Aerostructural wing shape optimization assisted by algorithmic differentiation. Articles uri icon

publication date

  • August 2021

start page

  • 739

end page

  • 760

issue

  • 2

volume

  • 64

International Standard Serial Number (ISSN)

  • 1615-147X

Electronic International Standard Serial Number (EISSN)

  • 1615-1488

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.

keywords

  • adjoint sensitivities; aeroelasticity; aerostructural optimization; algorithmic differentiation; computational fluid dynamics; open-source suite su2