中文摘要：

为快速识别流型的类型，提出微细通道纳米流体气液两相流流型K—means聚类识别的方法，该方法采用高速摄像机获取微细通道内气液两相流的流型图像，利用灰度流型图像的直方图获得峰值并且该峰值作为K—means聚类的初始中心点，结合不变矩原理和欧氏距离进行相似度流型图像的识别。由查准率-查全率评估体系和5500幅流型图像识别实验的执行耗时分析结果表明：采用K—means聚类对微细通道纳米流体气液两相流流型进行识别的整体识别率达到97．8％，其中弹状和泡状识别率为100％。该方法为微细通道纳米流体两相流的在线识别流型提供了一种新途径。

英文摘要：

A novel approach for identification of flow pattern of micro-channel nanofluid gas liquid two-phase flow was presented based on K-means for the purpose of improving the accuracy and efficiency of flow patterns identification. The proposed flow pattern identification method acquired the whole flow pattern images of the gas - liquid two-phase flow of micro-channel with high-speed camera firstly. In the second place, peak values which were obtained by histogram of gray scale, flow pattern images were thought of as the original center point of K-means clustering. As for the final step, similarity identification of different flow pattern images was carried out with the principles of invariant moment theory and Euclidean distance. The accuracy and efficiency of the proposed flow pattern identification method were demonstrated with the precision-ratio and recall-ratio assessment system as well as time-consuming analysis results of fifty five hundred pieces of flow pattern images identification experiment. Experimental results showed that the overall identification rate of the new flow pattern identification method based on K-means clustering was 97.8%, while the identification rate of slug flow was up to 100% and that of bubble flow was able to reach 100% as well. The new method provided a novel perspective for the online identification of flow pattern of micro-channel nanofluid two-phase flow.