Increasingly advanced technology allows the monitoring of complex systems from a wide variety of perspectives. But the exploration of such systems from a multichannel sensor information viewpoint remains a complicated challenge of ongoing interest. In this paper, first, based on a well-designed double-layer distributed-sector conductance (DLDSC) sensor, systematic oil-water and gas-liquid two-phase flow experiments are carried out to capture abundant spatiotemporal flow information. Second, well flow parameter measurement performance of the DLDSC sensor is effectively validated from the perspective of normalized conductance. Third, 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.

中文翻译：

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一种新型的基于多重网络的传感器信息融合模型及其在工业多相流系统中的应用


日益先进的技术允许从多种角度监控复杂系统。但从多通道传感器信息的角度探索此类系统仍然是人们持续关注的复杂挑战。本文首先基于精心设计的双层分布式扇区电导（DLDSC）传感器，进行了系统的油水和气液两相流实验，以捕获丰富的时空流动信息。其次，从归一化电导角度有效验证了DLDSC传感器的井流参数测量性能。第三，提出了一种新颖的基于多重网络的模型来实现数据挖掘并表征流动力学的演化。结果表明，该模型对于探索所研究的两相流中从非均质性到随机性的空间流行为非常有用。