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Towards Adaptive Grids for Atmospheric Boundary-Layer Simulations

机译:面向大气边界层模拟的自适应网格

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摘要

We present a proof-of-concept for the adaptive mesh refinement method applied to atmospheric boundary-layer simulations. Such a method may form an attractive alternative to static grids for studies on atmospheric flows that have a high degree of scale separation in space and/or time. Examples include the diurnal cycle and a convective boundary layer capped by a strong inversion. For such cases, large-eddy simulations using regular grids often have to rely on a subgrid-scale closure for the most challenging regions in the spatial and/or temporal domain. Here we analyze a flow configuration that describes the growth and subsequent decay of a convective boundary layer using direct numerical simulation (DNS). We validate the obtained results and benchmark the performance of the adaptive solver against two runs using fixed regular grids. It appears that the adaptive-mesh algorithm is able to coarsen and refine the grid dynamically whilst maintaining an accurate solution. In particular, during the initial growth of the convective boundary layer a high resolution is required compared to the subsequent stage of decaying turbulence. More specifically, the number of grid cells varies by two orders of magnitude over the course of the simulation. For this specific DNS case, the adaptive solver was not yet more efficient than the more traditional solver that is dedicated to these types of flows. However, the overall analysis shows that the method has a clear potential for numerical investigations of the most challenging atmospheric cases.
机译:我们提出了适用于大气边界层模拟的自适应网格细化方法的概念验证。对于在空间和/或时间上具有高度比例分离的大气流的研究,这种方法可以形成静态网格的有吸引力的替代方案。例如,昼夜周期和对流边界层被强反演覆盖。对于这种情况,使用规则网格的大涡模拟通常必须依赖于子网格规模的封闭,才能解决空间和/或时间域中最具挑战性的区域。在这里,我们使用直接数值模拟(DNS)分析描述对流边界层生长和随后衰减的流动配置。我们验证所获得的结果,并使用固定的规则网格针对两次运行对自适应求解器的性能进行基准测试。看起来,自适应网格算法能够动态地粗化和细化网格,同时保持精确的解决方案。特别地,在对流边界层的初始生长期间,与随后的湍流衰减阶段相比,需要高分辨率。更具体地说,网格单元的数量在模拟过程中变化了两个数量级。对于这种特定的DNS情况,自适应求解器的效率尚未比专用于这些类型的流的更传统的求解器更高。但是,整体分析表明,该方法具有对最具挑战性的大气情况进行数值研究的明显潜力。

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