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.

中文翻译：

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伴随CFD方法的工业机翼设计工具的算法区分

计算机辅助设计（CAD）系统和工具被认为是工业设计必不可少的。他们使用一组任意的设计参数来构造和操纵某个组件的几何形状。但是，将参数定义并入基于梯度的形状优化循环中是一项艰巨的任务，因为CAD库通常不提供要优化模型的设计参数的形状敏感性。通常，对这些导数的评估存在不精确的有限差异。相反，要获得确切的导数信息，可以在CAD源可用的情况下应用算法区分（AD）。在这项研究中，使用AD软件工具ADOL-C和Tapenade对劳斯莱斯内部机翼设计和叶片生成工具Parablading进行了区分。差异化的CAD工具与离散的伴随CFD求解器相结合，该求解器是劳斯莱斯内部HYDRA代码套件的一部分，也是通过算法差异化产生的。这种差异化的设计链用于对柏林工业大学进行基于梯度的形状优化TurboLab定子测试箱可最大程度地减少总压力损失和出口角度偏差目标。