This dataset is for publication "Digital twin enables quantitative multiphase flow imaging with low-cost tomography". ABSTRACT: Multiphase flow is ubiquitous in nature, industry, and research. Quantitative multiphase flow imaging is the key to understanding this complex phenomenon. However, there are as yet no low-cost, scalable imaging techniques to achieve quantitative multiphase flow imaging for a wide range of scenarios. Here we report a digital twin (DT) framework that unlocks the real-time quantitative multiphase flow imaging capability of electrical tomography (ET) that is non-invasive, non-radioactive, scalable, and low-cost. The DT framework, building upon a synergistic integration of the 3D field coupling model, deep learning, and edge computing, allows ET to dynamically learn the flow features in the virtual space and implement the learned model in the physical device, thus providing unprecedented resolution and accuracy in the real world. The DT framework is demonstrated on dynamic gas-liquid two-phase flows, showing step-change performance compared to the state of the art. It can be readily extended to various tomography modalities and multiphase flows at different scales for precise flow visualization and characterization.
Wang, Shengnan; Yang, Yunjie. (2022). Edinburgh virtual multiphase flow imaging dataset, [dataset]. University of Edinburgh. School of Engineering. Institute for Digital Communications. https://doi.org/10.7488/ds/3501.
|Date made available||18 Aug 2022|