Research Papers: Gas Turbines: Manufacturing, Materials, and Metallurgy

Optimization of a Centrifugal Compressor Impeller for Robustness to Manufacturing Uncertainties

[+] Author and Article Information
A. Javed

Faculty of Mechanical,
Maritime and Materials Engineering,
Delft University of Technology,
Process and Energy Laboratory,
Leeghwaterstraat 39,
Delft 2628 CB, The Netherlands
e-mail: adeel.javed@epfl.ch

R. Pecnik

Faculty of Mechanical,
Maritime and Materials Engineering,
Delft University of Technology,
Process and Energy Laboratory,
Leeghwaterstraat 39,
Delft 2628 CB, The Netherlands
e-mail: r.pecnik@tudelft.nl

J. P. van Buijtenen

Faculty of Mechanical,
Maritime and Materials Engineering,
Delft University of Technology,
Process and Energy Laboratory,
Leeghwaterstraat 39,
Delft 2628 CB, The Netherlands
e-mail: j.p.vanbuijtenen@tudelft.nl

1Present address: École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.

Contributed by the Manufacturing Materials and Metallurgy Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received March 9, 2016; final manuscript received March 22, 2016; published online May 3, 2016. Editor: David Wisler.

J. Eng. Gas Turbines Power 138(11), 112101 (May 03, 2016) (11 pages) Paper No: GTP-16-1101; doi: 10.1115/1.4033185 History: Received March 09, 2016; Revised March 22, 2016

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.

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Bunker, R. S. , 2009, “ The Effects of Manufacturing Tolerances on Gas Turbine Cooling,” ASME J. Turbomach., 131(4), pp. 1–11. [CrossRef]
Gazron, V. E. , and Darmofal, D. L. , 2003, “ Impact of Geometric Variability on Axial Compressor Performance,” ASME J. Turbomach., 125(4), pp. 692–703. [CrossRef]
Lecerf, N. , Jeannel, D. , and Laude, A. , 2003, “ A Robust Design Methodology for High-Pressure Compressor Throughflow Optimization,” ASME Paper No. GT2003-38264.
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.
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. [CrossRef]
Baines, N. C. , 2005, Fundamentals of Turbocharging, Concepts ETI, Wilder, VT.
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. [CrossRef]
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. [CrossRef]
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.
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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.
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. [CrossRef]
Myers, R. H. , and Montgomery, D. C. , 1995, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Wiley, New York.
Sacks, J. , Schiller, S. , and Welch, W. , 1989, “ Design of Computer Experiments,” Technometrics, 31(1), pp. 41–47. [CrossRef]
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. [CrossRef]
Hurtado, J. E. , and Barbat, A. H. , 1998, “ Monte Carlo Techniques in Computational Stochastic Mechanics,” Arch. Comput. Methods Eng., 5(1), pp. 3–30. [CrossRef]
Goldberg, D. E. , 1989, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA.
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.
ANSYS, 2009, “ ANSYS BladeGen, Release 13.0 User's Guide,” ANSYS, Inc., Canonsburg, PA.
ANSYS, 2009, “ ANSYS TurboGrid, Release 13.0 User's Guide,” ANSYS, Inc., Canonsburg, PA.
ANSYS, 2009, “ ANSYS CFX, Release 13.0 User's Guide,” ANSYS, Inc., Canonsburg, PA.
Japikse, D. , 1996, Centrifugal Compressor Design and Performance, Concepts ETI, Wilder, VT.
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.
Verstraete, T. , 2008, Multidisciplinary Turbomachinery Component Optimization Considering Performance, Stress, and Internal Heat Transfer, von Karman Institute (VKI), Brussels, Belgium.
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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Fig. 3

Flow chart for robust design optimization methodology applied for the turbocharger compressor impeller

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Fig. 2

Machining of an impeller wheel

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Fig. 1

Deterministic versus robust designs

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Fig. 4

The turbocharger compressor

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Fig. 7

Performance sensitivity analysis using the surrogate model and comparison with CFD

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Fig. 5

Compressor parameterization

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Fig. 6

Geometric model and grid

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Fig. 11

Schematic comparison between baseline impeller and optimized robust impeller designs

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Fig. 8

Sensitivity ranking (a) pressure ration and (b) isentropic efficiency

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Fig. 9

Probability distributions of variation in impeller pressure ratio and isentropic efficiency

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Fig. 10

Pareto optimal solutions

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Fig. 12

Probability distributions of robust impeller designs and comparison with the baseline (denoted by “Bsl” in the plots): (a) impeller A (white histogram-baseline, gray histogram-robust), (b) impeller B (white histogram-baseline, gray histogram-robust), and (c) impeller C (white histogram-baseline, gray histogram-robust)




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