• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2026, Vol. 62 ›› Issue (4): 377-391.doi: 10.3901/JME.260133

• 交叉与前沿 • 上一篇    

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压力旋流式雾化喷嘴流场分析与结构优化

袁晓明, 肖浩阳, 徐新宇, 张杰, 宋具宝, 刘存飞   

  1. 燕山大学起重机械关键技术全国重点实验室 秦皇岛 066004
  • 收稿日期:2024-09-10 修回日期:2025-10-08 发布日期:2026-04-02
  • 作者简介:袁晓明(通信作者),男,1984年出生,博士,副教授,博士研究生导师。主要研究方向为消防水炮机液耦合动力学,新型液压元件开发,消防车长管路系统内部流场分析与优化。E-mail:yuanxiaoming@ysu.edu.cn
  • 基金资助:
    国家自然科学基金青年基金(52206054)、中国博士后科学基金(2020M683525)和陕西省自然科学基金面上(2021JM-328)资助项目。

Flow Field Analysis and Structural Optimization of Pressure Swirl Atomizing Nozzle

YUAN Xiaoming, XIAO Haoyang, XU Xinyu, ZHANG Jie, SONG Jubao, LIU Cunfei   

  1. National Key Laboratory of Hoisting Machinery Key Technology, Yanshan University, Qinhuangdao 066004
  • Received:2024-09-10 Revised:2025-10-08 Published:2026-04-02

摘要: 雾化喷嘴是液压传动系统的末端执行元件,可将水或油等流体介质进行雾化,其雾化性能对消防灭火、农药喷洒和降尘清洁等应用场景的工作效率有显著影响。以一种典型压力旋流式雾化喷嘴为研究对象,采用VoF to DPM(VtD)多相流模型、Realizable k-ε湍流模型和网格自适应技术建立流场仿真模型,分析喷嘴雾化原理,探究在不同入口压力下喷嘴雾化锥角和雾滴平均粒径的变化规律。以喷嘴旋流槽宽度、旋流槽数量、喷嘴直管段长度和喷嘴直径为设计变量,喷嘴雾化锥角和雾滴平均粒径为目标变量设计正交试验,结合神经网络与非支配排序遗传算法,优化喷嘴关键结构参数。优化后喷嘴的雾化锥角为64.14°,平均粒径为0.102 mm,相比于初始设计模型,分别提升了8.6%和5.6%。经试验验证,雾化锥角和系统流量的仿真与试验结果最大误差分别为6.2%和6.6%。本研究可为喷嘴类产品的雾化射流机理研究提供理论支持。

关键词: 雾化喷嘴, VtD模型, 正交试验, 遗传算法, 结构优化

Abstract: Atomizing nozzle is the end execution element of hydraulic transmission system, which can atomize fluid media such as water or oil. Its atomization performance significantly impacts the work efficiency in application scenarios such as firefighting, pesticide spraying, and dust reduction. This study focuses on a typical pressure-swirl atomizing nozzle. Using the VoF to DPM (VtD) multiphase model, the Realizable k-ε turbulence model, and the grid adaptive technology, a flow field simulation model is established to analyze the atomization principle of the nozzle and explore the variation of the nozzle atomization spray angle and droplet mean diameter under different inlet pressures. With the swirl groove width, number of swirl grooves, length of the nozzle straight pipe section, and nozzle diameter as design variables, and the nozzle atomization cone angle and droplet mean diameter as objective variables, an orthogonal experiment is designed. Combining neural networks with the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the key structural parameters of the nozzle are optimized. The atomization spray angle of the optimized nozzle is 64.14°, and the droplet mean diameter is 0.102 mm, which are 8.6% and 5.6% higher than the initial design model, respectively. The maximum errors between the simulation and experimental results of the atomization cone angle and system flow rate are 6.2% and 6.6%, respectively. This research can provide theoretical support for the study of atomizing jet mechanisms of nozzle products.

Key words: atomizing nozzle, VtD model, orthogonal test, genetic algorithm, structure optimization

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