The scope of this paper is to demonstrate the viability of unstructured anisotropic mesh adaptation for commercial aircraft drag and high-lift prediction studies. The main achievement of this work is to demonstrate that mesh-independent certified numerical solutions can be achievable thanks to anisotropic mesh adaptation and that it is possible to run high-fidelity CFD on unstructured adapted meshes composed only of tetrahedra which is fundamental to design robust meshing process for complex geometries. It also points out the early capturing property of the solution-adaptive process in the sense that accurate output functional values are obtained on relatively coarse adapted meshes. On a more practical point of view, this paper demonstrates how mesh adaptation, thanks to its automation, is able to generate meshes that are extremely difficult to envision and almost impossible to generate manually, leading to highly accurate numerical solutions. Moreover, as the process can start from any coarse initial mesh, it greatly simplifies the overall meshing process. This study also analyze the influence of different strategies in the mesh adaptation algorithm and in the error analysis which are key components of the process. Several error estimates are considered: feature-based ones which are based on the standard multiscale $L^p$ interpolation error estimate and goal-oriented ones to control the error on output functionals which rely on an accurate computation of the adjoint state. The adjoint problem proves to be a stiff problem for RANS equations, failing to converge the adjoint state to machine zero may impact negatively the adaptive process. The maturity of the solution-adaptive process is demonstrated on numerous drag and high-lift prediction cases. It has also excelled in sonic boom and turbomachine applications.

本文的范围旨在证明非结构化各向异性网格自适应技术在商用飞机阻力和高升力预测研究中的可行性。这项工作的主要成果是证明，由于各向异性的网格自适应，可以实现独立于网格的认证数值解决方案，并且可以在仅由四面体组成的非结构化自适应网格上运行高保真CFD，这对于设计鲁棒的网格划分至关重要复杂几何形状的过程。从在相对较粗的自适应网格上获得准确的输出功能值的意义上，它还指出了解决方案自适应过程的早期捕获特性。从更实际的角度来看，本文演示了网格自适应的实现，这要归功于它的自动化，能够生成极其难以想象且几乎无法手动生成的网格，从而获得高度精确的数值解。而且，由于该过程可以从任何粗略的初始网格开始，因此大大简化了整个网格划分过程。这项研究还分析了网格调整算法和误差分析中不同策略的影响，它们是该过程的关键组成部分。考虑了几种误差估计：基于特征的误差估计，基于标准多尺度 这项研究还分析了不同策略在网格自适应算法和误差分析中的影响，它们是过程的关键组成部分。考虑了几种误差估计：基于特征的误差估计，基于标准多尺度 这项研究还分析了网格调整算法和误差分析中不同策略的影响，它们是该过程的关键组成部分。考虑了几种误差估计：基于特征的误差估计，基于标准多尺度${\mathrm{大号}}^p$插值误差估计和面向目标的误差估计方法是控制输出函数的误差，这些函数依赖于伴随状态的精确计算。对于RANS方程，伴随问题被证明是一个刚性问题，未能将伴随状态收敛到机器零可能会对自适应过程产生负面影响。在众多阻力和高升力预测案例中证明了解决方案自适应过程的成熟性。它还在音爆和涡轮机械应用方面表现出色。