基于电容层析成像系统测量信号稀疏性的两相流流型辨识

提出了基于电容层析成像(ECT)测量电容信号稀疏性的两相流流型辨识算法,该算法首先使用所有流型对应的归一化测量电容值信号构建一个过完备字典,并将待辨识样本通过该过完备字典进行稀疏表示,使其具有稀疏性并满足稀疏重构的基本要求,然后以压缩感知的正交匹配追踪(OMP)算法求取各标准样本对应于完备样本集的稀疏解,最后根据待辨识样本与标准样本稀疏解之间的线性相关程度进行流型辨识。使用该方法对5种典型的两相流流型识别进行了仿真及实验研究,结果表明:该方法的流型正确识别率均高于98%。

Identification of Two-phase Flow Pattern Based on the Sparsity of Measured Capacitance for Electrical Capacitance Tomography

The flow pattern identification algorithm based on the sparsity of measured capacitance for electrical capacitance tomography (ECT) is proposed. Firstly, the over-complete dictionary of normalized capacitance measurement for investigated flow patterns is built, with which the sparse representation of the sample can be obtained. And then, the basic requirements of sparse reconstruction can be met. The orthogonal matching pursuit (OMP) algorithm is used to calculate the sparse solution of each standard sample using the over-complete dictionary.Finally, the flow pattern is identified according to the linear correlation between the sample to be identified and the sparse solution of the standard sample. Simulation and static experiments are carried out for the five typical two-phase flow patterns, and the correct identification rate is higher than 98%.