首页|Multi-Physics Coupled Acoustic-Mechanics Analysis and Synergetic Optimization for a Twin-Fluid Atomization Nozzle

Multi-Physics Coupled Acoustic-Mechanics Analysis and Synergetic Optimization for a Twin-Fluid Atomization Nozzle

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Fine particulate matter produced during the rapid industrialization over the past decades can cause significant harm to human health.Twin-fluid atomization technology is an effective means of controlling fine particulate matter pollution.In this paper,the influences of the main parameters on the droplet size,effective atomization range and sound pressure level(SPL)of a twin-fluid nozzle(TFN)are investigated,and in order to improve the atomization performance,a multi-objective synergetic optimization algorithm is presented.A multi-physics coupled acoustic-mechanics model based on the discrete phase model(DPM),large eddy simulation(LES)model,and Ffowcs Williams-Hawkings(FW-H)model is established,and the numerical simulation results of the multi-physics coupled acoustic-mechanics method are verified via experimental comparison.Based on the analysis of the multi-physics coupled acoustic-mechanics numerical simulation results,the effects of the water flow on the characteristics of the atomization flow distribution were obtained.A multi-physics coupled acoustic-mechanics numerical simulation result was employed to establish an orthogonal test database,and a multi-objective synergetic optimization algorithm was adopted to optimize the key parameters of the TFN.The optimal parameters are as follows:A gas flow of 0.94 m3/h,water flow of 0.0237 m3/h,orifice diameter of the self-excited vibrating cavity(SVC)of 1.19 mm,SVC orifice depth of 0.53 mm,distance between SVC and the outlet of nozzle of 5.11 mm,and a nozzle outlet diameter of 3.15 mm.The droplet particle size in the atomization flow field was significantly reduced,the spray distance improved by 71.56％,and the SPL data at each corresponding measurement point decreased by an average of 38.96％.The conclusions of this study offer a references for future TFN research.

Twin-fluid nozzleBP neural networkMulti-objective optimizationMulti-physics coupledAcoustic-mechanics analysisGenetic algorithm

Wenying Li、Yanying Li、Yingjie Lu、Jinhuan Xu、Bo Chen、Li Zhang、Yanbiao Li

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College of Mechanical Engineering,Zhejiang University of Technology,Hangzhou 310023,China

State Key Laboratory of Fluid Power and Mechatronic Systems,Zhejiang University,Hangzhou 310027,China

School of Materials Science and Engineering,Northwestern Polytechnical University,Xian 710072,China

Guizhou Anda Aviation Forging Co.,Ltd.,Anshun 561005,China

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National Natural Science Foundation of ChinaZhejiang Provincial Natural Science Foundation of ChinaChina Postdoctoral Science FoundationChina Postdoctoral Science FoundationOpen Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems of ChinaStudents in Zhejiang Province Science and Technology Innovation Plan of China

U21A20122LY22E0500122023T1605802023M743102GZKF-2022252023R403073

2024

10.1186/s10033-024-01037-1

中国机械工程学报
中国机械工程学会

中国机械工程学报

CSTPCD
影响因子：0.765
ISSN：1000-9345
年,卷(期)：2024.37(3)
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