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Toward Affordable Uncertainty Quantification for Industrial Problems: Part II — Turbomachinery Application

[+] Author Affiliations
Tiziano Ghisu

University of Cagliari, Cagliari, Italy

Shahrokh Shahpar

Rolls-Royce plc, Derby, UK

Paper No. GT2017-64845, pp. V02CT47A021; 14 pages
  • ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition
  • Volume 2C: Turbomachinery
  • Charlotte, North Carolina, USA, June 26–30, 2017
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-5080-0
  • Copyright © 2017 by Rolls-Royce plc


Uncertainty Quantification (UQ) is an increasingly important area of research. As components and systems become more efficient and optimized, the impact of uncertain parameters (such as boundary and operating conditions, geometrical variations due to manufacturing or in-service deterioration, etc.) is likely to become critical. It is therefore fundamental to consider the impact of these uncertainties as early as possible during the design process, with the aim of producing more robust designs (less sensitive to the presence of uncertainties). The cost of UQ with high-fidelity simulations becomes therefore of fundamental importance.

The companion part I paper presented an efficient approach for UQ based on Polynomial Chaos expansions, demonstrating its applicability to multi-fimensional uncertainty quantification problems. This paper focuses on the application of this approach to quantify the variability in the performance of two large bypass-ratio jet engine fans in the presence of shape uncertainty due to possible manufacturing processes. The impacts of shape uncertainty on the two geometries are compared, and sensitivities to the location of the blade shape variability are extracted. The mechanisms at the origin of the change in performance are analyzed in detail, as well as the differences between the two configurations. These results provide important information both for controling the manufacturing process, and for designing blades that are less sensitive to the presence of manufacturing uncertainties.

Copyright © 2017 by Rolls-Royce plc



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