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横流中液体射流雾化的液滴尺寸分布统计分析

Statistical Analysis of Droplet Size Distributions in Liquid-Jet-in-Crossflow Atomization

Tom Johny, Bharat Bhatia, Zafar Alam, Ashoke De

arXiv 2608.03030首次发表:更新:

AI 中文总结

本研究采用VOF-LPT耦合框架模拟LJICF雾化,分析多参数对液滴尺寸的影响,明确分布规律及两种分布的适用性,为雾化特性研究提供支撑。

AI 中文摘要

本研究探究了在不同动量通量比、韦伯数及流动条件下,注入横流(LJICF)的液体射流的液滴尺寸分布(DSD)与雾化特性。采用经过验证的可压缩体积流体-拉格朗日粒子跟踪(VOF-LPT)耦合框架开展数值模拟,以捕捉一次雾化与二次雾化过程。分析了动量通量比、韦伯数、横流压力及速度等关键参数对下游区域液滴尺寸特性的影响,包括索特平均直径(SMD)与标准差(STD)。包含概率密度与累积分布的离散液滴尺寸分布显示,在破碎条件增强时,液滴会向更细小、更均匀的方向转变。研究结果强调气动力与不稳定性对高效雾化的关键作用,较高的动量通量比与韦伯数会产生更细小、更均匀的液滴。横流压力升高会促进更细小液滴的形成,但由于喷雾羽流受限、粒子转化延迟及液滴停留时间缩短,下游区域的液滴密度会降低。对数正态分布与罗辛-拉姆勒分布可有效捕捉液滴尺寸趋势,前者能较好表征偏度与尾部行为,后者可准确表征中等尺寸及较大尺寸液滴,但两者分别在复刻尖锐峰值与最小液滴尺寸方面存在局限性。

英文摘要

This study investigates the droplet size distribution (DSD) and atomization characteristics of a liquid jet injected into a crossflow (LJICF) under varied momentum flux ratios, Weber numbers, and flow conditions. Numerical simulations are performed using the validated compressible Volume of Fluid-Lagrangian Particle Tracking (VOF-LPT) coupled framework to capture both the primary and secondary atomization processes. Key parameters, including momentum flux ratio, Weber number, crossflow pressure, and velocity, were analyzed to assess their impact on droplet size characteristics, including Sauter mean diameter (SMD) and standard deviation (STD) in the downstream region. The discrete size distribution of droplets comprising probability density and cumulative distribution reveals a shift toward finer, more uniform droplets under enhanced breakup conditions. The findings emphasize the critical role of aerodynamic forces and instabilities in driving efficient atomization, with higher momentum flux ratios and Weber numbers leading to finer and more uniform droplets. Increased crossflow pressure promotes finer droplet formation but is found to reduce droplet density in the downstream domain due to a confined spray plume, delayed particle conversion, and reduced droplet residence time. The log-normal and Rosin-Rammler distributions effectively capture droplet size trends, with the former closely representing the skewness and tail behavior and the latter accurately representing intermediate and larger droplets. However, both have limitations in replicating sharp peaks and the smallest droplet sizes, respectively.

Journal refPhysics of Fluids, 2025, 37, 083391

DOI:10.1063/5.0283083

论文原文

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