Probabilistic endurance level analyses of compressor blades

• Published in: CEAS aeronautical journal 2012-04, Vol.3 (1), p.55-65
• Main Authors: Heinze, Kay, Schrape, Sven, Voigt, Matthias, Vogeler, Konrad
• Format: Article
• Language: English
• Subjects: Aerospace Technology and Astronautics; Engineering; Original Paper
• ISSN: 1869-5582; 1869-5590
• DOI: 10.1007/s13272-012-0043-y

The geometric parameters of manufactured compressor blades deviate from the nominal design intent due to manufacturing capabilities. The impact of these deviations on the mechanical strength and the aeroelastic behaviour of the compressor blades is almost unknown. This unknown impact is currently covered with reserve factors and conservative assumptions. This paper presents a workable approach to consider geometric deviations of compressor blades, within the design process, using probabilistic methods. Several compressor blades were 3D-scanned with an optical scan system. The polygon meshes of the scanned blades were parameterised. The probability density functions of all geometric parameters were calculated. A process chain was developed to morph a validated FE mesh according to the geometric deviations. The probabilistic investigation was carried out with a Latin-Hypercube Monte Carlo Simulation. Realisations were generated by using the developed process chain. These realisations represent the produced compressor blade geometries. Results of a Rolls-Royce aeroelasticity in-house code were considered based on nominal design predictions. The determination of aerodynamic damping and aerodynamic forcing enables to investigate the scatter of the aero-mechanical behaviour in principle. Additionally, the impact of geometric deviations can be analysed. Finally, a detailed discussion of the results determines the characteristic of the system. The scatter of the eigenfrequencies, the modeshapes and the aero-mechanical behaviour of the compressor blades is statistically analysed. Correlations between input parameters and result variables are investigated. Statistical analysis tools, i.e. probability plot, help to detect weak spots in the design. The intermediate-term objective of subsequent investigations is to avoid cost-intensive redesign solutions by designing a more robust compressor blade.