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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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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. |
format | Online Article Text |
id | pubmed-8799677 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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