Increasingly advanced technology allows the monitoring of complex systems from a wide variety of perspectives. But the exploration of such systems from a multi-channel sensor information viewpoint remains a complicated challenge of ongoing interest. In this paper, firstly, based on well-designed double-layer distributed-sector conductance sensor, systematic oil-water and gas-liquid two-phase flow experiments are carried out to capture abundant spatio-temporal flow information. Secondly, well flow parameter measurement performance of DLDSC Sensor is effectively validated from the perspective of normalized conductance. Thirdly, a novel multiplex network-based model is presented to implement data mining and characterize the evolution of flow dynamics. The results demonstrate that the model is powerful for the exploration of the spatial flow behaviors from heterogeneity to randomness in the studied two-phase flows.
Bibliographical noteThis work was supported by National Natural Science Foundation of China under Grant No. 61473203, and the Natural Science Foundation of Tianjin, China under Grant No. 16JCYBJC18200.
- signal analysis
- industrial multiphase flow
- multiplex network
- information fusion