Void Fraction Measurement for Two-Phase Flow Using Electrical Resistance Tomography

Purpose The purpose of this paper is to explore gas/liquid two-phase flow is widely existed in industrial fields, especially in chemical engineering. Electrical resistance tomography (ERT) is considered to be one of the most promising techniques to monitor the transient flow process because of its advantages such as fast respond speed and cross-section imaging. However, maintaining high resolution in space together with low cost is still challenging for two-phase flow imaging because of the ill-conditioning of ERT inverse problem. Design/methodology/approach In this paper, a sparse reconstruction (SR) method based on the learned dictionary has been proposed for ERT, to accurately monitor the transient flow process of gas/liquid two-phase flow in a pipeline. The high-level representation of the conductivity distributions for typical flow regimes can be extracted based on denoising the deep extreme learning machine (DDELM) model, which is used as prior information for dictionary learning. Findings The results from simulation and dynamic experiments indicate that the proposed algorithm efficiently improves the quality of reconstructed images as compared to some typical algorithms such as Landweber and SR-discrete fourier transformation/discrete cosine transformation. Furthermore, the SR-DDELM has also used to estimate the important parameters of the chemical process, a case in point is the volume flow rate. Therefore, the SR-DDELM is considered an ideal candidate for online monitor the gas/liquid two-phase flow. Originality/value This paper fulfills a novel approach to effectively monitor the gas/liquid two-phase flow in pipelines. One deep learning model and one adaptive dictionary are trained via the same prior conductivity, respectively. The model is used to extract high-level representation. The dictionary is used to represent the features of the flow process. SR and extraction of high-level representation are performed iteratively. The new method can obviously improve the monitoring accuracy and save calculation time.

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Overview of Void Fraction Measurement Techniques, Databases and Correlations for Two-Phase Flow in Small Diameter Channels

Fluids ◽

10.3390/fluids5040216 ◽

2020 ◽

Vol 5 (4) ◽

pp. 216

Author(s):

Álvaro Roberto Gardenghi ◽

Erivelto dos Santos Filho ◽

Daniel Gregório Chagas ◽

Guilherme Scagnolatto ◽

Rodrigo Monteiro Oliveira ◽

...

Keyword(s):

Void Fraction ◽

Two Phase Flow ◽

Measurement Techniques ◽

Small Diameter ◽

Phase Flow ◽

Two Phase ◽

Channel Diameter ◽

Experimental Database ◽

Absolute Relative Error ◽

Fraction Measurement

Void fraction is one of the most important parameters for the modeling and characterization of two-phase flows. This manuscript presents an overview of void fraction measurement techniques, experimental databases and correlations, in the context of microchannel two-phase flow applications. Void fraction measurement techniques were reviewed and the most suitable techniques for microscale measurements were identified along its main characteristics. An updated void fraction experimental database for small channel diameter was obtained including micro and macrochannel two-phase flow data points. These data have channel diameter ranging from 0.5 to 13.84 mm, horizontal and vertical directions, and fluids such as air-water, R410a, R404a, R134a, R290, R12 and R22 for both diabatic and adiabatic conditions. New published void fraction correlations as well high cited ones were evaluated and compared to this small-diameter void fraction database in order to quantify the prediction error of them. Moreover, a new drift flux correlation for microchannels was also developed, showing that further improvement of available correlations is still possible. The new correlation was able to predict the microchannel database with mean absolute relative error of 9.8%, for 6% of relative improvement compared to the second-best ranked correlation for small diameter channels.

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