Lagrangian Tracking Simulation of Droplet Growth in Turbulence–Turbulence Enhancement of Autoconversion Rate*
Lagrangian Tracking Simulation of Droplet Growth in Turbulence–Turbulence Enhancement of Autoconversion Rate*
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湍流中液滴生长的拉格朗日跟踪模拟 - 湍流增强自动转换率*
DOI：
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发表时间：
2015
期刊：
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通讯作者：
Keiko Takahashi
中科院分区：
文献类型：
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作者：
R. Onishi;K. Matsuda;Keiko Takahashi
作者介绍了拉格朗日云模拟器（LCS），它模拟了空气湍流中的液滴生长。LCS采用欧拉-拉格朗日框架，可以通过单独跟踪粒子的生长来为云微物理模式提供参考数据。停滞流中的碰撞增长是由LCS和求解随机碰撞聚并方程（SCE）计算的。LCS和SCE模拟之间取得了良好的协议。停滞和湍流的结果之间的比较证实，在云湍流增强碰撞增长。如果采用合适的碰撞模型，SCE方法可以很好地预测这种增强。为了量化增强，本文定义了自动转换过程的时间尺度，其中云滴通过碰撞成长为雨滴，作为10%的云变成雨所需的时间（t10%）。然后，作者定义湍流增强因子Eturb为，其中overbar表示平均值。
AbstractThe authors describe the Lagrangian cloud simulator (LCS), which simulates droplet growth in air turbulence. The LCS adopts the Euler–Lagrangian framework and can provide reference data for cloud microphysical models by tracking the growth of particles individually. The collisional growth in a stagnant flow is calculated by the LCS and also by solving the stochastic collision–coalescence equation (SCE). Good agreement is obtained between the LCS and SCE simulations. Comparisons between the results for stagnant and turbulent flows confirm that in-cloud turbulence enhances collisional growth. The enhancement is well predicted by the SCE method if a proper collision model is employed. To quantify the enhancement, the paper defines the time scale of the autoconversion process, in which cloud droplets grow into raindrops through collisions, as the time taken for 10% of the cloud to become rain (t10%). The authors then define the turbulence enhancement factor Eturb as , where the overbar denotes the mea...