Algorithmic differentiation of an industrial airfoil design tool coupled with the adjoint CFD method
- 23 Downloads
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.
KeywordsAlgorithmic differentiation Industrial CAD tool Adjoint CFD method Gradient-based optimization
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.
- Auriemma S, Banović M, Walther A, Mykhaskiv O, Müller JD (2018) Applications of differentiated CAD kernel in gradient-based aerodynamic shape optimisation. In: 2018 joint propulsion conference. American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2018-4828
- Bestle D, Flassig P (2010) Optimal aerodynamic compressor blade design considering manufacturing noise. In: 8th Association for Structural and Multidisciplinary Optimization in the UK/International Society for Structural and Multidisciplinary Optimization (ASMO-UK/ISSMO) conference on engineering design optimization, LondonGoogle Scholar
- Dannenhoffer J, Haimes R (2015) Design sensitivity calculations directly on CAD-based geometry. In: 53rd AIAA aerospace sciences meeting, AIAA SciTech Forum. AIAA 2015-1370Google Scholar
- Griewank A, Walther A (2008) Evaluating derivatives: principles and techniques of algorithmic differentiation, 2nd edn. Society for Industrial MathematicsGoogle Scholar
- Lapworth L (2004) Hydra-CFD: a framework for collaborative CFD development. In: International conference on scientific and engineering computation (IC-SEC)Google Scholar
- Müller JD (2018) AboutFlow benchmark test-case: TU Berlin TurboLab stator. http://aboutflow.sems.qmul.ac.uk/events/munich2016/benchmark/testcase3/. Accessed 23 Oct 2018
- Shahpar S, Lapworth L (2003) PADRAM: parametric design and rapid meshing system for turbomachinery optimisation. In: ASME Turbo ExpoGoogle Scholar
- Vasilopoulos I, Flassig P, Meyer M (2017) CAD-based aerodynamic optimization of a compressor stator using conventional and adjoint-driven approaches. In: ASME Turbo ExpoGoogle Scholar
- Walther A, Griewank A (2012) Getting started with ADOL-C. Dagstuhl seminar proceedings 09061, pp 181–202Google Scholar
- Xu S, Radford D, Meyer M, Müller JD (2015) CAD-based adjoint shape optimisation of a one-stage turbine with geometric constraints. In: ASME Turbo Expo 2015 2C: TurbomachineryGoogle Scholar