Ansaldo Energia Switzerland's validation approach for vibrational behaviour of gas turbine compressor blading is a proven but costly multi-step process. An enhanced process relying on less and early-stage measurements only while retaining prediction quality would enable substantial savings. Accordingly, an analysis of possible variations of measurement data available during all validation phases is performed in order to confirm observed and expected correlations on a large scale, facilitating measurement chain improvement and simplification.

Profile deviation and frequency distribution analysis finds coordinate-measuring machine (CMM) data and statically measured eigenfrequencies to be normally distributed. Correlation analysis exposes strong autocorrelations of CMM and static frequency data and distinct linear correlations between them. Consistent correlations with and within rotating machinery cannot be established with the limited data basis at hand as clamping conditions of blades with hammer foot roots introduce an additional degree of variation.

CMM data are employed for meta-models translating geometry deviations into static-test-alike frequency information. Average aerofoil thickness deviation is identified as the decisive parameter for eigenfrequency uncertainties founded in geometry deviations.

A stage statistics based approach for frequency forecasting in rotating machinery is proposed, allowing the calculation of expected eigenfrequency bands with corresponding confidence levels. Brought up to operational levels it would allow more efficient forecasting of in-engine conditions and constitute a valuable means for exploring compressor blading design space.

Keywords: HCF validation, uncertainty quantification, surrogate modelling


A Statistical Study on HCF Validation Data for Axial Gas Turbine Compressor Blades

Division / Sponsor:
International Gas Turbine Institute (IGTI)

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2020/volume/84058

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