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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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Autores principales: Feng, Lang, Natu, Stefan, Som de Cerff Edmonds, Victoria, Valenza, John J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8799677/
https://www.ncbi.nlm.nih.gov/pubmed/35091556
http://dx.doi.org/10.1038/s41467-022-28174-2
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author Feng, Lang
Natu, Stefan
Som de Cerff Edmonds, Victoria
Valenza, John J.
author_facet Feng, Lang
Natu, Stefan
Som de Cerff Edmonds, Victoria
Valenza, John J.
author_sort Feng, Lang
collection PubMed
description 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.
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spelling pubmed-87996772022-02-07 Multiphase flow detection with photonic crystals and deep learning Feng, Lang Natu, Stefan Som de Cerff Edmonds, Victoria Valenza, John J. Nat Commun Article 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. Nature Publishing Group UK 2022-01-28 /pmc/articles/PMC8799677/ /pubmed/35091556 http://dx.doi.org/10.1038/s41467-022-28174-2 Text en © ExxonMobil Research and Engineering Company 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Feng, Lang
Natu, Stefan
Som de Cerff Edmonds, Victoria
Valenza, John J.
Multiphase flow detection with photonic crystals and deep learning
title Multiphase flow detection with photonic crystals and deep learning
title_full Multiphase flow detection with photonic crystals and deep learning
title_fullStr Multiphase flow detection with photonic crystals and deep learning
title_full_unstemmed Multiphase flow detection with photonic crystals and deep learning
title_short Multiphase flow detection with photonic crystals and deep learning
title_sort multiphase flow detection with photonic crystals and deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8799677/
https://www.ncbi.nlm.nih.gov/pubmed/35091556
http://dx.doi.org/10.1038/s41467-022-28174-2
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