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Evaluation of the utility of static and adaptive mesh refinement for idealized tropical cyclone problems in a spectral element shallow water model

机译:谱元浅水模型中理想化热带气旋问题的静态和自适应网格细化实用性评估

摘要

The utility of static and adaptive mesh refinement (SMR and AMR, respectively) are examined for idealized tropical cyclone (TC) simulations in a spectral element f-plane shallow water model. The SMR simulations have varying sizes of the statically refined meshes (geometry-based) while the AMR simulations use a potential vorticity (PV) threshold to adaptively refine the mesh to the evolving TC. Numerical simulations are conducted for four cases: (i) TC-like vortex advecting in a uniform flow, (ii) binary vortex interaction, (iii) barotropic instability of a PV ring, and (iv) barotropic instability of a thin strip of PV. For each case, a high resolution “truth” simulation is compared to two different SMR simulations and three different AMR simulations for accuracy and efficiency. The multiple SMR and AMR simulations have variations in the number of fully-refined elements in the vicinity of the TC. For these idealized cases, it is found that the SMR and AMR simulations are able to resolve the vortex dynamics as well as the “truth” runs, with no significant loss in accuracy in the refined region in the vortex vicinity and with significant speed-ups (factor of 2-5). The overall accuracy is enhanced by a greater area of fully refined mesh in both the SMR and AMR simulations. While these results are highly idealized, they demonstrate the potential for SMR and AMR for the numerical simulation of TCs in three dimensions and more complex models.
机译:检验了静态和自适应网格细化(分别为SMR和AMR)在频谱元素f平面浅水模型中用于理想热带气旋(TC)模拟的效用。 SMR仿真具有可变大小的静态精炼网格(基于几何),而AMR仿真使用潜在涡度(PV)阈值将网格自适应地精炼为不断变化的TC。针对以下四种情况进行了数值模拟:(i)均匀流动的TC型涡流,(ii)二元涡旋相互作用,(iii)PV环的正压不稳定性和(iv)PV薄带的正压不稳定性。对于每种情况,将高分辨率“真相”仿真与两个不同的SMR仿真和三个不同的AMR仿真进行比较,以提高准确性和效率。多个SMR和AMR模拟在TC附近的完全精炼元素数量上有所变化。对于这些理想情况,我们发现SMR和AMR仿真能够解决涡旋动力学以及“真相”运行,在涡旋附近精炼区域的精度没有明显损失,并且提速明显。 (2-5的系数)。在SMR和AMR仿真中,通过更大范围的完全精炼的网格可以提高整体精度。尽管这些结果非常理想,但它们证明了SMR和AMR在三维和更复杂模型中进行TC数值模拟的潜力。

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