Simultaneous laser-induced fluorescence and capacitance probe measurement of downwards annular gas-liquid flows

Voulgaropoulos, Victor; Patapas, Andrius; Lecompte, Steven; Charogiannis, Alexandros; Matar, Omar; Paepe, Michel De

doi:10.1016/j.ijmultiphaseflow.2021.103665

Cited by 22 publications

(3 citation statements)

References 43 publications

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“…To cover a wide range of flow conditions, a comprehensive analysis was conducted by combining previous experimental data obtained from the developed flow region with the current results. These data [11,12,15,18,33,[36][37][38][39][40][41] were then compared with Nusselt's theoretical solution and two recently proposed empirical predictions [36,37]. The comparison, as shown in Fig.…”

Section: Film Thicknessmentioning

confidence: 99%

Experimental Study of Developing Free-Falling Annular Flow in a Large Scale Vertical Pipe

Xue¹,

Stewart²,

Kelly³

et al. 2023

Preprint

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Section: Film Thicknessmentioning

confidence: 99%

Experimental Study of Developing Free-Falling Annular Flow in a Large Scale Vertical Pipe

Xue¹,

Stewart²,

Kelly³

et al. 2023

Preprint

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“…Other studies have been conducted in this subject. Authors in [ 16 ], to enhance the variety of imitated phase distributions, simulations using a particular electrode configuration performed, such as the finite element method (FEM). Similar to simulations based on experiments, curves fitted to describe the relationships between capacitance and some of relevant parameters.…”

Section: Introductionmentioning

confidence: 99%

Measuring volume fractions of a three-phase flow without separation utilizing an approach based on artificial intelligence and capacitive sensors

Mayet,

Fouladinia,

Alizadeh

et al. 2024

PLoS ONE

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Many different kind of fluids in a wide variety of industries exist, such as two-phase and three-phase. Various combinations of them can be expected and gas-oil-water is one of the most common flows. Measuring the volume fraction of phases without separation is vital in many aspects, one of which is financial issues. Many methods are utilized to ascertain the volumetric proportion of each phase. Sensors based on measuring capacity are so popular because this kind of sensor operates seamlessly and autonomously without necessitating any form of segregation or disruption for measuring in the process. Besides, at the present moment, Artificial intelligence (AI) can be nominated as the most useful tool in several fields, and metering is no exception. Also, three main type of regimes can be found which are annular, stratified, and homogeneous. In this paper, volume fractions in a gas-oil-water three-phase homogeneous regime are measured. To accomplish this objective, an Artificial Neural Network (ANN) and a capacitance-based sensor are utilized. To train the presented network, an optimized sensor was implemented in the COMSOL Multiphysics software and after doing a lot of simulations, 231 different data are produced. Among all obtained results, 70 percent of them (161 data) are awarded to the train data, and the rest of them (70 data) are considered for the test data. This investigation proposes a new intelligent metering system based on the Multilayer Perceptron network (MLP) that can estimate a three-phase water-oil-gas fluid’s water volume fraction precisely with a very low error. The obtained Mean Absolute Error (MAE) is equal to 1.66. This dedicates the presented predicting method’s considerable accuracy. Moreover, this study was confined to homogeneous regime and cannot measure void fractions of other fluid types and this can be considered for future works. Besides, temperature and pressure changes which highly temper relative permittivity and density of the liquid inside the pipe can be considered for another future idea.

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“…Compared with this direct method, the indirect measurement method is more widely used, especially in the industrial production process, which does not require direct observation of flow patterns in most cases. The indirect measurement method obtains signals such as capacitance [ 14 ], pressure [ 15 ], temperature [ 16 ], and capacitance [ 17 ] that can be easily measured, and indicate characteristics of different flow patterns by combining signal-processing technology. Then, the flow pattern or mixing state can be classified and recognized according to these characteristics.…”

Section: Introductionmentioning

confidence: 99%

Identifying Flow Patterns in a Narrow Channel via Feature Extraction of Conductivity Measurements with a Support Vector Machine

Yang

Wang

et al. 2023

Sensors

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In this work, a visualization experiment for rectangular channels was carried out to explore gas–liquid two-phase flow characteristics. Typical flow patterns, including bubble, elastic and mixed flows, were captured by direct imaging technology and the corresponding measurements with fluctuation characteristics were recorded by using an electrical conductivity sensor. Time-domain and frequency-domain characteristics of the corresponding electrical conductivity measurements of each flow pattern were analyzed with a probability density function and a power spectral density curve. The results showed that the feature vectors can be constructed to reflect the time–frequency characteristics of conductivity measurements successfully by introducing the quantized characteristic parameters, including the maximum power of the frequency, the standard deviation of the power spectral density, and the range of the power distribution. Furthermore, the overall recognition rate of the four flow patterns measured by the method was 93.33% based on the support vector machine, and the intelligent two-phase flow-pattern identification method can provide a new technical support for the online recognition of gas–liquid two-phase flow patterns in rectangular channels. It may thus be concluded that this method should be of great significance to ensure the safe and efficient operation of relevant industrial production systems.

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Simultaneous laser-induced fluorescence and capacitance probe measurement of downwards annular gas-liquid flows

Cited by 22 publications

References 43 publications

Experimental Study of Developing Free-Falling Annular Flow in a Large Scale Vertical Pipe

Experimental Study of Developing Free-Falling Annular Flow in a Large Scale Vertical Pipe

Measuring volume fractions of a three-phase flow without separation utilizing an approach based on artificial intelligence and capacitive sensors

Identifying Flow Patterns in a Narrow Channel via Feature Extraction of Conductivity Measurements with a Support Vector Machine

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