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.
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e-mail: nt1f06@soton.ac.uk
e-mail: ajk@soton.ac.uk
e-mail: pbn@soton.ac.uk
e-mail: abhijit.rao@rolls-royce.com
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December 2010
Research Papers
Probabilistic Life Assessment of Gas Turbine Blades
Nikita Thakur,
Nikita Thakur
Computational Engineering and Design Group, School of Engineering Sciences,
e-mail: nt1f06@soton.ac.uk
University of Southampton
, Southampton SO17 1BJ, UK
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A. J. Keane,
A. J. Keane
Computational Engineering and Design Group, School of Engineering Sciences,
e-mail: ajk@soton.ac.uk
University of Southampton
, Southampton SO17 1BJ, UK
Search for other works by this author on:
P. B. Nair,
P. B. Nair
Computational Engineering and Design Group, School of Engineering Sciences,
e-mail: pbn@soton.ac.uk
University of Southampton
, Southampton SO17 1BJ, UK
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A. R. Rao
A. R. Rao
Turbines System Engineering,
e-mail: abhijit.rao@rolls-royce.com
Rolls-Royce plc
, Derby DE24 8BJ, UK
Search for other works by this author on:
Nikita Thakur
Computational Engineering and Design Group, School of Engineering Sciences,
University of Southampton
, Southampton SO17 1BJ, UKe-mail: nt1f06@soton.ac.uk
A. J. Keane
Computational Engineering and Design Group, School of Engineering Sciences,
University of Southampton
, Southampton SO17 1BJ, UKe-mail: ajk@soton.ac.uk
P. B. Nair
Computational Engineering and Design Group, School of Engineering Sciences,
University of Southampton
, Southampton SO17 1BJ, UKe-mail: pbn@soton.ac.uk
A. R. Rao
Turbines System Engineering,
Rolls-Royce plc
, Derby DE24 8BJ, UKe-mail: abhijit.rao@rolls-royce.com
J. Mech. Des. Dec 2010, 132(12): 121005 (9 pages)
Published Online: November 30, 2010
Article history
Received:
April 21, 2010
Revised:
October 12, 2010
Online:
November 30, 2010
Published:
November 30, 2010
Citation
Thakur, N., Keane, A. J., Nair, P. B., and Rao, A. R. (November 30, 2010). "Probabilistic Life Assessment of Gas Turbine Blades." ASME. J. Mech. Des. December 2010; 132(12): 121005. https://doi.org/10.1115/1.4002806
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