@article{961780f286f14c41b7036c111c81f755,
title = "A virtual platform of electrical tomography for multiphase flow imaging",
abstract = "This paper reports a new approach to quantitatively evaluate the performance of Electrical Tomography (ET) in measuring dynamic multiphase flows. A virtual multiphase flow imaging platform based on ET is constructed and demonstrated on two typical gas–liquid flows, i.e., water–gas flow and oil–gas flow. Two coupling simulation cases, i.e., water–gas flow field and electric currents field coupling simulation and oil–gas flow field and electrostatics field coupling simulation, are performed to simulate multiphase flow sensing of Electrical Impedance Tomography (EIT) and Electrical Capacitance Tomography (ECT). We quantitatively evaluated the representative EIT and ECT image reconstruction algorithms on the virtual evaluation platform bringing evidence of the improved capability to capture the key flow features of the fluid mixture with respect to traditional static phantoms. Ad-hoc treatment of the signal noise enables one to better capture dynamic responses of the fluid phase volume fractions and their spatial gradients throughout their mixing along the conduit, ultimately demonstrating unprecedented potential in the quantitative characterization of complex, unsteady multi-phase systems. The proposed image reconstruction constitutes a highly effective platform for quantitative performance evaluation of ET, parameter optimization of model-based ET image reconstruction algorithms, and for the development of data-driven ET algorithms in multiphase flow imaging.NOME",
author = "Shengnan Wang and Francesco Giorgio-Serchi and Yunjie Yang",
note = "Funding Information: This work was supported by the European Union{\textquoteright}s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement (Grant No. 801215) and the University of Edinburgh Data-Driven Innovation programme, part of the Edinburgh and South East Scotland City Region Deal, Data Driven Innovation Chancellor{\textquoteright}s Fellowship, and National Natural Science Foundation of China (Grant No. 51906209). Publisher Copyright: {\textcopyright} 2022 Author(s).",
year = "2022",
month = oct,
day = "6",
doi = "10.1063/5.0103187",
language = "English",
volume = "34",
journal = "Physics of Fluids",
issn = "1070-6631",
publisher = "American Institute of Physics",
number = "10",
}