Flow regime identification and classification based on void fraction and differential pressure of vertical two-phase flow in rectangular channel

Chalgeri, Vikrant Siddharudh; Jeong, Ji Hwan

doi:10.1016/j.ijheatmasstransfer.2018.12.015

Cited by 43 publications

(6 citation statements)

References 23 publications

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“…Pressure drops fluctuation of a two-phase flow in pipes is very nearly connected with the behavior of flow patterns. Chalgeri and Jeong [16] studied the vertical two-phase flow in a vertical rectangular channel using a DPT. The outcome showed the possibility of using the sensor for classifying the twophase flow structures, where many flow patterns were classified such as bubbly flow.…”

Section: Related Workmentioning

confidence: 99%

Investigation in Gas-Oil Two-Phase Flow using a Differential Pressure Transducer and Wire Mesh Sensor in Vertical Pipes

Musa

Mahmood

Simo

et al. 2022

EMITTER Int'l J. of Engin. Technol.

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The current study is performed to identify the flow regimes of oil-gas two-phase flow experimentally in a vertical pipe has an internal diameter of 6.7 cm. It also aims to provide more details about the possibility of using Differential Pressure Transducers (DPT) for indicating flow patterns. A flow development of oil and gas has been investigated in a vertical pipe of 6 m in length and operated at atmospheric pressure. A series of experiments have been run to cover a range of inlet oil superficial velocities from 0.262 to 0.419 m/s, and inlet gas superficial velocities from 0.05 to 4.7 m/s. Wire Mesh Sensors (WMS) have been used to collect the obtained void fraction values of the flow. The Differential Pressure Transducer (DPT) is utilized to measure the pressure drop values of a one-meter along the pipe. The flow patterns are classified according to the analysis of void fractions, pressure gradients regarding time series, tomographic images, probability density functions of the void fractions, and pressure gradients. A bubbly flow is observed at low superficial velocities of gas and liquid, slug flow is observed at the lower flow rate of liquid and moderate flow rates of gas, while the churn flow pattern is recognized at the higher rates of liquid and gas. Also, the result has revealed the possibility of using Differential Pressure Transducers (DPT) to classify the gas-oil flow patterns in vertical pipes.

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Section: Related Workmentioning

confidence: 99%

Investigation in Gas-Oil Two-Phase Flow using a Differential Pressure Transducer and Wire Mesh Sensor in Vertical Pipes

Musa

Mahmood

Simo

et al. 2022

EMITTER Int'l J. of Engin. Technol.

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“…The complexity of the problem is directly related to the number of neurons that make up both the input and output layers, as they are equal to the number of parameters included in each phase respectively. The parameter that is used as the initial determinant to establish the proximity of the model is generated with the minimization of the mean square error, which follows equation (4).…”

Section: Treatment To Minimize Errors By Selecting the Appropriate Numentioning

confidence: 99%

“…The oil industry has focused its interest on the development of technologies that allow obtaining updated systems for the precise measurement of multiphase flow, defining this as a concurrent flow of substances in certain states or phases (solid, liquid, gas) which generate a layer separation with mixture between the phases [1] or characteristic patterns, derived from the initial hydrodynamic parameters of the Flow [2]. Hydrodynamic parameters are identified by applying different methodologies such as electrical impedance [3], pressure variation [4], ultrasonic echoes [5] and optical image analysis [6].…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Holdup in Horizontal Wateroil Flow Using a Neural Network Approach

Díaz¹,

González‐Estrada²,

Cely³

2021

14th WCCM-ECCOMAS Congress

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In this work, the application of an artificial neural network (ANN) is proposed to develop a predicting model for the holdup of a two-phase flow composed of water and mineral oil in a horizontal pipe. For this, the surface velocities of each fluid and the differential pressure in the pipeline are used as input parameters of the multilayer artificial neural network with backpropagation, while the holdup of the fluids is used as the output parameter for the training. A set of 56 experimental data was obtained in the LabPetro-CEPETRO-UNICAMP laboratory. The best performing results for the predictive model show a mean absolute error (AAPE) of 3.01% and a coefficient of determination 2 of 0.9964 using 15 neurons in the hidden layer of the network and the TanSig transfer function.

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“…La industria petrolera ha centrado su interés en el desarrollo de tecnologías enfocadas en sistemas actualizados para la medición precisa de flujo multifásico [1]; este se define como un flujo continuo de sustancias en determinados estados termodinámicos (sólido, líquido, gas), los cuales generan una capa de separación con mezcla entre las fases [2] o patrones característicos [3], [4], [5], [6] derivados de los parámetros fluidodinámicos iniciales del flujo en la vertical [7], [8] y en la horizontal [9], [10]. Los parámetros hidrodinámicos son identificados mediante la aplicación de distintas metodologías, tales como la impedancia eléctrica [11], variación de presión [12], ecos ultrasónicos [13] y análisis de imágenes ópticas [14].…”

Section: Introductionunclassified

Modelo predictivo para el cálculo de la fracción volumétrica de un flujo bifásico agua-aceite en la horizontal utilizando una red neuronal artificial

Hernández-Cely¹,

Ruiz-Diaz²,

González‐Estrada

2022

Rev. UIS ing.

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Este artículo presenta la aplicación de una red neuronal artificial (RNA) para el desarrollo de un modelo capaz de predecir la fracción volumétrica de un flujo bifásico compuesto por agua y aceite mineral en una tubería horizontal. Se utilizan las velocidades superficiales de cada fluido y el diferencial de presión en la tubería como parámetros de entrada de la red neuronal artificial multicapa con retropropagación, mientras que la fracción volumétrica de los fluidos se utiliza como parámetro de salida en el entrenamiento de la misma. Los 56 datos experimentales con los que se trabajó se obtuvieron en el laboratorio LabPetro - CEPETRO-UNICAMP. Los resultados que arrojó el modelo predictivo con mejor rendimiento presentan un error absoluto medio porcentual (AAPE) de 3,01 % y un coeficiente de determinación de 0,9964 utilizando 15 neuronas en la capa oculta de la red y la función de transferencia TanSig.

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Flow regime identification and classification based on void fraction and differential pressure of vertical two-phase flow in rectangular channel

Cited by 43 publications

References 23 publications

Investigation in Gas-Oil Two-Phase Flow using a Differential Pressure Transducer and Wire Mesh Sensor in Vertical Pipes

Investigation in Gas-Oil Two-Phase Flow using a Differential Pressure Transducer and Wire Mesh Sensor in Vertical Pipes

Predictive Modeling of Holdup in Horizontal Wateroil Flow Using a Neural Network Approach

Modelo predictivo para el cálculo de la fracción volumétrica de un flujo bifásico agua-aceite en la horizontal utilizando una red neuronal artificial

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