滤波器(信号处理)
信号(编程语言)
谐波
衰减
材料科学
声学
微波食品加热
波导管
插入损耗
光学
波导滤波器
功率(物理)
滤波器设计
物理
电气工程
原型滤波器
计算机科学
电信
工程类
量子力学
程序设计语言
作者
Haixu Liu,Hou Man-Hong,Xinsheng Li
出处
期刊:Chinese Physics
[Acta Physica Sinica, Chinese Physical Society and Institute of Physics, Chinese Academy of Sciences]
日期:2018-01-01
卷期号:67 (19): 198401-198401
被引量:1
标识
DOI:10.7498/aps.67.20180577
摘要
A gradient leaky-wall waveguide loaded absorbing load filter structure is proposed, which is designed for harmonic suppression in super-high power transmitter of deep-space probe. The attenuation loss characteristics of the filter is analyzed according to the equivalent circuit method, and the massive structure is simulated by the electromagnetic field simulation software. The filter sample which includes one main waveguide, 288 deputy waveguides and 288 absorb loads is processed following the simulating and designing sizes. In order to prevent microwave from leaking and keep good air tightness under the condition of high power, all the components of the filter will be welded together by means of vacuum welding, and then the sample is cleaned ultrasonically. Finally, the filter sample is tested under small signal and large signal separately. According to our test results, the pass band max insertion loss of the filter is 0.3 dB, the min suppression of second harmonic is 75 dB, the min suppression of third harmonic is 50 dB, and the min suppression of fourth harmonic is 35 dB. The measured results show that they are almost the same as the simulation results, and consistent completely with the anticipated. We further conduct the high power experiment on the filter under a large signal of 100 kW, showing that the continuous wave power capacity of the filter can reach up to 100 kW through the power resistance test with the liquid-cooled system. All the test data show that the study and development are very successful. At present, the filer has been applied to a type of ground high power transmitter, and its performances and indicators behave well.
科研通智能强力驱动
Strongly Powered by AbleSci AI