For large Reynolds number flows, it is typically necessary to perform simulations that are under-resolved with respect to the underlying flow physics. For nodal discontinuous spectral element approximations of these under-resolved flows, the collocation projection of the nonlinear flux can introduce aliasing errors which can result in numerical instabilities. In Dzanic and Witherden (J. Comput. Phys., 468, 2022), an entropy-based adaptive filtering approach was introduced as a robust, parameter-free shock-capturing method for discontinuous spectral element methods. This work explores the ability of entropy filtering for mitigating aliasing-driven instabilities in the simulation of under-resolved turbulent flows through high-order implicit large eddy simulations of a NACA0021 airfoil in deep stall at a Reynolds number of 270,000. It was observed that entropy filtering can adequately mitigate aliasing-driven instabilities without degrading the accuracy of the underlying high-order scheme on par with standard anti-aliasing methods such as over-integration, albeit with marginally worse performance at higher approximation orders.

翻译：对于高雷诺数流动，通常需要执行相对于底层流动物理解析度不足的模拟。在针对这些欠解析流动的节点不连续谱元近似中，非线性通量的配点投影可能引入混叠误差，进而导致数值不稳定性。在Dzanic与Witherden（J. Comput. Phys., 468, 2022）的研究中，提出了一种基于熵的自适应滤波方法，作为不连续谱元法的一种鲁棒、无参数的激波捕捉技术。本文通过NACA0021翼型在雷诺数270,000深度失速状态下的高阶隐式大涡模拟，探讨了熵滤波在缓解欠解析湍流模拟中由混叠驱动的失稳方面的能力。实验表明，熵滤波能够充分缓解混叠驱动的失稳现象，且其精度与过积分等标准抗混叠方法不相上下，但在更高近似阶次下性能略逊一筹。