摘要
传统内插式吸气消声器腔室之间密封性差,影响消声器消声性能和阻力特性,为解决这一问题,设计一种新结构吸气消声器,通过初步实验对比,证明新吸气消声器腔室密封性好,且具有较好的声学性能和阻力特性,可使压缩机声功率级降低1.87 d B,制冷量提高7.4 W,性能系数提高0.016。为进一步提高新消声器的消声性能,利用声学分析软件LMS Virtual. Lab进行声学仿真模拟,分析其引流管长度、引流管通流截面宽度、扩张孔位置和出口内插管长度等内部结构参数对传递损失的影响,优化内部结构参数。最终,压缩机声功率级降低2.51 dB,制冷量提高5.63 W,性能系数提高0.015。
The poor sealing between the chambers of the traditional inserted suction mufflers seriously affects the mufflers’ performances and resistance characteristics. In order to solve this problem, a new structure of suction mufflers is designed. Through the preliminary experimental comparison with the traditional inserted suction muffler, it is proved that the new suction muffler has good sealing performance, good acoustic performance and resistance characteristics. The sound power level of the compressor is reduced by 1.87 dB, the refrigeration capacity is increased by 7.4 W, and the coefficient of performance is raised by 0.016. In order to further improve the silencing performance of the new muffler, the acoustic analysis software LMS Virtual.Lab is used to simulate the acoustic performance of the new muffler. The influence of the internal structure parameters such as the length of the drainage tube, the width of the flow section of the drainage tube, the position of the expansion hole and the length of the outlet intubation on the transmission loss is analyzed, and the internal structure parameters are optimized. Finally, the sound power level of the compressor is again reduced by 2.51 dB, the refrigeration capacity is again increased by 5.63 W, and the performance coefficient is again increased by 0.015.
作者
张则刚
高丽
孙海滨
兰同宇
吴伟
ZHANG Zegang;GAO Li;SUN Haibin;LAN Tongyu;WU Wei(College of Mechanical and Electronic Engineering,Shangdong Univesity of Science and Technology,Qingdao 266590,Shangdong,China;QingdaoWanbao Compressor Co.,Ltd.,Qingdao 266590,Shangdong,China)
出处
《噪声与振动控制》
CSCD
北大核心
2022年第2期231-235,共5页
Noise and Vibration Control
关键词
声学
压缩机
吸气消声器
腔室泄露
传递损失
结构优化
acoustics
compressor
suction muffler
cavity leakage
transmission loss
structure optimization
