摘要
引射器是目前家用燃气热水器、壁挂炉中燃烧器的重要组成部件,承担着两种以上介质相互引射及其混合的关键任务。首先对燃气燃烧器用的两级引射器进行了结构设计和优化,并对设计出的两种两级引射器进行了二维、稳态的数值模拟,探究了两级引射器内燃气和空气进行动量及质量交换的混合过程,采用质量引射系数和出口甲烷质量分数标准差系数来分别表征引射器的引射性能和混合性能,研究了引射器结构参数和运行参数对引射器性能的影响。研究表明,两种两级引射器均可以引射超过化学当量比的空气,第一级引射器混合段的长度存在最优值使引射器质量引射系数达到最大,当引射器其他参数相同时,优化后的两级引射器在不同背压下引射性能均有提升,在不同背压下混合性能有所降低,背压越高引射性能提升程度越大,混合性能降低程度越小。
The ejector is an important part of the current atmospheric burner,and it undertakes the key task of ejecting and mixing two or more media with each other.This paper first designed and optimized the structure of the two-stage ejector for the gas burner,and carried out two-dimensional,steady-state numerical simulations of the two designed two-stage ejectors.The mixing process of momentum and mass exchange of gas and air uses the mass ejection coefficient and the standard deviation coefficient of the outlet methane mass fraction to characterize the ejection performance and mixing performance of the ejector.The structure parameters and operating parameters of the ejector are studied.Studies have shown that two types of two-stage ejectors can eject air that exceeds the stoichiometric ratio.There is an optimal value for the length of the first-stage mixing section to maximize the mass flow rate ejecting coefficient of the ejector.When the other parameters of the ejector are the same,the optimized two-stage ejector's ejection performance is improved under different back pressure,and the mixing performance is reduced under different back pressures.The higher the back pressure,the greater the improvement in ejection performance,and the smaller the decrease in mixing performance.
作者
倪永涛
赵钦新
桂雍
王云刚
邵怀爽
NI Yongtao;ZHAO Qinxin;GUI Yong;WANG Yungang;SHAO Huaishuang(Department of Thermal Engineering,School of Energy and Power Engineering,Xi'an Jiaotong University,Xi’an 710049,Shaanxi,China)
出处
《化工进展》
EI
CAS
CSCD
北大核心
2020年第S01期69-76,共8页
Chemical Industry and Engineering Progress
基金
西安市科技计划(201922111KYPT002JC004)。
关键词
两级引射器
混合
结构设计
数值分析
优化
two-stage ejector
mixing
structural design
numerical analysis
optimization