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Aerostructural wing shape optimization assisted by algorithmic differentiation

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Abstract

With more efficient structures, last trends in aeronautics have witnessed an increased flexibility of wings, calling for adequate design and optimization approaches. To correctly model the coupled physics, aerostructural optimization has progressively become more important, being nowadays performed also considering higher-fidelity discipline methods, i.e., CFD for aerodynamics and FEM for structures. In this work a model for high-fidelity gradient-based aerostructural optimization of wings, assisted by algorithmic differentiation and including aerodynamic and structural nonlinearities, is presented. First, the model is illustrated: a key feature lies in its enhanced modularity. Each discipline solver, employing algorithmic differentiation for the evaluation of adjoint-based sensitivities, is interfaced at high-level by means of a wrapper to both solve the aerostructural primal problem and evaluate discrete-consistent gradients of the coupled problem. Second, to demonstrate the feasibility of the method, a framework is ad hoc set up, within the open-source SU2 multiphysics suite, with the inclusion of a geometrically nonlinear beam FE and an interface module to deal with non-matching 3D surfaces. Finally, the framework is applied to perform aerostructural optimization of aeroelastic test cases based on the ONERA M6 and NASA CRM wings. Single-point optimizations, employing Euler or RANS flow models, are carried out to find wing optimal outer mold line in terms of aerodynamic efficiency. Results remark the importance of taking into account the aerostructural coupling when performing wing shape optimization.

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Acknowledgements

Part of the simulations were executed on the high performance cluster “Elwetritsch” at TU Kaiserslautern, which is part of the Alliance for High Performance Computing in Rhineland-Palatinate (AHRP). The authors would like to thank Dr. Beckett Y. Zhou and Guillermo Suàrez of the Chair for Scientific Computing of TU Kaiserslautern for their assistance.

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Authors and Affiliations

  1. Department of Bioengineering and Aerospace Engineering, Universidad Carlos III de Madrid (UC3M), Madrid, Spain

    Rocco Bombardieri & Rauno Cavallaro

  2. SU2 Foundation, San Francisco, CA, USA

    Ruben Sanchez

  3. Chair for Scientific Computing, TU Kaiserslautern, Kaiserslautern, Germany

    Nicolas R. Gauger

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  1. Rocco Bombardieri
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  2. Rauno Cavallaro
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  3. Ruben Sanchez
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  4. Nicolas R. Gauger
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Correspondence to Rauno Cavallaro.

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Replication of results

The employed framework is currently on GitHub in the branch feature_pyBeam_ShapeDesignV2 of SU2 repository and will soon be available in the official release of the suite.

PyBeam organization on GitHub provides the complete set of test cases discussed above in the repository SAMO_testcases.

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Responsible Editor: Joaquim R. R. A. Martins

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Bombardieri, R., Cavallaro, R., Sanchez, R. et al. Aerostructural wing shape optimization assisted by algorithmic differentiation. Struct Multidisc Optim 64, 739–760 (2021). https://doi.org/10.1007/s00158-021-02884-5

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  • DOI: https://doi.org/10.1007/s00158-021-02884-5

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