Void Fraction Measurement in a Gas-Liquid Swirling Flow Using an Ultrasonic Sensor

Ofuchi, César Yutaka; Eidt, Henrique Krainer; Bertoldi, Dalton; Silva, Marco J. da; Morales, Rigoberto E. M.; Neves, Flávio

doi:10.1109/access.2020.3033425

Cited by 8 publications

(1 citation statement)

References 27 publications

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“…Non-intrusive techniques to monitor the light-phase core are desirable to the oil industry since they do not introduce a pressure drop in the pipeline and do not require stopping the production for maintenance or replacement. In literature, numerous measurement techniques such as electromagnetic flow meters [9], optical cameras [10], ultrasound sensing [11], and electrical tomography [12] are widely used for the characterization of two-phase flows.…”

Section: Introductionmentioning

confidence: 99%

A Fast Electrical Resistivity-Based Algorithm to Measure and Visualize Two-Phase Swirling Flows

Sattar

Garcia

Portela

et al. 2022

Sensors

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Electrical resistance tomography (ERT) has been used in the literature to monitor the gas–liquid separation. However, the image reconstruction algorithms used in the studies take a considerable amount of time to generate the tomograms, which is far above the time scales of the flow inside the inline separator and, as a consequence, the technique is not fast enough to capture all the relevant dynamics of the process, vital for control applications. This article proposes a new strategy based on the physics behind the measurement and simple logics to monitor the separation with a high temporal resolution by minimizing both the amount of data and the calculations required to reconstruct one frame of the flow. To demonstrate its potential, the electronics of an ERT system are used together with a high-speed camera to measure the flow inside an inline swirl separator. For the 16-electrode system used in this study, only 12 measurements are required to reconstruct the whole flow distribution with the proposed algorithm, 10× less than the minimum number of measurements of ERT (120). In terms of computational effort, the technique was shown to be 1000× faster than solving the inverse problem non-iteratively via the Gauss–Newton approach, one of the computationally cheapest techniques available. Therefore, this novel algorithm has the potential to achieve measurement speeds in the order of 104 times the ERT speed in the context of inline swirl separation, pointing to flow measurements at around 10kHz while keeping the average estimation error below 6 mm in the worst-case scenario.

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