Uncertainty analysis of machining error influence of compressor blade

Limin Gao; Yutong Cai; Haoliang Xu; Weimin Deng

doi:10.13224/j.cnki.jasp.2017.09.025

Uncertainty analysis of machining error influence of compressor blade

Limin Gao, Yutong Cai, Haoliang Xu, Weimin Deng

School of Power and Energy

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

A numerical method was innovatively presented to achieve uncertainty quantification of airfoil considering the randomness of distribution and magnitude of manufacturing error. Airfoil parameterization achieved by NURBS(non-uniform rational B-spline) method and random sample was generated by Monte Carlo method based on error detection and statistics. And then the Kriging surrogate model was trained to get the influence of machining error on compressor performance. Result shows the influence of profile error is related to the working condition and the loss may increase by 0.853% at most. Moreover, the aerodynamic performance is more sensitive to suction surface than to pressure surface. The conclusion can provide more reasonable machining technology requirement and criteria of blade evaluation.

Original language	English
Pages (from-to)	2253-2259
Number of pages	7
Journal	Hangkong Dongli Xuebao/Journal of Aerospace Power
Volume	32
Issue number	9
DOIs	https://doi.org/10.13224/j.cnki.jasp.2017.09.025
State	Published - 1 Sep 2017

Keywords

Aerodynamic performance
Kriging surrogate model
Monte Carlo
Non-uniform rational B-spline (NURBS)
Random machining error

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10.13224/j.cnki.jasp.2017.09.025

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title = "Uncertainty analysis of machining error influence of compressor blade",

abstract = "A numerical method was innovatively presented to achieve uncertainty quantification of airfoil considering the randomness of distribution and magnitude of manufacturing error. Airfoil parameterization achieved by NURBS(non-uniform rational B-spline) method and random sample was generated by Monte Carlo method based on error detection and statistics. And then the Kriging surrogate model was trained to get the influence of machining error on compressor performance. Result shows the influence of profile error is related to the working condition and the loss may increase by 0.853% at most. Moreover, the aerodynamic performance is more sensitive to suction surface than to pressure surface. The conclusion can provide more reasonable machining technology requirement and criteria of blade evaluation.",

keywords = "Aerodynamic performance, Kriging surrogate model, Monte Carlo, Non-uniform rational B-spline (NURBS), Random machining error",

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T1 - Uncertainty analysis of machining error influence of compressor blade

AU - Gao, Limin

AU - Cai, Yutong

AU - Xu, Haoliang

AU - Deng, Weimin

PY - 2017/9/1

Y1 - 2017/9/1

N2 - A numerical method was innovatively presented to achieve uncertainty quantification of airfoil considering the randomness of distribution and magnitude of manufacturing error. Airfoil parameterization achieved by NURBS(non-uniform rational B-spline) method and random sample was generated by Monte Carlo method based on error detection and statistics. And then the Kriging surrogate model was trained to get the influence of machining error on compressor performance. Result shows the influence of profile error is related to the working condition and the loss may increase by 0.853% at most. Moreover, the aerodynamic performance is more sensitive to suction surface than to pressure surface. The conclusion can provide more reasonable machining technology requirement and criteria of blade evaluation.

AB - A numerical method was innovatively presented to achieve uncertainty quantification of airfoil considering the randomness of distribution and magnitude of manufacturing error. Airfoil parameterization achieved by NURBS(non-uniform rational B-spline) method and random sample was generated by Monte Carlo method based on error detection and statistics. And then the Kriging surrogate model was trained to get the influence of machining error on compressor performance. Result shows the influence of profile error is related to the working condition and the loss may increase by 0.853% at most. Moreover, the aerodynamic performance is more sensitive to suction surface than to pressure surface. The conclusion can provide more reasonable machining technology requirement and criteria of blade evaluation.

KW - Aerodynamic performance

KW - Kriging surrogate model

KW - Monte Carlo

KW - Non-uniform rational B-spline (NURBS)

KW - Random machining error

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JO - Hangkong Dongli Xuebao/Journal of Aerospace Power

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Uncertainty analysis of machining error influence of compressor blade

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