Abstract

This study proposes a new global gas dynamics optimization method, which was applied to a multi-objective optimization task of centrifugal compressor performance with the aim of determining the improvement probability for achieving high efficiency across a wide operating range. Initially, the original nondominated neighbor immune algorithm (NNIA) was extended to solve constrained multi-objective optimization problems for the first time, which mainly incorporated a procedure for handling inequality and equality constraints without additional parameters. Subsequently, an adaptive topological back-propagation multilayer feed-forward artificial neural network (BP-MLFANN) was trained using the modified NNIA to quickly evaluate the fitness value of the centrifugal compressor stage performance during the optimization. The feasibility of the method was validated using the first stage of a refrigeration centrifugal compressor. The results indicated a substantial enhancement in the stage efficiency of the optimized impeller at the Near-stall, Design, and Near-choke operating points, with increasement of 1.8%, 1.9%, and 4%, respectively, as compared to the baseline stage. The flow field analysis shows that the impact loss at impeller leading edge and flow separation in the passage reduced greatly, the mixing process between the leakage flow and mainstream in the channel is significantly weakened, thus the flow field becomes more uniform after optimization. The new global gas dynamics optimization method provides a reference for the development of efficient and rapid optimization techniques for centrifugal compressor.

References

1.
Krain
,
H.
,
2005
, “
Review of Centrifugal Compressor's Application and Development
,”
ASME J. Turbomach.
,
127
(
1
), pp.
25
34
.10.1115/1.1791280
2.
Tan
,
C. S.
,
Day
,
I.
,
Morris
,
S.
, and
Wadia
,
A.
,
2010
, “
Spike-Type Compressor Stall Inception, Detection, and Control
,”
Annu. Rev. Fluid Mech.
,
42
(
1
), pp.
275
300
.10.1146/annurev-fluid-121108-145603
3.
Yang
,
C.
,
Wang
,
W.
,
Zhang
,
H.
,
Yang
,
C.
, and
Li
,
Y.
,
2018
, “
Investigation of Stall Process Flow Field in Transonic Centrifugal Compressor With Volute
,”
Aerosp. Sci. Technol.
,
81
, pp.
53
64
.10.1016/j.ast.2018.07.047
4.
Horlock
,
J.
, and
Denton
,
J.
,
2005
, “
A Review of Some Early Design Practice Using Computational Fluid Dynamics and a Current Perspective
,”
ASME J. Turbomach.
,
127
(
1
), pp.
5
13
.10.1115/1.1650379
5.
Li
,
Z. H.
, and
Zheng
,
X. Q.
,
2017
, “
Review of Design Optimization Methods for Turbomachinery Aerodynamics
,”
Prog. Aerosp. Sci.
,
93
, pp.
1
23
.10.1016/j.paerosci.2017.05.003
6.
Tajiri
,
K.
,
Zhao
,
J.
,
Hohlweg
,
W. C.
, and
Zhang
,
H.
,
2013
, “
On the Coupling of Direct Design and Optimization Techniques for Mitigating the Turbomachinery Performance Test Risk
,”
ASME
Paper No. GT2013-94645.10.1115/GT2013-94645
7.
Mueller
,
L.
,
Alsalihi
,
Z.
, and
Verstraete
,
T.
,
2013
, “
Multidisciplinary Optimization of a Turbocharger Radial Turbine
,”
ASME J. Turbomach.
,
135
(
2
), p.
021022
.10.1115/1.4007507
8.
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
.10.1115/1.3144162
9.
Liang
,
D.
,
Yang
,
H.
,
Li
,
Q.
,
Xu
,
C.
, and
Xie
,
Q.
,
2022
, “
Performance Optimization of the High-Pressure Compressor in Series Two-Stage Turbocharging System Based on Low-Speed Performance Requirements of Diesel Engine
,”
ASME J. Eng. Gas Turbines Power
,
144
(
8
), p.
081008
.10.1115/1.4054747
10.
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
.10.1115/1.1516814
11.
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
.10.1115/1.1516815
12.
Trevor
,
H.
,
Robert
,
T.
, and
Jerome
,
F.
,
2008
,
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
,
Springer
,
Berlin, Germany
.
13.
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
.10.1115/1.2429697
14.
Qin
,
R. H.
,
Ju
,
Y. P.
, and
Xie
,
H.
,
2020
, “
High Dimensional Matching Optimization of Impeller-Vaned Diffuser Interaction for a Centrifugal Compressor Stage
,”
ASME J. Turbomach.
,
142
(
12
), p.
121004
.10.1115/1.4047898
15.
Zhang
,
S.
,
Yang
,
B.
,
Xie
,
H.
, and
Song
,
M.
,
2020
, “
Applications of an Improved Aerodynamic Optimization Method on a Low Reynolds Number Cascade
,”
Processes
,
8
(
9
), p.
1150
.10.3390/pr8091150
16.
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
.10.1115/1.4046232
17.
Backhaus
,
J.
,
Aulich
,
M.
,
Frey
,
C.
, and
Lengyel
,
T.
,
2012
, “
Gradient Enhanced Surrogate Models Based on Adjoint CFD Methods for the Design of a Counter Rotating Turbofan
,”
ASME
Paper No. GT2012-69706.10.1115/GT2012-69706
18.
Lopez
,
D. I.
,
Ghisu
,
T.
, and
Shahpar
,
S.
,
2021
, “
Global Optimization of a Transonic Fan Blade Through AI-Enabled Active Subspaces
,”
ASME J. Turbomach.
,
144
(
1
), pp.
1
13
.10.1115/1.4052136
19.
Wu
,
L.
,
Kim
,
J. W.
,
Wilson
,
A. G.
, and
Shahpar
,
S.
,
2022
, “
Automatic Design Optimization of a Transonic Compressor Rotor for Improving Aeroacoustic and Aerodynamic Performance
,”
ASME J. Turbomach.
,
144
(
8
), p.
081016
.10.1115/1.4053916
20.
Li
,
J.
,
Li
,
W.
, and
Ji
,
L.
,
2022
, “
Efficient Optimization Design of Vortex Generators in a Highly Loaded Compressor Stator
,”
ASME J. Eng. Gas Turbines Power
,
144
(
6
), p.
061002
.10.1115/1.4053707
21.
Gan
,
X.
,
Pei
,
J.
,
Wang
,
W.
,
Yuan
,
S.
, and
Lin
,
B.
,
2023
, “
Application of a Modified MOPSO Algorithm and Multi-Layer Artificial Neural Network in Centrifugal Pump Optimization
,”
Eng. Optim.
,
55
(
4
), pp.
580
598
.10.1080/0305215X.2021.2015585
22.
Avila
,
G.
, and
Matyus
,
E.
,
2019
, “
Toward Breaking the Curse of Dimensionality in (ro) Vibrational Computations of Molecular Systems With Multiple Large-Amplitude Motions
,”
J. Chem. Phys.
,
150
(
17
), p.
174107
.10.1063/1.5090846
23.
Wang
,
J.
,
Zheng
,
X.
,
Yang
,
H.
,
Sun
,
Z.
,
Song
,
Z.
, and
Fu
,
Y.
,
2023
, “
An Efficient Quantification Method Based on Feature Selection for High-Dimensional Uncertainties of Multistage Compressors
,”
ASME J. Eng. Gas Turbines Power
,
145
(
2
), p.
021002
.10.1115/1.4056017
24.
Coello
,
C. A.
, and
Cruz
,
C. N.
,
2002
, “
An Approach to Solve Multiobjective Optimization Problems Based on an Artificial Immune System
,”
Proceedings of 1st International Conference on Artificial Immune Systems
, Canterbury, UK, pp.
212
221
.
25.
Gong
,
M.
,
Jiao
,
L.
,
Du
,
H.
, and
Bo
,
L.
,
2008
, “
Multiobjective Immune Algorithm With Nondominated Neighbor-Based Selection
,”
Evol. Comput.
,
16
(
2
), pp.
225
255
.10.1162/evco.2008.16.2.225
26.
Himanshu
,
J.
, and
Kalyanmoy
,
D.
,
2014
, “
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
,”
IEEE Trans. Evol. Comput.
,
18
(
4
), pp.
577
601
.10.1109/TEVC.2013.2281535
27.
Himanshu
,
J.
, and
Kalyanmoy
,
D.
,
2014
, “
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach
,”
IEEE Trans. Evol. Comput.
,
18
(
4
), pp.
602
622
.10.1109/TEVC.2013.2281534
28.
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
29.
Irie
,
B.
, and
Miyake
,
S.
,
1988
, “
Capabilities of Three-Layered Perceptrons
,”
Proceedings of IEEE International Conference on Neural Networks
,
San Diego, CA
, July 24–27.10.1109/ICNN.1988.23901
30.
Nazghelichi
,
T.
,
Aghbashlo
,
M.
, and
Kianmehr
,
M. H.
,
2011
, “
Optimization of an Artificial Neural Network Topology Using Coupled Response Surface Methodology and Genetic Algorithm for Fluidized Bed Drying
,”
Comput Electron Agric.
,
75
(
1
), pp.
84
91
.10.1016/j.compag.2010.09.014
31.
Cutello
,
V.
,
Nicosia
,
G.
, and
Pavone
,
M.
,
2004
, “
Exploring the Capability of Immune Algorithms: A Characterization of Hypemutation Operators
,”
Proceedings of Third International Conference on Artificial Immune Systems
, ICARIS 2004, Volume 3239 of Lecture Notes in Computer Science, Sicily, Italy, Sept. 13–16, pp.
263
276
.10.1007/978-3-540-30220-9_22
32.
Deb
,
K.
,
Pratap
,
A.
,
Agarwal
,
S.
, and
Meyarivan
,
T.
,
2002
, “
A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II
,”
IEEE Trans. Evol. Comput.
,
6
(
2
), pp.
182
197
.10.1109/4235.996017
33.
Miettinen
,
K.
,
1999
,
Nonlinear Multi-Objective Optimization
,
Kluwer
,
Alphen aan den Rijn, The Netherlands
.
34.
Zhang
,
Q.
,
Zhou
,
A.
,
Zhao
,
S. Z.
,
Suganthan
,
P. N.
,
Liu
,
W.
, and
Tiwari
,
S.
,
2008
, “
Multiobjective Optimization Test Instances for the CEC-2009 Special Session and Competition
,”
Nanyang Technological University
,
Singapore
, p.
2008
.https://alroomi.org/multimedia/CEC_Database/CEC2009/MultiObjectiveEA/CEC2009_MultiObjectiveEA_TechnicalReport.pdf
35.
Shirazian
,
S.
, and
Alibabaei
,
M.
,
2016
, “
Using Neural Networks Coupled With Particle Swarm Optimization Technique for Mathematical Modeling of Air Gap Membrane Distillation (AGMD) Systems for Desalination Process
,”
Neural Comput. Appl.
,
28
(
8
), pp.
2099
2104
.10.1007/s00521-016-2184-0
36.
Japikse
,
D.
,
1996
,
Centrifugal Compressor Design and Performance
,
Concepts ETI, Wilder
,
VT
.
37.
ANSYS CFX 2021 R1
,
2021
,
ANSYS CFX-Solver Theory Guide
,
ANSYS, Inc
.,
Canonsburg, PA
.
38.
Degani
,
D.
,
Seginer
,
A.
, and
Levy
,
Y.
,
1990
, “
Graphical Visualization of vortical flows by Means of Helicity
,”
AIAA J.
,
28
(
8
), pp.
1347
1352
.10.2514/3.25224
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