Visualizing turbulence anisotropy in the spatial domain with componentality contours By
Visualizing turbulence anisotropy in the spatial domain with componentality contours By
复制标题
使用分量轮廓可视化空间域中的湍流各向异性
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
G. Iaccarino
中科院分区：
文献类型：
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作者：
M. Emory;G. Iaccarino
定量信息的可视化表示在流体工程中被证明是非常有用的。在实验中，可压缩流的纹影图像和低速流的染料/烟雾条纹（Van Dyke 1982）是如何观察到物理行为和重要系统动力学的生动例子，使分析人员更好地理解流动结构。计算流体动力学（CFD）在很大程度上依赖于视觉表示来从数值模拟产生的大量数据中提取理解和探索。无论是使用q准则（Dubief & Delcayre 2000）来研究涡旋还是绘制速度剖面，可视化地表示定量数据的能力都是数值模拟分析的关键方面。湍流的各向异性行为就是这样一个经常被可视化的量，这是复杂流体流动的一个共同特征。在许多工程应用中，正确预测各向异性的数量和类型对于这些流动的精确数值模拟至关重要。在实践中，湍流建模界使用了雷诺应力各向异性张量的各种特性
Visual representations of quantitative information have proven very useful in the context of fluids engineering. In experiments Schlieren imagery for compressible flows and dye/smoke streaks for low-speed flows (Van Dyke 1982) are vivid examples of how physical behaviors and important system dynamics can be observed, giving the analyst a better understanding of the flow structures. Computational fluid dynamics (CFD) relies just as heavily on visual representations to extract understanding from and explore the large amounts of data generated by numerical simulations. Whether using the Q-criterion (Dubief & Delcayre 2000) to investigate vortices or plotting of velocity profiles, the ability to visually represent quantitative data is a critical aspect of numerical simulation analysis. One such frequently visualized quantity is the anisotropic behavior of turbulence, a common feature of complex fluid flows. In many engineering applications, properly predicting the amount and type of anisotropy is critical for accurate numerical simulation of these flows. In practice, the turbulence modeling community uses various properties of the Reynolds stress anisotropy tensor