传统的电阻层析成像(ERT)传感器皆采用静态结构,无法满足在两相流实时检测的过程中对ERT传感器根据流型的变化进行自适应式优化的智能化要求.针对这一问题,本文分析了流型对软场特性的影响,提出一种具有动态结构的自适应ERT传感器.该ERT传感器的电极排列采用阵列式结构,并将流型识别技术引入ERT传感器设计,提出一种基于信号稀疏性的ERT流型识别方法,采用信号的稀疏表示方法将ERT系统的采样电压表示为稀疏性组合,并求出其稀疏解用以实现对不同的流型进行分类.流型识别信息的引入,使得该传感器具有根据实时的流型变化,自适应地动态调整传感器结构,优化传感器性能的智能化功能.实验表明,该传感器可以自动识别芯流、泡状流、层流和环流等4种典型流型,识别率分别为91％、93％、90％、88％.针对不同流型,经动态优化后的传感器可使ERT图像重建的质量显著提高.%The all traditional electrical resistance tomography (ERT) sensors have a static structure, which cannot satisfy the intelligent requirements for adaptive optimization to ERT sensors that is subject to flow pattern changes during the real-time detection of two-phase flow. In view of this problem, an adaptive ERT sensor with a dynamic structure is proposed. The electrodes of the ERT sensor are arranged in an array structure, the flow pattern recognition technique is introduced into the ERT sensor design and accordingly an ERT flow pattern recognition method based on signal sparsity is proposed. This method uses the sparse representation of the signal to express the sampling voltage of the ERT system as a sparse combination and find its sparse solution to achieve the classification of different flow patterns. With the introduction of flow identification information, the sensor has an intelligent function of adaptively and dynamically adapting the sensor structure according to the real-time flow pattern change. The experimental results show that the sensor can automatically identify four typical flow patterns: core flow, bubble flow, laminar flow and circulation flow with recognition rates of 91％, 93％, 90％ and 88％ respectively. For different flow patterns, the dynamically optimized sensor can significantly improve the quality of ERT image reconstruction.
展开▼
