The recently proposed high-order TENO scheme [Fu et al., Journal of Computational Physics, 305, pp.333-359] has shown great potential in predicting complex fluids owing to the novel weighting strategy, which ensures the high-order accuracy, the low numerical dissipation, and the sharp shock-capturing capability. However, the applications are still restricted to simple geometries with Cartesian or curvilinear meshes. In this work, a new class of high-order shock-capturing TENO schemes for unstructured meshes are proposed. Similar to the standard TENO schemes and some variants of WENO schemes, the candidate stencils include one large stencil and several small third-order stencils. Following a strong scale-separation procedure, a tailored novel ENO-like stencil selection strategy is proposed such that the high-order accuracy is restored in smooth regions by selecting the candidate reconstruction on the large stencil while the ENO property is enforced near discontinuities by adopting the candidate reconstruction from smooth small stencils. The nonsmooth stencils containing genuine discontinuities are explicitly excluded from the final reconstruction, leading to excellent numerical stability. Different from the WENO concept, such unique sharp stencil selection retains the low numerical dissipation without sacrificing the shock-capturing capability. The newly proposed framework enables arbitrarily high-order TENO reconstructions on unstructured meshes. For conceptual verification, the TENO schemes with third- to sixth-order accuracy are constructed. Without parameter tuning case by case, the performance of the proposed TENO schemes is demonstrated by examining a set of benchmark cases with broadband flow length scales.
翻译:最近提议的高顺序TENO计划[Fu等人,《计算物理杂志》,305, pp.333-359]表明,由于新的加权战略确保了高顺序准确性、低数字消散率和惊人的冲击捕捉能力,因此在预测复杂流体方面潜力巨大。但是,应用程序仍然局限于使用Cartesian 或curvilinear meshes的简单地貌。在这项工作中,提出了一个新的类别,即为无结构的meshes提出了高层次冲击采集TENO计划。与标准的TENO计划和WENO计划的一些变量类似,候选人的精度精度包括一个大的精度精度和几个小三级的精度精度精度战略。经过了强大的缩放程序,提出了定制的精度精度精度精度选择战略选择策略的精度,从而恢复了平坦的区域的精度,而ENO财产则通过采用不精度的精度模型来进行不精确的精度重建,而没有精度的精度结构的精度框架,从而排除了精确的精确的精度结构。