基于灰色神经网络和支持向量机的两相流流型辨识  

Methods for discriminating gas-liquid two phase flow patterns based on gray neural networks and SVM

作　　者：李精精[1] 周涛[1] 段军[1] 张蕾[1] 

机构地区：[1]华北电力大学核热工安全与标准化研究所,北京102206

出　　处：《核技术》2013年第2期67-71,共5页Nuclear Techniques

基　　金：国家自然科学基金(50976033);华北电力大学校内"211"基金资助

摘　　要：两相流流型直接影响两相流的流动特性和传热传质性能。利用小波分析对气液两相流压降实验数据进行处理,提取不同频率的小波系数。以小波能量为特征,输入BP神经网络进行训练,进行流型的初步辨识。将灰色神经网络模型应用于气液两相流的辨识,同时创立将压差波动数据和小波能量数据输入Lib-SVM机分类器的方法,分别对流型进行辨识。结果显示,这三种方法均可进行流型的辨识,小波能量支持向量机的判别结果比灰色神经网络和BP神经网络的判别结果准确。支持向量机对压差信号直接进行流型辨识时准确率达到95.2%。Background： The flow patterns of two phase flow will directly influence the heat transfer and mass transfer of the flow. Purpose： By wavelet analysis of the pressure drop experimental data, the wavelet coefficients of different frequency can be obtained. Methods： Get the wavelet energy and then train them in the model of BP neural network to distinguish the flow patterns. Introduced the implant gray neural networks model and use it for the two phase flow for the first time. At the same time, set up the method of training the pressure data and wavelet energy data in the support vector machine. Results： Through treatment of the gray layer, the result of the neural network is more accuracy. It can obviously reduce the effect of data marginalization. The accuracy of the pressure drop Lib-SVM method is 95.2%. Conclusions： The results show that these three methods can make a distinction among the different flow patterns and the Lib-SVM method gets the best result, then the gray neural networks, and at last the BP neural networks.

关 键 词：小波分析 两相流 流型 灰色神经网络 Lib-SVM 

分 类 号：TL249[核科学技术—核燃料循环与材料]