湍流中可变形液滴的分散与聚类
Dispersion and clustering of deformable droplets in turbulence
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中文总结 AI 辅助
本研究以航空喷雾燃烧为背景，采用HIT框架结合LPT方法，探究不同斯托克斯数液滴变形对湍流中分散与聚类的影响，发现变形影响依惯性 regime 变化，需考虑非稳态形状振荡以准确预测相关特性。
中文摘要 AI 辅助
受航空工业喷雾燃烧应用的驱动，本研究探究湍流中非球形液滴的分散特性。喷雾建模最常用的策略依赖于LPT方法，该方法将喷雾表示为离散的球形粒子集合。LPT的一个局限在于忽略了液滴变形对喷雾动力学的影响。已有研究强调了非球形在液滴蒸发、燃烧及阻力系数中的重要性，但这些研究仅限于均匀流中孤立液滴等理想化构型。为在更真实的构型中研究液滴变形，本研究采用均匀各向同性湍流（HIT）作为框架，探究其对液滴分散的影响。研究了不同斯托克斯数的液滴，以考察变形与惯性之间的相互作用。对液滴统计量的分析表明，液滴变形对分散和聚类的影响取决于惯性 regime。对于弱惯性液滴，变形会削弱分散和优先聚集；而对于强惯性液滴，变形倾向于增强优先聚集，同时削弱分散。结果还表明，为达到相同的聚类水平，变形液滴需要更高的斯托克斯数。有趣的是，对于无惯性液滴，变形似乎会诱导出有效惯性。这一点通过完整非稳态TAB模型与其稳态极限的对比得到验证，该对比表明，非稳态形状动力学影响时间相关统计量，但不改变平均聚类模式。这些发现表明，考虑液滴变形及其非稳态形状振荡，对于准确预测湍流中液滴的分散与聚类至关重要。
英文摘要
Motivated by the application of spray combustion in aviation industry, this work investigates the dispersion of non-spherical droplets in turbulence. The most common strategy for modeling sprays relies on LPT method, which represents the spray as a discrete collection of spherical particles. One limitation of LPT is that it neglects the influence of droplet deformation on spray dynamics. Prior studies have highlighted the importance of non-sphericity in droplet vaporization, combustion and drag coefficient. However, these works are restricted to idealized configurations such as an isolated droplet in a uniform flow. To study droplet deformation in a more realistic configuration, we adopt homogeneous isotropic turbulence (HIT) as the framework to investigate its effect on droplet dispersion. Droplets of various Stokes number are studied to investigate the interplay between deformation and inertia. Analysis of droplet statistics reveals that the impact of droplet deformation on both dispersion and clustering is dependent on the inertia regime. For weakly-inertial droplets, deformation weakens both dispersion and preferential concentration, whereas for strongly-inertial droplets, deformation tends to enhance preferential concentration while weakening dispersion. The results also suggest that to achieve the same level of clustering, deformed droplets require a higher Stokes number. Interestingly, for non-inertial droplets, the deformation seems to induce an effective inertia. This is verified by a comparison between the full unsteady TAB model and its steady-state limit, which suggests that unsteady shape dynamics affect temporal correlation statistics, but leave the mean clustering pattern unchanged. These findings demonstrate that accounting for droplet deformation and its unsteady shape oscillation is essential for accurately predicting droplet dispersion and clustering in turbulence.
