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Comparison of dynamic subgrid-scale models for simulations of neutrally buoyant shear-driven atmospheric boundary layer flows

机译:模拟中性浮力驱动的大气边界层流动的动态亚网格规模模型的比较

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摘要

Several non-dynamic, scale-invariant, and scale-dependent dynamic subgrid-scale (SGS) models are utilized in large-eddy simulations of shear-driven neutral atmospheric boundary layer (ABL) flows. The popular Smagorinsky closure and an alternative closure based on Kolmogorov's scaling hypothesis are used as SGS base models. Our results show that, in the context of neutral ABL regime, the dynamic modeling approach is extremely useful, and reproduces several establised results (e.g., the surface layer similarity theory) with fidelity. The scale-dependence framework, in general, improves the near-surface statistics from the Smagorinsky model-based simulations. We also note that the local averaging-based dynamic SGS models perform significantly better than their planar averaging-based counterparts. Lastly, we find more or less consistent superiority of the Smagorinsky-based SGS models (over the corresponding Kolmogorov's scaling hypothesis-based SGS models) for predicting the inertial range scaling of spectra.
机译:在剪切驱动的中性大气边界层(ABL)流的大涡模拟中,利用了几种非动态的,尺度不变的和依赖尺度的动态亚网格尺度(SGS)模型。流行的Smagorinsky闭包和基于Kolmogorov缩放假设的替代闭包用作SGS基础模型。我们的结果表明,在中性ABL机制下,动态建模方法非常有用,并且可以忠实地再现一些已确定的结果(例如,表面层相似性理论)。通常,比例依赖框架可通过基于Smagorinsky模型的仿真来改善近地表统计信息。我们还注意到,基于局部平均的动态SGS模型的性能明显优于其基于平面平均的模型。最后,我们发现基于Smagorinsky的SGS模型(相对于相应的Kolmogorov的缩放假设基于SGS的模型)或多或少具有一致的优势,可以预测光谱的惯性范围缩放。

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