
34.
Ziegler
,
K. U.
,
Gallus
,
H. E.
, and
Niehuis
,
R.
,
2003
, “
A Study on Impeller-Diffuser Interaction—Part I: Influence on the Performance
,”
ASME J. Turbomach.
,
125
(
1
), pp.
173
182
.
35.
Ziegler
,
K. U.
,
Gallus
,
H. E.
, and
Niehuis
,
R.
,
2003
, “
A Study on Impeller-Diffuser Interaction—Part II: Detailed Flow Analysis
,”
ASME J. Turbomach.
,
125
(
1
), pp.
183
192
.
36.
ANSYS
,
2013
,
ANSYS Manual, Version 17.0
,
ANSYS, Inc.
,
Canonsburg, PA
.
37.
Menter
,
F. R.
,
1994
, “
Two-Equation Eddy-Viscosity Turbulence Models for Engineering Applications
,”
AIAA J.
,
32
(
8
), pp.
1598
1605
.
38.
Gibson
,
L.
,
Galloway
,
L.
,
Kim
,
S. I.
, and
Spence
,
S.
,
2017
, “
Assessment of Turbulence Model Predictions for a Centrifugal Compressor Simulation
,”
J. Glob. Power Propuls. Soc.
,
1
, pp.
142
156
.
39.
Robinson
,
C.
,
Casey
,
M.
,
Hutchinson
,
B.
, and
Steed
,
R.
,
2016
, “
Impeller-Diffuser Interaction in Centrifugal Compressor
,” ASME Paper No. GT2012-69151.
40.
Connell
,
S.
,
Braaten
,
M.
,
Zori
,
L.
,
Steed
,
R.
,
Hutchinson
,
B.
, and
Cox
,
G.
,
2011
, “
A Comparison of Advanced Numerical Techniques to Model Transient Flow in Turbomachinery Blade Rows
,” ASME Paper No. GT2011-458.
41.
Biesinger
,
T.
,
Cornelius
,
C.
,
Rube
,
C.
,
Braune
,
A.
,
Schmid
,
G.
,
Campregher
,
R.
,
Godin
,
P. G.
, and
Zori
,
L.
,
2010
, “
Unsteady CFD Methods in a Commercial Solver for Turbomachinery Applications
,” ASME Paper No. GT2010-22762.
42.
Pampreen
,
R. C.
,
1993
,
Compressor Surge and Stall
,
Concepts ETI
,
Norwich, VT
.
43.
Sobol
,
I. M.
,
1993
, “
Sensitivity Estimates for Nonlinear Mathematical Models
,”
Math. Model. Comput. Exp.
,
1
(
4
), pp.
407
414
.
44.
Rabitz
,
H.
, and
Alis
,
O. F.
,
1999
, “
General Foundations of High-Dimensional Model Representations
,”
J. Math. Chem.
,
25
(
2–3
), pp.
197
233
.
45.
Weisberg
,
H. F.
,
1992
,
Central Tendency and Variability
,
SAGE Publications, Inc
,
Thousand Oaks, CA
.
46.
Kohonen
,
T.
,
1982
, “
Self-organized Formation of Topologically Correct Feature Maps
,”
Biol. Cybern.
,
43
(
1
), pp.
59
69
.
47.
Mathworks
,
2010
,
MATLAB Manual—R2010b
,
The Mathworks, Inc.
,
Natick, MA
.
48.
Bentley
,
J. L.
,
1975
, “
Multidimensional Binary Search Trees Used for Associative Searching
,”
Commun. ACM
,
18
(
9
), pp.
509
517
.
49.
Sobol
,
I. M.
,
2001
, “
Global Sensitivity Indices for Nonlinear Mathematical Models and Their Monte Carlo Estimates
,”
Math. Comput. Simul.
,
55
(
1–3
), pp.
271
280
.
50.
Krain
,
H.
,
Karpinski
,
G.
, and
Beversdorff
,
M.
,
2001
, “
Flow Analysis in a Transonic Centrifugal Compressor Rotor Using 3-Component Laser Velocimetry
,” ASME Paper No. GT2001-0315, 1.
51.
Deniz
,
S.
,
Greitzer
,
E. M.
, and
Cumpsty
,
N. A.
,
2000
, “
Effects of Inlet Flow Field Conditions on the Performance of Centrifugal Compressor Diffusers : Part 2—Straight- Channel Diffuser
,”
ASME J. Turbomach.
,
122
(
1
), pp.
11
21
.
52.
Everitt
,
J. N.
,
Spakovszky
,
Z. S.
,
Rusch
,
D.
, and
Schiffmann
,
J.
,
2017
, “
The Role of Impeller Outflow Conditions on the Performance of Vaned Diffusers
,”
ASME J. Turbomach.
,
139
(
4
), p.
041004
.
53.
Dean
,
R.
, and
Senoo
,
Y.
,
1960
, “
Rotating Wakes in Vaneless Diffusers
,”
ASME J. Basic Eng.
,
82
(
3
), pp.
563
574
.
54.
Mashimo
,
T.
,
Watanabe
,
I.
, and
Ariga
,
I.
,
1979
, “
Effects of Fluid Leakage on Performance of a Centrifugal Compressor
,”
J. Eng. Power
,
101
(
3
), pp.
337
342
.
55.
Han
,
L.
,
Yuan
,
W.
, and
Wang
,
Y.
,
2018
, “
Influence of Tip Leakage Flow and Ejection on Stall Mechanism in a Transonic Tandem Rotor
,”
Aerosp. Sci. Technol.
,
77
, pp.
499
509
.
56.
Gooding
,
W. J.
,
Meier
,
M. A.
, and
Key
,
N. L.
,
2021
, “
The Impact of Various Modeling Decisions on Flow Field Predictions in a Centrifugal Compressor
,”
ASME J. Turbomach.
,
143
(
10
), p.
101006
.
57.
Huang
,
G.
,
Yang
,
Y.
,
Hong
,
S.
,
Liu
,
Z.
, and
Du
,
S.
,
2020
, “
A New Unsteady Casing Treatment for Micro Centrifugal Compressors to Enlarge Stall Margin
,”
Aerosp. Sci. Technol.
,
106
, p.
106176
.
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