Research Papers

Nonlinear Dynamic Probabilistic Analysis for Turbine Casing Radial Deformation Using Extremum Response Surface Method Based on Support Vector Machine

[+] Author and Article Information
Chengwei Fei

e-mail: feicw544@163.com

Guangchen Bai

e-mail: dlxbgc@buaa.edu.cn
School of Jet Propulsion,
Beijing University of Aeronautics and Astronautics,
Beijing, 100191, China

1Corresponding author.

Contributed by the Design Engineering Division of ASME for publication in the Journal of Computational and Nonlinear Dynamics. Manuscript received May 18, 2012; final manuscript received February 3, 2013; published online March 26, 2013. Assoc. Editor: Jozsef Kovecses.

J. Comput. Nonlinear Dynam 8(4), 041004 (Mar 26, 2013) (8 pages) Paper No: CND-12-1075; doi: 10.1115/1.4023589 History: Received May 18, 2012; Revised February 03, 2013

To improve the computational efficiency of nonlinear dynamic probabilistic analysis for aeroengine typical components, an extremum response surface method based on the support vector machine (SVM ERSM) was proposed in this paper. The basic principle was introduced and the mathematical model was established for the SVM ERSM. The probabilistic analysis of turbine casing radial deformation was taken as an example to validate the SVM ERSM considering the influences of nonlinear material property and dynamic heat loads. The results of probabilistic analysis imply that the distribution features of random parameters and the major factors are gained for more accurate the design of casing radial deformation. The SVM ERSM offers a feasible and valid method, which possesses high efficiency and high precision in the nonlinear dynamic probabilistic analysis. Moreover, the SVM ERSM is promising to provide an useful insight for casing dynamic optimal design and the blade-tip clearance control of aeroengine high pressure turbine.

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Fig. 1

ERSM idea for structural dynamic PA

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Fig. 2

Basic principle of SVM ERSM

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Fig. 8

Simulation results of casing radial deformation Y

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Fig. 9

Probabilistic distributions of partial random variables

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Fig. 10

Sensitivity analysis results of random variables

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Fig. 3

Structure and FEM of turbine casing

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Fig. 4

Load spectrums from aeroengine

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Fig. 5

Sampling history of extremum output response Y

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Fig. 6

Variation of casing radial deformation Y

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Fig. 7

Casing radial deformations distribution at t = 180 s



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