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 (WEC-CS) and Oil-gas flow field and ElectroStatics field Coupling Simulation (OES-CS), 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 panthoms. Ad-hoc treatment of the signal noise enables to better capture dynamic responses of the fluid phases 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.

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“…In the medical field, it has been employed for pulmonary function screening ( Martins et al, 2019 ; Shi et al, 2021 ; Fossali et al, 2022 ), functional brain imaging ( Jiang & Soleimani, 2019 ; Ke et al, 2022 ), and cancer detection ( Wu & Soleimani, 2019 ; Sun et al, 2019 ; Mansouri et al, 2022 ). Industrial applications include material damage detection ( Mao et al, 2019 ), artificial skin ( Liu et al, 2020 ), and industrial simulation ( Wang, Giorgio-Serchi & Yang, 2022 ). In the field of agriculture, EIT has been harnessed for observing plant growth ( Corona-Lopez et al, 2019 ; Ehosioke et al, 2020 ), root development ( Wang et al, 2023 ), and resistance assessment ( Qian et al, 2021 ), among other uses.…”