Research Papers: Gas Turbines: Structures and Dynamics

Surrogate Modeling of Manufacturing Variation Effects on Unsteady Interactions in a Transonic Turbine

[+] Author and Article Information
Jeffrey M. Brown

Engine Integrity Branch,
Turbine Engine Division,
Aerospace Systems Directorate,
Wright-Patterson AFB, OH 45433
e-mail: jeffrey.brown.70@us.af.mil

Joseph Beck

Perceptive Engineering Analytics, LLC,
Minneapolis, MN 55418
e-mail: joseph.a.beck@peanalyticsllc.com

Alexander Kaszynski

Advanced Engineering Solutions,
Lafayette, CO 80026
e-mail: akascap@gmail.com

John Clark

Turbomachinery Branch,
Turbine Engine Division,
Aerospace Systems Directorate,
Wright-Patterson AFB, OH 45433
e-mail: john.clark.38@us.af.mil

Contributed by the Structures and Dynamics Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received August 1, 2018; final manuscript received August 15, 2018; published online October 11, 2018. Editor: Jerzy T. Sawicki. This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Government's contributions.

J. Eng. Gas Turbines Power 141(3), 032506 (Oct 11, 2018) (12 pages) Paper No: GTP-18-1537; doi: 10.1115/1.4041314 History: Received August 01, 2018; Revised August 15, 2018

This effort develops a surrogate modeling approach for predicting the effects of manufacturing variations on performance and unsteady loading of a transonic turbine. Computational fluid dynamics (CFD) results from a set of 105 as-manufactured turbine blade geometries are used to train and validate the surrogate models. Blade geometry variation is characterized with point clouds gathered from a structured light, optical measurement system and as-measured CFD grids are generated through mesh morphing of the nominal design grid data. Principal component analysis (PCA) of the measured airfoil geometry variations is used to create a reduced basis of independent surrogate model parameters. It is shown that the surrogate model typically captures between 60% and 80% of the CFD predicted variance. Three new approaches are introduced to improve surrogate effectiveness. First, a zonal PCA approach is defined which investigates surrogate accuracy when limiting analysis to key regions of the airfoil. Second, a training point reduction strategy is proposed that is based on the kd tree nearest neighbor search algorithm and reduces the required training points up to 38% while only having a small impact on accuracy. Finally, an alternate reduction approach uses k-means clustering to effectively select training points and reduces the required training points up to 66% with a small impact on accuracy.

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Clark, J. P. , Beck, J. A. , Kaszynski, A. A. , Still, A. , and Ni, R.-H. , 2018, “ The Effect of Manufacturing Variations on Unsteady Interactions in a Transonic Turbine,” ASME J. Turbomach., 140(6), p. 061007.
Lange, A. , Voigt, M. , Vogeler, K. , Schrapp, H. , Johann, E. , and Gummer, V. , 2010, “ Probabilistic CFD Simulation of a High-Pressure Compressor Stage Taking Manufacturing Variability Into Account,” ASME Paper No. GT2010-22484.
Lange, A. , Voigt, M. , Vogeler, K. , Schrapp, H. , Johann, E. , and Gummer, V. , 2012, “ Impact of Manufacturing Variability and Nonaxisymmetry on High-Pressure Compressor Stage Performance,” ASME J. Eng. Gas Turbines Power, 134(3), p. 032504.
Scharfenstein, J. , Heinze, K. , Voigt, M. , Vogeler, K. , and Meyer, M. , 2013, “ Probabilistic CFD Analysis of High Pressure Turbine Blades Considering Real Geometric Effects,” ASME Paper No. GT2013-94161.
Gorelik, M. , Obayomi, J. , Slovisky, J. , Frias, D. , Swanson, H. , McFarland, J. , Enright, M. , and Riha, D. , 2013, “ Effect of Manufacturing Variability on Turbine Engine Performance: A Probabilistic Study,” ASME Paper No. GT2013-95145.
Panizza, A. , Valente, R. , Rubino, D. , and Tapinassi, L. , 2016, “ Impact of Manufacturing Variability on the Aerodynamic Performance of a Centrifugal Compressor Stage With Curvilinear Blades,” ASME Paper No. GT2016-57791.
Panizza, A. , Iurisci, G. , Sassanelli, G. , and Sivasubramaniyan, S. , 2012, “ Performance Uncertainty Quantification for Centrifugal Compressors—Part 1: stage Performance Variation,” ASME Paper No. GT2012-680361.
Panizza, A. , Rubino, D. , and Tapinassi, L. , 2014, “ Efficient Uncertainty Quantification of Centrifugal Compressor Performance Using Polynomial Chaos,” ASME Paper No. GT2014-25081.
van Lil, T. , Voigt, M. , Vogeler, K. , Wacker, C. , and Rockstroh, U. , 2012, “ Probabilistic Analysis of a Radial Gear Compressor,” ASME Paper No. GT2012-69647.
Buche, D. , Beetz, M. , and Ribi, B. , 2010, “ Uncertainty Analysis for Large-Scale Industrial Radial Compressor,” ASME Paper No. GT2010-22918.
Javed, A. , Pecnick, R. , and van Buijtenen, J. , 2016, “ Optimization of a Centrifugal Compressor Impeller for Robustness to Manufacturing Uncertainties,” ASME J. Eng. Gas Turbines Power, 138(11), p. 112101.
Zamboni, G. , Banks, G. , and Bather, S. , 2016, “ Gradient-Based Adjoint and Design of Experiment CFD Methodologies to Improve the Manufacturability of High Pressure Turbine Blades,” ASME Paper No. GT2016-56042.
Xiong, J. , Yang, J. , McBean, I. , Havakechian, S. , and Liu, F. , 2016, “ Statistical Evaluation of the Performance Impact of Manufacturing Variations for Steam Turbines,” ASME Paper No. GT2016-56553.
Marcu, B. , Tran, K. , and Wright, B. , 2002, “ Prediction of Unsteady Loads and Analysis of Flow Changes Due to Turbine Blade Manufacturing Variations During the Development of Turbines for the MB-XX Advanced Upper Stage Engine,” AIAA Paper No. 2002-4162.
Schnell, R. , Lengyel-Kampmann, T. , and Nicke, E. , 2013, “ On the Impact of Geometric Variability on Fan Aerodynamic Performance, Unsteady Blade Row Interaction, and Its Mechanical Characteristics,” ASME J. Turbomach., 136(9), p. 091005.
Clark, J. P. , Aggarwala, A. S. , Velonis, M. A. , Magge, S. S. , and Price, F. R. , 2002, “ Using CFD to Reduce Resonant Stresses on a Single-Stage, High-Pressure Turbine Blade,” ASME Paper No. GT2002-30320.
Brown, J. M. , Slater, J. , and Grandhi, R. V. , 2003, “ Probabilistic Analysis of Geometric Uncertainty Effects on Blade Modal Response,” ASME Paper No. GT2003-38577.
Brown, J. M. , and Grandhi, R. V. , 2005, “ Probabilistic High Cycle Fatigue Assessment Process for Integrally Bladed Rotors,” ASME Paper No. GT2005-69022.
Beck, J. A. , Brown, J. M. , Cross, C. J. , and Slater, J. C. , 2013, “ Probabilistic Mistuning Assessment Using Nominal and Geometry Based Mistuning Methods,” ASME J. Turbomach., 135(5), p. 051004.
Beck, J. A. , Brown, J. M. , Cross, C. J. , and Slater, J. C. , 2014, “ Component-Mode Reduced-Order Models for Geometric Mistuning of Integrally Bladed Rotors,” AIAA J., 52(7), pp. 1345–1356. [CrossRef]
Kaszynski, A. A. , Beck, J. A. , and Brown, J. M. , 2015, “ Experimental Validation of a Mesh Quality Optimized Morphed Geometric Mistuning Model,” ASME Paper No. GT2015-43150.
Kaszynski, A. A. , Beck, J. A. , and Brown, J. M. , 2013, “ Uncertainties of an Automated Optical 3D Geometry Measurement, Modeling, and Analysis Process for Mistuned Integrally Bladed Rotor Reverse Engineering,” ASME J. Eng. Gas Turbines Power, 135(10), p. 102504.
Kaszynski, A. A. , Beck, J. A. , and Brown, J. M. , 2014, “ Automated Finite Element Model Mesh Updating Scheme Applicable to Mistuning Analysis,” ASME Paper No. GT2014-26925.
Clark, J. P. , Koch, P. J. , Ooten, M. K. , Johnson, J. J. , Dagg, J. , McQuilling, M. W. , adn, P. D. , and Johnson, F. H. , 2009, “ Design of Turbine Components to Answer Research Questions in Unsteady Aerodynamics and Heat Transfer,” WPAFB, OH, AFRL Report No. AFRL-RZ-WP-TR-2009-2180.
Ooten, M. K. , Anthony, R. J. , Lethander, A. T. , and Clark, J. P. , 2015, “ Unsteady Aerodynamic Interaction in a Closely Coupled Turbine Consistent With Contrarotation,” ASME J. Turbomach., 138(6), p. 061004.
Ni, R.-H. , Humber, W. , Ni, M. , Capece, V. R. , Ooten, M. K. , and Clark, J. P. , 2016, “ Aerodynamic Dynamic Predictions for Oscillating Airfoils in Cascades Using Moving Meshes,” ASME Paper No. GT2016-57017.
Hancock, B. J. , and Clark, J. P. , 2014, “ Reducing Shock Interactions in Transonic Turbine Via Three-Dimensional Aerodynamic Shaping,” AIAA J. Propul. Power, 30(5), pp. 1248–1256. [CrossRef]
Lange, A. , Vogeler, K. , Gummer, V. , Schrapp, H. , and Clemen, C. , 2009, “ Introduction of a Parameter Based Compressor Blade Model for Considering Measured Geometry Uncertainties in Numerical Simulation,” ASME Paper No. GT2009-59937.


Grahic Jump Location
Fig. 1

Efficiency histogram

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Fig. 2

46E DFT magnitude histogram

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Fig. 4

PC participation histogram

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Fig. 5

PC explained cumulative variance

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Fig. 6

Kulite locations and 46E scaled pressure

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Fig. 7

Efficiency surrogate A versus P

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Fig. 8

Kulite #6 surrogate A versus P

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Fig. 9

Forty-six percent span max pressure surrogate A versus P

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Fig. 10

Kulite 10 phase surrogate A versus P



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