对流近地面层大尺度对流的能见度网络分析turbulence
Visibility network analysis of large-scale intermittency in convective surface layer turbulence
摘要：
大尺度不稳定性是对流表面层湍流中广泛观察到的现象，其引起非高斯温度统计，而对于速度信号则没有观察到这种特征。虽然基于概率密度函数的方法已被使用到目前为止，这些都不能解释在何种程度上的信号的时间结构的影响的速度和温度波动的统计特性。为了解决这个问题，能见度网络分析进行了现场实验数据集从对流大气近地层流。通过替代数据和基于网络的措施，我们证明了温度的不稳定性与温度信号中的强非线性相关性有关。相反，线性和非线性效应之间的竞争往往会抑制流向和垂直速度中的类似温度的不稳定性行为。这些发现为对流湍流大尺度不稳定性的研究开辟了新的研究途径。
英文摘要：
Large-scale intermittency is a widely observed phenomenon in convective surface layer turbulence that induces non-Gaussian temperature statistics, while such signature is not observed for velocity signals. Although approaches based on probability density functions have been used so far, those are not able to explain to what extent the signals' temporal structure impacts the statistical characteristics of the velocity and temperature fluctuations. To tackle this issue, a visibility network analysis is carried out on a field-experimental dataset from a convective atmospheric surface layer flow. Through surrogate data and network-based measures, we demonstrate that the temperature intermittency is related to strong non-linear dependencies in the temperature signals. Conversely, a competition between linear and non-linear effects tends to inhibit the temperature-like intermittency behaviour in streamwise and vertical velocities. Based on present findings, new research avenues are likely to be opened up in studying large-scale intermittency in convective turbulence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
