Research Papers

Probabilistic Life Assessment of Gas Turbine Blades

[+] Author and Article Information
Nikita Thakur

Computational Engineering and Design Group, School of Engineering Sciences, University of Southampton, Southampton SO17 1BJ, UKnt1f06@soton.ac.uk

A. J. Keane1

Computational Engineering and Design Group, School of Engineering Sciences, University of Southampton, Southampton SO17 1BJ, UKajk@soton.ac.uk

P. B. Nair

Computational Engineering and Design Group, School of Engineering Sciences, University of Southampton, Southampton SO17 1BJ, UKpbn@soton.ac.uk

A. R. Rao

Turbines System Engineering, Rolls-Royce plc, Derby DE24 8BJ, UKabhijit.rao@rolls-royce.com

PS=pressureside, SS=suctionside, LE=leadingedge, CE=center, and TE=trailingedge.


Corresponding author.

J. Mech. Des 132(12), 121005 (Nov 30, 2010) (9 pages) doi:10.1115/1.4002806 History: Received April 21, 2010; Revised October 12, 2010; Published November 30, 2010; Online November 30, 2010

This paper addresses the problem of analyzing measurement data to estimate the variations in turbine blade life in the presence of manufacturing variability. A methodology that employs existing denoising techniques, namely, Principal Component Analysis and Fast Fourier Transform analysis, is proposed for filtering measurement error from the measured data set. An approach for dimensionality reduction is employed that uses prior knowledge on the measurement error obtained from analyzing repeated measurements. The proposed methodology also helps in capturing the effects of manufacturing drift with time and the blade to blade manufacturing error. The filtered data is then used for generating three-dimensional representations of probable manufactured blade shapes from the limited number of available measurements. This is accomplished by using a Free-Form Deformation based approach for deforming a nominal mesh to the desired shapes. Estimations of life on the probable turbine blade shapes manufactured over a span of 1 year indicate a reduction of around 1.7% in the mean life relative to the nominal life, with a maximum relative reduction of around 3.7%, due to the effects of manufacturing variability.

Copyright © 2010 by American Society of Mechanical Engineers
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Figure 1

A typical turbine blade model with internal cooling core shape

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Figure 2

Flowchart representation of the proposed methodology for denoising measurement data, estimating geometric variability from limited measurements and calculating life for the probable manufactured blade shapes

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Figure 3

(a) A typical turbine blade shape showing the tip, mid, and root planes and (b) cross-sectional thickness measurement locations across each measurement plane

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Figure 4

Variance plot for dimensionality reduction using PCA on the measurement data

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Figure 5

(a) Measurements ordered according to the time of blade manufacture, (b) smoothed measurement data when TM=4, (c) smoothed measurement data when TM=6, and (d) smoothed measurement data when TM=10

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Figure 6

Results obtained from (a) PCA analysis and (b) FFT analysis compared with the mean of threshold thicknesses and nominal thickness values

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Figure 7

(a) Lattice control point grid enclosing the core, (b) nominal core with no deformation, (c) deformed core with increased leg thickness, and (d) deformed core with decreased leg thickness

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Figure 8

Histogram indicating the effect of manufacturing variability on normalized gas turbine blade life



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