Compressor impellers for mass-market turbochargers are die-casted and machined with an aim to achieve high dimensional accuracy and acquire specific performance. However, manufacturing uncertainties result in dimensional deviations causing incompatible operational performance and assembly errors. Process capability limitations of the manufacturer can cause an increase in part rejections, resulting in high production cost. This paper presents a study on a centrifugal impeller with focus on the conceptual design phase to obtain a turbomachine that is robust to manufacturing uncertainties. The impeller has been parameterized and evaluated using a commercial computational fluid dynamics (CFDs) solver. Considering the computational cost of CFD, a surrogate model has been prepared for the impeller by response surface methodology (RSM) using space-filling Latin hypercube designs. A sensitivity analysis has been performed initially to identify the critical geometric parameters which influence the performance mainly. Sensitivity analysis is followed by the uncertainty propagation and quantification using the surrogate model based Monte Carlo simulation. Finally, a robust design optimization has been carried out using a stochastic optimization algorithm leading to a robust impeller design for which the performance is relatively insensitive to variability in geometry without reducing the sources of inherent variation, i.e., the manufacturing noise.

References

1.
Bunker
,
R. S.
,
2009
, “
The Effects of Manufacturing Tolerances on Gas Turbine Cooling
,”
ASME J. Turbomach.
,
131
(
4
), pp.
1
11
.
2.
Gazron
,
V. E.
, and
Darmofal
,
D. L.
,
2003
, “
Impact of Geometric Variability on Axial Compressor Performance
,”
ASME J. Turbomach.
,
125
(
4
), pp.
692
703
.
3.
Lecerf
,
N.
,
Jeannel
,
D.
, and
Laude
,
A.
,
2003
, “
A Robust Design Methodology for High-Pressure Compressor Throughflow Optimization
,”
ASME
Paper No. GT2003-38264.
4.
Bestle
,
D.
,
Flassig
,
P. M.
, and
Dutta
,
A. K.
,
2010
, “
Robust Design of Compressor Blades in the Presence of Manufacturing Noise
,”
European Turbomachinery Conference ETC
, Istanbul, Turkey.
5.
Kumar
,
A.
,
Nair
,
P. B.
,
Keane
,
A. J.
, and
Shahrokh
,
S.
,
2008
, “
Robust Design Using Bayesian Monte Carlo
,”
Int. J. Numer. Methods Eng.
,
73
(
11
), pp.
1497
1517
.
6.
Baines
,
N. C.
,
2005
,
Fundamentals of Turbocharging
,
Concepts ETI
, Wilder, VT.
7.
Wallace
,
G.
,
Jackson
,
A. P.
, and
Zhu
,
Q.
,
2010
, “
High-Quality Aluminum Turbocharger Impellers Produced by Thixocasting
,”
Trans. Nonferrous Met. Soc. China
,
20
(
9
), pp.
1786
1791
.
8.
Liu
,
K.
,
Waumans
,
T.
,
Peirs
,
J.
, and
Reynaerts
,
D.
,
2009
, “
Precision Manufacturing of Key Components for an Ultra Miniature Gas Turbine Unit for Power Generation
,”
Microsyst. Technol.
,
15
(
9
), pp.
1417
1425
.
9.
Sotome
,
T.
, and
Sakoda
,
S.
,
2007
, “
Development of Manufacturing Technology for Precision Compressor Wheel Castings for Turbochargers
,” Castings and Forging Division, Furukawa-Sky Aluminum,
Technical Review 32
, pp.
56
60
.
10.
Childs
,
P. R. N.
, and
Noronha
,
M. B.
,
1999
, “
The Impact of Machining Techniques on Centrifugal Compressor Impeller Performance
,”
ASME J. Turbomach.
,
121
(
4
), pp.
637
643
.
11.
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
), pp.
1
7
.
12.
Bonaiuti
,
D.
,
Arnone
,
A.
,
Ermini
,
M.
, and
Baldassarre
,
L.
,
2006
, “
Analysis and Optimization of Transonic Centrifugal Compressor Impellers Using the Design of Experiment Technique
,”
ASME J. Turbomach.
,
128
(
4
), pp.
786
797
.
13.
Kim
,
J. H.
,
Kim
,
J. W.
, and
Kim
,
K. Y.
,
2011
, “
Axial-Flow Ventilation Fan Design Through Multi-Objective Optimization to Enhance Aerodynamic Performance
,”
ASME J. Fluids Eng.
,
133
(10), pp.
1
12
.
14.
Kipourous
,
T.
,
Jaeggi
,
D. M.
,
Dawes
,
W. N.
,
Perks
,
G. T.
,
Savill
,
A. M.
, and
Clarkson
,
P. J.
,
2008
, “
Biobjective Design Optimization for Axial Compressors Using Tabu Search
,”
AIAA J.
,
46
(
3
), pp.
701
711
.
15.
Shahpar
,
S.
,
2004–2007
, “
Design of Experiment, Screening and Response Surface Modeling to Minimize the Design Cycle Time
,”
Optimization Methods & Tools for Multi-Criteria/Multidisciplinary Design
(2004–2007 Lecture Series),
von Karman Institute
,
Brussels, Belgium
.
16.
Javed
,
A.
,
Pecnik
,
R.
,
Olivero
,
M.
, and
van Buijtenen
,
J. P.
,
2012
, “
Effects of Manufacturing Noise on Microturbine Centrifugal Impeller Performance
,”
ASME J. Eng. Gas Turbines Power
,
134
(10), pp.
1
9
.
17.
Myers
,
R. H.
, and
Montgomery
,
D. C.
,
1995
,
Response Surface Methodology: Process and Product Optimization Using Designed Experiments
,
Wiley
,
New York
.
18.
Sacks
,
J.
,
Schiller
,
S.
, and
Welch
,
W.
,
1989
, “
Design of Computer Experiments
,”
Technometrics
,
31
(
1
), pp.
41
47
.
19.
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
.
20.
Hurtado
,
J. E.
, and
Barbat
,
A. H.
,
1998
, “
Monte Carlo Techniques in Computational Stochastic Mechanics
,”
Arch. Comput. Methods Eng.
,
5
(
1
), pp.
3
30
.
21.
Goldberg
,
D. E.
,
1989
,
Genetic Algorithms in Search, Optimization and Machine Learning
,
Addison-Wesley
,
Reading, MA
.
22.
Olivero
,
M.
,
Javed
,
A.
,
Pecnik
,
R.
,
Colonna
,
P.
, and
van Buijtenen
,
J. P.
,
2011
, “
Study on the Tip Clearance Effects in the Centrifugal Compressor of a Micro Gas Turbine by Means of Numerical Simulations
,” IGTC, Osaka, Japan, Paper No. IGTC2011-233.
23.
ANSYS
,
2009
, “
ANSYS BladeGen, Release 13.0 User's Guide
,” ANSYS, Inc., Canonsburg, PA.
24.
ANSYS
,
2009
, “
ANSYS TurboGrid, Release 13.0 User's Guide
,” ANSYS, Inc., Canonsburg, PA.
25.
ANSYS
,
2009
, “
ANSYS CFX, Release 13.0 User's Guide
,” ANSYS, Inc., Canonsburg, PA.
26.
Japikse
,
D.
,
1996
,
Centrifugal Compressor Design and Performance
,
Concepts ETI
, Wilder, VT.
27.
Harinck
,
J.
,
Alsalihi
,
Z.
,
van Buijtenen
,
J. P.
, and
van den Braembussche
,
R. A.
,
2005
, “
Optimization of a 3D Radial Turbine by Means of an Improved Genetic Algorithm
,”
6th European Turbomachinery Conference
, Lille, France, pp.
1033
1042
.
28.
Verstraete
,
T.
,
2008
,
Multidisciplinary Turbomachinery Component Optimization Considering Performance, Stress, and Internal Heat Transfer
,
von Karman Institute (VKI)
,
Brussels, Belgium
.
29.
Libelli
,
S. M.
, and
Alba
,
P.
,
2000
, “
Adaptive Mutation in Genetic Algorithms
,”
Soft Computing
, Vol.
4
,
Springer-Verlag
, Berlin, pp.
76
80
.
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