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Multiphase flow detection with photonic crystals and deep learning

Author

Listed:
  • Lang Feng

    (Corporate Strategic Research, ExxonMobil Research and Engineering)

  • Stefan Natu

    (Corporate Strategic Research, ExxonMobil Research and Engineering
    Amazon Alexa)

  • Victoria Som de Cerff Edmonds

    (Research and Engineering IT, ExxonMobil Technical Computing Company)

  • John J. Valenza

    (Corporate Strategic Research, ExxonMobil Research and Engineering)

Abstract

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.

Suggested Citation

  • Lang Feng & Stefan Natu & Victoria Som de Cerff Edmonds & John J. Valenza, 2022. "Multiphase flow detection with photonic crystals and deep learning," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28174-2
    DOI: 10.1038/s41467-022-28174-2
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    References listed on IDEAS

    as
    1. Weining Man & Mischa Megens & Paul J. Steinhardt & P. M. Chaikin, 2005. "Experimental measurement of the photonic properties of icosahedral quasicrystals," Nature, Nature, vol. 436(7053), pages 993-996, August.
    2. Bo Zhen & Chia Wei Hsu & Yuichi Igarashi & Ling Lu & Ido Kaminer & Adi Pick & Song-Liang Chua & John D. Joannopoulos & Marin Soljačić, 2015. "Spawning rings of exceptional points out of Dirac cones," Nature, Nature, vol. 525(7569), pages 354-358, September.
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