论文标题
通过算法分化辅助的航空结构机翼形状优化
Aerostructural Wing Shape Optimization assisted by Algorithmic Differentiation
论文作者
论文摘要
凭借更有效的结构,航空学的最后趋势见证了翅膀的灵活性,呼吁采用适当的设计和优化方法。为了正确建模耦合物理学,航空结构优化逐渐变得越来越重要,如今所执行的也考虑了更高的纪律方法,即用于空气动力学的CFD和结构的FEM。在本文中,提出了一种基于空气动力学和结构非线性在内的高保真梯度的空气结构优化的方法。该方法的主要关键特征是其模块化:每个纪律求解器,独立地采用算法分化来评估基于伴随的敏感性,都通过包装器在高层上连接,以解决空气结构的原始问题并评估耦合问题问题的确切离散梯度。实施的功能,为证明该方法而创建的临时功能,并在开源SU2多物理套件中自由使用,用于根据ONERA M6和NASA CRM机翼对气体弹性测试用例进行航空结构优化。采用Euler或Rans流量模型进行单点优化,以在空气动力学效率方面找到最佳的外部模具线。结果指出,在执行机翼形状优化时考虑到航空结构耦合的重要性。
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 paper a methodology for high-fidelity gradient-based aerostructural optimization of wings, including aerodynamic and structural nonlinearities, is presented. The main key feature of the method is its modularity: each discipline solver, independently 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 exact discrete gradients of the coupled problem. The implemented capability, ad-hoc created to demonstrate the methodology, and freely available within the open-source SU2 multiphysics suite, 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.