摘要
在线识别的关键问题在于如何利用最短时间历程的参数波动过程完成由参数波动的特征空间到流型空间的映射。CPN神经网络能够对自组织映射的结果进行有导师的训练 ,因此可以为流型的自动、客观和在线识别提供有效的手段。文中结合压力波动过程的快速傅立叶变换系数 ,对U形管垂直上升段内空气 -水两相流的流型进行自动识别 ,在基金项目 :国家重点基础研究发展纲要资助项目(G19990 2 2 30 8) ;国家 86 3海洋高技术资助项目 (82 0 0 1)。ProjectSupportedbyFoundationoftheNationalProgramforPrior ityBasicResearch’sDevelopment(G19990 2 2 30 8) ;TheNationalHighTechnologyResearchandDevelopmentofChina (86 3Program) (82 0 0 1) .线性指标达到了 8.2s。
It has important industrial background and scientific significance to study the online recognition of gas liquid two phase flow regime.The key problem of the online recognition is how to map to the space of flow regimes from the space of character of the parameter fluctuations with the shortest time.Counter propagation network (CPN) can train the results of self organizes mapping with supervising.At the same time, it takes advantage of simple algorithm and doesn't require lots of training samples.Therefore, it meets the online and automatic recognition of gas liquid two phase flow regimes.In the present test case, the online criterion was 8.2s with the FFT of pressure fluctuations from the vertical upward section of the U type tube.The presented technology and theory has some obvious advantages, which provides a feasible solution of online recognition without databank of flow regime and can overcome partially the problems induced by not enough experiments in advance.In addition, the problems to shorten the identification time and to increase the identifying possibility are discussed in the paper.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2001年第7期46-50,共5页
Proceedings of the CSEE
基金
国家重点基础研究发展纲要资助项目(G19990 2 2 30 8)
国家 86 3海洋高技术资助项目 (82 0 0 1)&&
关键词
气液两相流
流型
在线智能识别
神经网络
gas liquid two phase flow
flow regime
online recognition
neural network
