Digital Twin of Electrical Tomography for Quantitative Multiphase Flow Imaging

TL;DR

This paper introduces a digital twin framework for electrical tomography that enables real-time, high-resolution, and accurate multiphase flow imaging without radioactive methods, leveraging simulation, deep learning, and edge computing.

Contribution

The novel digital twin framework integrates simulation and deep learning to enhance electrical tomography for real-time multiphase flow imaging, surpassing qualitative limitations.

Findings

Achieved high-resolution flow imaging in gas-liquid two-phase flow.

Demonstrated the framework's adaptability to various tomography modalities.

Provided a non-radioactive alternative for precise flow visualization.

Abstract

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.