An adaptive electrical resistance tomography sensor with flow pattern recognition capability

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...

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Bibliographic Details
Published in:Journal of Central South University 2019-03, Vol.26 (3), p.612-622
Main Authors: Wang, Pai, Li, Yang-bo, Wang, Mei, Qin, Xue-bin, Liu, Lang
Format: Article
Language:English
Subjects:
Core flow
Electrical resistance
Engineering
Flow resistance
Image quality
Image reconstruction
Laminar flow
Metallic Materials
Optimization
Pattern recognition
Real time
Sensor arrays
Sensors
Tomography
Two phase flow
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Summary: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.
ISSN:2095-2899
2227-5223
DOI:10.1007/s11771-019-4032-8