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计算流体力学（CFD）模拟至关重要。《CFD 2030愿景研究》强调了该技术的迫切需求，特别是针对超级计算机的模拟。本研究将细粒度推测性方法应用于各向异性网格操作。在多核节点上，我们的实现展现出超过90%的并行效率。此外，我们在一系列公开测试案例的自适应流程中评估了该方法，涵盖解析导出场与基于误差场。在所有测试案例中，我们的结果均与文献中已发表的结果一致。本文引入了对基于CAD数据的支持，并在NASA高升力预测研讨会案例之一中验证了其有效性。