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Research Papers

Multiscale Modeling of Turbine Engine Component Under Manufacturing Uncertainty

[+] Author and Article Information
Austin M. McKeand

G.W.W. School of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: amckeand3@gatech.edu

Recep M. Gorguluarslan

Department of Mechanical Engineering,
TOBB University of Economics and Technology,
Ankara, Turkey
e-mail: rmgorguluarslan@gmail.com

Jeff Brown

Turbine Engine Division,
Air Force Research Laboratory,
Dayton, OH 45433
e-mail: jeffrey.brown.70@us.af.mil

Seung-Kyum Choi

G.W.W. School of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30318
e-mail: schoi@me.gatech.edu

1Corresponding author.

Manuscript received October 30, 2018; final manuscript received May 28, 2019; published online August 5, 2019. Assoc. Editor: John Michopoulos. This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Government’s contributions.

J. Comput. Inf. Sci. Eng 19(4), (Aug 05, 2019) (12 pages) Paper No: JCISE-18-1287; doi: 10.1115/1.4044011 History: Received October 30, 2018; Accepted May 29, 2019

Efficient modeling of uncertainty introduced by the manufacturing process is critical in the design of turbine engine components. In this study, a stochastic multiscale modeling framework is developed to efficiently account for the geometric uncertainty associated with the manufacturing process to accurately predict the performance of engine components. Multiple efficient statistic tools are integrated into the proposed framework. Specifically, a semivariogram analysis procedure is proposed to quantify spatial variability of the uncertain geometric parameters based on a set of manufactured specimens. Karhunen–Loeve expansion is utilized to create a set of correlated random variables from the uncertainty data obtained by variogram analysis. A detailed finite element model of the component is created that accounts for the uncertainties quantified by these correlated random variables. A stochastic upscaling method is then developed to form a simplified model that can represent this detailed model with high accuracy under uncertainties. Specifically, a parametric model generation process is developed to represent the detailed model using Bezier curves and the uncertainties are upscaled to the parameters of this parametric representation. The results of the simulations are then validated with real experimental results. The application results show that the proposed framework effectively captures the geometric uncertainties introduced by manufacturing while providing accurate predictions under uncertainties.

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Figures

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Fig. 1

Example of area calculation in u-pooling

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Fig. 2

Flowchart of phase 1 of the proposed approach: capturing the spatial variations and uncertainty quantification

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Fig. 3

Flowchart of phase 2 of the proposed approach: stochastic upscaling method

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Fig. 4

Obtained CMM measurement data: (a) CMM measurement curves and (b) separated sections to use in coarse-scale modeling

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Fig. 5

Demonstration of the curve sections for one profile on x–y plane

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Fig. 6

Random field realization with K-L expansion

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Fig. 7

Fine-scale model of the turbine blade: (a) the blade geometry and boundary conditions and (b) geometric variations in the fine-scale FE model shown for two different FE model generations based on the quantified uncertainties

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Fig. 8

PDF of the first natural frequency from the fine-scale model

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Fig. 9

Generation of the surfaces of the coarse-scale model using Bezier curves: (a) third degree Bezier curves are used in u direction for each of the four sections shown by S1, S1, S1, and S1, (b) fifth-degree Bezier curves are used in v direction for each of four sections, and (c) final surface geometry

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Fig. 10

Generation of the FE model: (a) Delaunay triangulation in two dimensions, (b) generated surface mesh of the turbine blade using the Delaunay triangulation, and (c) generated solid mesh of the turbine blade

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Fig. 11

Comparison of the surrogate model results for the response estimation

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Fig. 12

Stochastic upscaling results: (a) PDF of the optimal scaling parameter Δ and (b) comparison of the coarse-scale model responses by the fine-scale model responses

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Fig. 13

Validation via physical experiment: (a) experimental setup and (b) single transfer function result

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Fig. 14

Empirical CDF of experimental results

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