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Algorithmic differentiation of an industrial airfoil design tool coupled with the adjoint CFD method

  • Mladen BanovićEmail author
  • Ilias Vasilopoulos
  • Andrea Walther
  • Marcus Meyer
Research Article
  • 23 Downloads

Abstract

Computer Aided Design (CAD) systems and tools are considered essential for industrial design. They construct and manipulate the geometry of a certain component with an arbitrary set of design parameters. However, it is a challenging task to incorporate the parametric definition in a gradient-based shape optimization loop, since the CAD libraries usually do not provide shape sensitivities w.r.t. the design parameters of the model to be optimized. Typically, these derivatives are evaluated with inaccurate finite differences. On the contrary, to obtain the exact derivative information, algorithmic differentiation (AD) can be applied if the CAD sources are available. In this study, the Rolls-Royce in-house airfoil design and blade generation tool Parablading is differentiated using the AD software tools ADOL-C and Tapenade. The differentiated CAD tool is coupled with a discrete adjoint CFD solver that is part of the Rolls-Royce in-house HYDRA suite of codes, also produced by algorithmic differentiation. This differentiated design chain is used to perform gradient-based shape optimization of the TU Berlin TurboLab stator test-case w.r.t.  minimize the total pressure loss and exit angle deviation objectives.

Keywords

Algorithmic differentiation Industrial CAD tool Adjoint CFD method Gradient-based optimization 

Notes

Acknowledgements

The authors are very thankful to Dr.-Ing. Peter Flassig and Dr.-Ing. André Huppertz (Rolls-Royce Deutschland) for their support related to the Parablading tool and its parametrization principles. This research is part of the IODA Project—Industrial Optimal Design using Adjoint CFD. IODA is Marie Skłodowska-Curie Innovative Training Network funded by the European Union’s Horizon 2020 research and innovation programme under the Grant Agreement No. 642959.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Mladen Banović
    • 1
    Email author
  • Ilias Vasilopoulos
    • 3
  • Andrea Walther
    • 2
  • Marcus Meyer
    • 3
  1. 1.Paderborn UniversityPaderbornGermany
  2. 2.Humboldt University of BerlinBerlinGermany
  3. 3.Rolls-Royce Deutschland Ltd & Co KGBlankenfelde-MahlowGermany

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