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Multiphase flow detection with photonic crystals and deep learning
Lang Feng1, Stefan Natu2,3, Victoria Som de Cerff Edmonds4
1Corporate Strategic Research, ExxonMobil Research and Engineering, 1545 Route 22 East, Annandale, NJ, 08801, USA. lang.feng@exxonmobil.com.
Nature Communications
|January 29, 2022
Summary
This study introduces a novel photonic crystal sensor for real-time multiphase flow characterization. The technology offers accurate phase fraction, flow morphology, and flow rate measurements, enabling industrial process optimization.
Area of Science:
- Physics
- Engineering
- Material Science
Background:
- Multiphase flows are crucial in industrial processes.
- Current characterization methods lack frequency, accuracy, and cost-efficiency for process optimization.
Purpose of the Study:
- To present a new physics-based concept for real-time multiphase flow characterization.
- To demonstrate the efficacy of photonic crystals for this application.
Main Methods:
- Utilizing low-power microwave transmission through photonic crystals filled with fluid mixtures.
- Applying deep learning analysis to interrogate microwave transmission data.
- Inferring flow rate from differential pressure measurements.
Main Results:
- Achieved fast and accurate characterization of phase fraction and flow morphology.
- Successfully inferred flow rate based on known flow characteristics.
- Validated the concept with lab and field prototypes.
Conclusions:
- Photonic crystals offer a novel, inexpensive, and accurate method for multiphase flow characterization.
- This technology can significantly enhance industrial process optimization.
- The developed technique is convenient and suitable for real-world applications.
