论文标题
捕获湍流中的速度梯度和颗粒旋转速率
Capturing Velocity Gradients and Particle Rotation Rates in Turbulence
论文作者
论文摘要
湍流流具有复杂的小规模结构，并经常发生极端速度梯度。探测这种旋转和紧张区域的颗粒以复杂的形状依赖性定向动力学做出响应，这敏感地取决于粒子历史。在这里，我们系统地开发了一个小型湍流动力学的减少阶模型，该模型捕获了沿粒子路径的速度梯度统计。对所得随机动力学系统的分析允许指出非高斯统计数据的出现以及涡度和应变的非平地时间相关性，如先前从实验和模拟中报道的那样。基于这些见解，我们使用模型来预测湍流中各向异性颗粒的定向统计，从而实现了复杂颗粒流的大量建模应用。
Turbulent fluid flows exhibit a complex small-scale structure with frequently occurring extreme velocity gradients. Particles probing such swirling and straining regions respond with an intricate shape-dependent orientational dynamics, which sensitively depends on the particle history. Here, we systematically develop a reduced-order model for the small-scale dynamics of turbulence, which captures the velocity gradient statistics along particle paths. An analysis of the resulting stochastic dynamical system allows pinpointing the emergence of non-Gaussian statistics and non-trivial temporal correlations of vorticity and strain, as previously reported from experiments and simulations. Based on these insights, we use our model to predict the orientational statistics of anisotropic particles in turbulence, enabling a host of modeling applications for complex particulate flows.
