Li Huajun, Ji Haifeng, Huang Zhiyao, Wang Baoliang, Li Haiqing, Wu Guohua
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
Key Laboratory of Complex Systems Modeling and Simulation of Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel). 2016 Jan 27;16(2):159. doi: 10.3390/s16020159.
Based on a laser diode, a 12 × 6 photodiode array sensor, and machine learning techniques, a new void fraction measurement method for gas-liquid two-phase flow in small channels is proposed. To overcome the influence of flow pattern on the void fraction measurement, the flow pattern of the two-phase flow is firstly identified by Fisher Discriminant Analysis (FDA). Then, according to the identification result, a relevant void fraction measurement model which is developed by Support Vector Machine (SVM) is selected to implement the void fraction measurement. A void fraction measurement system for the two-phase flow is developed and experiments are carried out in four different small channels. Four typical flow patterns (including bubble flow, slug flow, stratified flow and annular flow) are investigated. The experimental results show that the development of the measurement system is successful. The proposed void fraction measurement method is effective and the void fraction measurement accuracy is satisfactory. Compared with the conventional laser measurement systems using standard laser sources, the developed measurement system has the advantages of low cost and simple structure. Compared with the conventional void fraction measurement methods, the proposed method overcomes the influence of flow pattern on the void fraction measurement. This work also provides a good example of using low-cost laser diode as a competent replacement of the expensive standard laser source and hence implementing the parameter measurement of gas-liquid two-phase flow. The research results can be a useful reference for other researchers' works.
基于激光二极管、12×6光电二极管阵列传感器和机器学习技术，提出了一种用于小通道气液两相流空隙率测量的新方法。为克服流型对空隙率测量的影响，首先通过Fisher判别分析（FDA）识别两相流的流型。然后，根据识别结果，选择由支持向量机（SVM）建立的相关空隙率测量模型来实现空隙率测量。开发了一种用于两相流的空隙率测量系统，并在四个不同的小通道中进行了实验。研究了四种典型流型（包括泡状流、弹状流、分层流和环状流）。实验结果表明测量系统的开发是成功的。所提出的空隙率测量方法是有效的，空隙率测量精度令人满意。与使用标准激光源的传统激光测量系统相比，所开发的测量系统具有成本低、结构简单的优点。与传统的空隙率测量方法相比，所提出的方法克服了流型对空隙率测量的影响。这项工作还提供了一个很好的例子，即使用低成本的激光二极管作为昂贵标准激光源的有效替代品，从而实现气液两相流参数的测量。研究结果可为其他研究人员的工作提供有益参考。
