  We report a digital twin (DT) framework of electrical tomography (ET) to
address the challenge of real-time quantitative multiphase flow imaging based
on non-invasive and non-radioactive technologies. Multiphase flow is ubiquitous
in nature, industry, and research. Accurate flow imaging is the key to
understanding this complex phenomenon. Existing non-radioactive multiphase flow
imaging methods based on electrical tomography are limited to providing
qualitative images. The proposed DT framework, building upon a synergistic
integration of 3D field coupling simulation, model-based deep learning, and
edge computing, allows ET to dynamically learn the flow features in the virtual
space and implement the model in the physical system, thus providing
unprecedented resolution and accuracy. The DT framework is demonstrated on
gas-liquid two-phase flow and electrical capacitance tomography (ECT). It can
be readily extended to various tomography modalities, scenarios, and scales in
biomedical, energy, and aerospace applications as an effective alternative to
radioactive solutions for precise flow visualization and characterization.
