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Three-dimensional electrical capacitance tomography (3D-ECT) has shownpromise for visualizing industrial multiphase flows. However, existing 3D-ECTapproaches suffer from limited imaging resolution and lack assessment metrics,hampering their effectiveness in quantitative multiphase flow imaging. Thispaper presents a digital twin (DT)-assisted 3D-ECT, aiming to overcome theselimitations and enhance our understanding of multiphase flow dynamics. The DTframework incorporates a 3D fluid-electrostatic field coupling model (3D-FECM)that digitally represents the physical 3D-ECT system, which enables us tosimulate real multiphase flows and generate a comprehensive virtual multiphaseflow 3D imaging dataset. Additionally, the framework includes a deep neuralnetwork named 3D deep back projection (3D-DBP), which learns multiphase flowfeatures from the virtual dataset and enables more accurate 3D flow imaging inthe real world. The DT-assisted 3D-ECT was validated through virtual andphysical experiments, demonstrating superior image quality, noise robustnessand computational efficiency compared to conventional 3D-ECT approaches. Thisresearch contributes to developing accurate and reliable 3D-ECT techniques andtheir implementation in multiphase flow systems across various industries.

Digital twin-assisted three-dimensional electrical capacitance tomography for multiphase flow imaging