Projects per year
Abstract
Three-dimensional electrical capacitance tomography (3D-ECT) has shown promise for visualizing industrial multiphase flows. However, existing 3D-ECT approaches suffer from limited imaging resolution and lack assessment metrics, hampering their effectiveness in quantitative multiphase flow imaging. This article presents a digital twin (DT)-assisted 3D-ECT, aiming to overcome these limitations and enhance our understanding of multiphase flow dynamics. The DT framework incorporates a 3-D fluid-electrostatic field coupling model (3D-FECM) that digitally represents the physical 3D-ECT system, which enables us to simulate real multiphase flows and generate a comprehensive virtual multiphase flow 3-D imaging dataset. In addition, the framework includes deep neural networks such as 3-D fully convolutional Unet (3D-FC-UNet) and 3-D deep backprojection (3D-DBP), which learn multiphase flow features from the virtual dataset and enable more accurate 3-D flow imaging in the real world. The DT-assisted 3D-ECT was validated through virtual and physical experiments, demonstrating superior image quality, noise robustness, and computational efficiency compared to conventional 3D-ECT approaches. This research contributes to developing accurate and reliable 3D-ECT techniques and their implementation in multiphase flow systems across various industries.
Original language | English |
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Article number | 4507612 |
Journal | IEEE Transactions on Instrumentation and Measurement |
Volume | 73 |
Early online date | 12 Aug 2024 |
DOIs | |
Publication status | E-pub ahead of print - 12 Aug 2024 |
Keywords / Materials (for Non-textual outputs)
- Accuracy
- Couplings
- Image reconstruction
- Imaging
- Permittivity
- Sensors
- Three-dimensional displays
- Three-dimensional electrical capacitance tomography (3D-ECT)
- digital twin (DT)
- field coupling model
- machine learning
- multiphase flow
- 3-D electrical capacitance tomography (3D-ECT)
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Dive into the research topics of 'Digital twin-assisted three-dimensional electrical capacitance tomography for multiphase flow imaging'. Together they form a unique fingerprint.Projects
- 1 Finished
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TRAIN@Ed: Transnational Research and Innovation Network at Edinburgh
Gorjanc, G., Bell, C., Duncan, A., Farrington, S., Florian, L., Forde, M., Hickey, J., Lacka, E., Ma, T., Mcneill, G., Medina-Lopez, E., Rosser, S., Rossi, R., Sabanis, S., Szpruch, L., Tenesa, A., Wake, D., Williamson, B. & Yang, Y.
EU government bodies, Non-EU industry, commerce and public corporations
1/11/19 → 19/04/23
Project: Research