A virtual platform of electrical tomography for multiphase flow imaging

Shengnan Wang; Francesco Giorgio-Serchi; Yunjie Yang

doi:10.1063/5.0103187

A virtual platform of electrical tomography for multiphase flow imaging

Shengnan Wang
, Francesco Giorgio-Serchi
, Yunjie Yang^*

^*Corresponding author for this work

School of Engineering

Research output: Contribution to journal › Article › peer-review

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

Original language	English
Article number	107104
Journal	Physics of Fluids
Volume	34
Issue number	10
Early online date	6 Oct 2022
DOIs	https://doi.org/10.1063/5.0103187
Publication status	E-pub ahead of print - 6 Oct 2022

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10.1063/5.0103187Licence: Creative Commons: Attribution (CC-BY)

5.0103187Final published version, 7.27 MBLicence: Creative Commons: Attribution (CC-BY)

TRAIN@Ed: Transnational Research and Innovation Network at Edinburgh
Gorjanc, G. (Principal Investigator), Bell, C. (Co-investigator), Duncan, A. (Co-investigator), Farrington, S. (Co-investigator), Florian, L. (Co-investigator), Forde, M. (Co-investigator), Hickey, J. (Co-investigator), Lacka, E. (Co-investigator), Ma, T. (Co-investigator), Mcneill, G. (Co-investigator), Medina-Lopez, E. (Co-investigator), Rosser, S. (Co-investigator), Rossi, R. (Co-investigator), Sabanis, S. (Co-investigator), Szpruch, L. (Co-investigator), Tenesa, A. (Co-investigator), Wake, D. (Co-investigator), Williamson, B. (Co-investigator) & Yang, Y. (Co-investigator)
EU government bodies, Non-EU industry, commerce and public corporations
1/11/19 → 19/04/23
Project: Research

Cite this

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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).",

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AU - Wang, Shengnan

AU - Giorgio-Serchi, Francesco

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N1 - Funding Information: This work was supported by the European Union’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’s Fellowship, and National Natural Science Foundation of China (Grant No. 51906209). Publisher Copyright: © 2022 Author(s).

PY - 2022/10/6

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N2 - 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

AB - 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

U2 - 10.1063/5.0103187

DO - 10.1063/5.0103187

M3 - Article

SN - 1070-6631

VL - 34

JO - Physics of Fluids

JF - Physics of Fluids

IS - 10

M1 - 107104

ER -

A virtual platform of electrical tomography for multiphase flow imaging

Abstract

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TRAIN@Ed: Transnational Research and Innovation Network at Edinburgh

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