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：对于大雷诺数流动，通常需要执行相对于底层流动物理欠解析的模拟。针对这些欠解析流动的节点间断谱元逼近，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（《计算物理学杂志》，468卷，2022年）的研究中，提出了一种基于熵的自适应滤波方法，作为间断谱元法鲁棒且无参数的激波捕捉技术。本研究通过NACA0021翼型在雷诺数270,000深失速条件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混叠驱动不稳定性方面的能力。结果表明：熵滤波能有效缓解混叠驱动的不稳定性，且不降低底层高阶格式的精度，其性能与过积分等标准抗混叠方法相当，但在高阶逼近精度下表现略逊一筹。