Multiphase flow detection with photonic crystals and deep learning

Multiphase flows are ubiquitous in industrial settings. It is often necessary to characterize these fluid mixtures in support of process optimization. Unfortunately, existing commercial technologies often fail to provide frequent, accurate, and cost-efficient data necessary to enable process optimiz...

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Bibliographic Details
Published in:Nature communications 2022-01, Vol.13 (1), p.567-567, Article 567
Main Authors: Feng, Lang, Natu, Stefan, Som de Cerff Edmonds, Victoria, Valenza, John J.
Format: Article
Language:English
Subjects:
639/766/25
639/766/930/12
Crystals
Deep learning
Differential pressure
Flow characteristics
Flow velocity
Humanities and Social Sciences
Microwave photonics
Microwave transmission
multidisciplinary
Multiphase flow
Optimization
Photonic crystals
Physics
Prototypes
Science
Science (multidisciplinary)
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Summary:Multiphase flows are ubiquitous in industrial settings. It is often necessary to characterize these fluid mixtures in support of process optimization. Unfortunately, existing commercial technologies often fail to provide frequent, accurate, and cost-efficient data necessary to enable process optimization. Here we show a new physics-based concept and testing with lab and field prototypes leveraging photonic crystals for real-time characterization of multiphase flows. In particular, low power (~1 mW) microwave transmission through photonic crystals filled with fluid mixtures may be interrogated by deep learning analysis techniques to provide a fast and accurate characterization of phase fraction and flow morphology. Moreover when these flow characteristics are known, the flow rate is accurately inferred from the differential pressure necessary for the flow to pass through the photonic crystal. This insight provides a basis to develop a unique class of inexpensive, accurate, and convenient techniques to characterize multiphase flows. Photonic crystal (PC)-based sensing is an attractive approach for achieving accurate environmental sensing applications due to its band structure. Here, the authors utilize microwave transmission through PCs and deep learning physics-based data analytics to characterize flowing fluid mixtures.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-022-28174-2