超越平均流：一种用于揭示雾收集网中液滴捕获物理机制的谱动力学方法
Beyond the Mean-flow: A Spectral-Dynamic Approach to Unraveling the Physics of Droplet Capture in Fog Harvesting Meshes
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中文总结 AI 辅助
本研究提出谱动力学框架，通过双向耦合欧拉-拉格朗日模型揭示雾收集网液滴捕获机制，引入动态匹配参数Π，为网几何优化提供设计指导。
中文摘要 AI 辅助
采用网式收集器的雾收集效率由液滴惯性与几何诱导流结构之间的复杂相互作用决定。尽管以往研究主要依赖平均流度量，但本研究引入了一种谱动力学框架，以探究对液滴捕获有重要影响却常被忽视的控制因素。采用双向耦合欧拉-拉格朗日模型模拟含液滴（粒径2-40微米）的流体流过五种代表性网几何结构。结果表明，捕获效率不仅与速度波动的幅值相关，还与其频谱分布和持续时间相关。频域分析显示，网几何结构会在孔隙与障碍物区域重新分配波动能量，从而定义出特征流动时间尺度。通过将该流动时间尺度与液滴响应时间进行比较，引入了动态匹配参数Π=液滴响应时间/流动时间尺度。当Π为1量级时，捕获效率最高，对应近网区域内液滴与流动的持续相互作用。产生宽带、适度放大频谱内容的几何结构（如三角形网）会增加液滴停留时间和拦截概率，而波动较弱或高度局部化的几何结构则会因激励不足或过早绕流而降低性能。基于此条件，提出了一种受物理启发的捕获效率关联式。因此，本研究为雾收集网的几何优化提供了基于机制的设计指导，而非确定的最优方案。
英文摘要
Fog harvesting efficiency with mesh collectors is governed by complex interactions between droplet inertia and geometry-induced flow structures. Although previous studies have primarily relied on mean-flow metrics, the present work introduces a spectral-dynamic framework to examine an important but often overlooked control on droplet capture. A two-way coupled Eulerian-Lagrangian model is used to simulate droplet-laden flow (2-40 microm) across five representative mesh geometries. The results show that capture efficiency correlates not only with the magnitude of velocity fluctuations, but also with their spectral distribution and persistence. Frequency-domain analysis indicates that mesh geometry redistributes fluctuation energy across pore and obstruction regions, thereby defining a characteristic flow timescale. By comparing this flow timescale with the droplet response time, a dynamic matching parameter, Π= droplet response time/flow time scale, is introduced. The highest capture efficiency occurs when Π is order unity, corresponding to sustained droplet-flow interaction in the near-mesh region. Geometries that generate broadband, moderately amplified spectral content (e.g., triangular mesh) increase droplet residence time and interception probability, whereas geometries with either weak or highly localized fluctuations reduce performance through insufficient forcing or premature bypass. A physics-inspired correlation for capture efficiency is proposed based on this condition. The study therefore provides mechanistic design guidance, rather than a definitive optimum, for geometry optimization in fog-harvesting meshes.
