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.
Highlights
Multiphase flows are ubiquitous in industrial settings
In this work we investigate the utility of exploiting microwave transmission through Photonic crystals[1–8] (PC) for characterizing phase fraction, flow rates, and flow morphology
While it is time consuming and computationally expensive to invert this image for the phase distribution in the PC, we investigate the use of a supervised machine learning analysis to predict the phase fractions and macroscopic distribution in the PC
Summary
Multiphase flows are ubiquitous in industrial settings. It is often necessary to characterize these fluid mixtures in support of process optimization. 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 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. Conventional techniques to determine these flow characteristics are cumbersome to implement, prone to fouling and error, and require regular calibration[15] This includes microwave-based phase fraction measurements which either do not sample the entire flow cross-section, or require a support structure or an alternative low-frequency RF band to allow for energy transmission through fluid mixtures with a high water cut[15]. We investigate the viability of using Deep learning physics-based data analytics[23] to interrogate microwave transmission data in support of rapid, easy to deploy, and relatively inexpensive MPFM
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