Abstract
Gas–liquid two-phase flows are encountered in various industrial processes involving high temperatures and high pressures, which necessitates nonintrusive sensing for real-time imaging of phase distribution and flow parameters. In this context, this article presents an electrical impedance tomography (EIT)-based eigenvalue correlation method that allows extracting two-phase flow features, namely, the void fraction and the flow regime, which are used in turn to improve flow visualizations. Benefiting from the so-called full-scan excitation strategy, the eigenvalue correlation method has been devised in to estimate the phase fraction from EIT raw measurements. In this article, this method is refined and integrated into an image-enhancing procedure, which is illustrated and validated using dynamic experimental data. A total of 80 experiments are considered with water and air mass flow rates ranging from 1.58 to 79.43 kg/min and from 0.1 to 5.0 kg/min, respectively, covering slug, plug, stratified smooth, stratified wavy, and annular flows. Based on a preliminary system calibration and a raw image guess, the volume-averaged void fractions are then estimated using the proposed method and integrated into EIT-based images to form binarized tomograms relative to the acquisition time. The EIT tomograms, thus, obtained show an excellent agreement with some <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> -ray reference measurements of the phase distribution.
Highlights
G AS–LIQUID two-phase flows play a vital role in various industrial processes, for example, in the electricity generation industry where the flow transfers heat from a power core to a generating turbine
The data acquired during extensive experiments involving various combinations of air and water with varying fractions and velocities were used to identify the flow regime and estimating the volume AVF using sensor data fusion
The extracted flow features are further integrated into electrical impedance tomography (EIT) tomograms to improve flow visualizations
Summary
G AS–LIQUID two-phase flows play a vital role in various industrial processes, for example, in the electricity generation industry where the flow transfers heat from a power core to a generating turbine. In such processes, it is crucial to optimize performances by detecting flow regimes and gas build-ups as potential escalations can compromise the safety of operations. In the nuclear power industry, in particular, Cédric Bellis is with CNRS, Centrale Marseille, LMA, Aix-Marseille University, 13007 Marseille, France. Evaluations of heat transfer and multiphase flow instabilities in reactors are critical to safety [1] In this context, there is a great need for nonintrusive instrumentation techniques for online monitoring of the gas–liquid two-phase flows
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