Efficient and robust anisotropic mesh adaptation is crucial for Computational Fluid Dynamics (CFD) simulations. The CFD Vision 2030 Study highlights the pressing need for this technology, particularly for simulations targeting supercomputers. This work applies a fine-grained speculative approach to anisotropic mesh operations. Our implementation exhibits more than 90% parallel efficiency on a multi-core node. Additionally, we evaluate our method within an adaptive pipeline for a spectrum of publicly available test-cases that includes both analytically derived and error-based fields. For all test-cases, our results are in accordance with published results in the literature. Support for CAD-based data is introduced, and its effectiveness is demonstrated on one of NASA's High-Lift prediction workshop cases.

翻译：高效且稳健的各向异性网格自适应对于计算流体力学（CFD）模拟至关重要。《CFD 2030年愿景研究》强调了该技术的迫切需求，尤其是针对超算的模拟场景。本研究将细粒度推测式方法应用于各向异性网格操作。我们的实现方案在多核节点上展现出超过90%的并行效率。此外，我们在包含解析推导场与基于误差场的公开测试案例谱系中，对自适应流水线内的算法进行了评估。所有测试案例的结果均与文献已发表成果一致。研究引入了基于CAD数据的支持，并通过NASA高升力预测研讨会的一个案例验证了其有效性。