Visualization of flow patterns during condensation in dimpled surface tubes of different materials

Tang, Weiyu; Sun, Zhigang; Li, Wei

doi:10.1016/j.ijheatmasstransfer.2020.120251

Cited by 14 publications

(2 citation statements)

References 27 publications

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“…The extraction of these substances generates the formation of flows with more than one phase consecutively which are monitored with intrusive devices or techniques [9][10][11][12] that quantify parameters of each of the fluids present [13] such as viscosities, densities, surface velocities [14,15], Reynolds number, holdups [16] and in certain cases, they are associated with geometric variables of the pipes, such as their diameter, length, and inclination [17,18]. The identification of flow patterns in pipelines is a fundamental factor [19], being of vital importance to obtain with precision a tool capable of modelling such behaviors of multiphase flows working together with existing artificial intelligence techniques [20,21] such as artificial neural networks [22], in order to train, validate and test an intelligent model [23] capable of generating flow maps [24] for vertical [25], horizontal [26] pipes in which all the performances generated from the modification of the physical…”

Section: Introductionmentioning

confidence: 99%

Flow pattern identification of liquid-liquid (oil and water) in vertical pipelines using machine learning techniques

Ruiz-Diaz

Camperos

Hernández-Cely

2022

J. Phys.: Conf. Ser.

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Given the importance of process control in the petrochemical industry, there is a need to determine the behavior of the fluids inside the pipes. In this work a methodology is developed for the identification of flow patterns in vertical pipes with diameters between 0.01 m and 0.10 m, from the implementation of artificial intelligence techniques, for a liquid combination of two phases composed of oil with viscosity in the range of 792 Kg/m3 to 1823 Kg/m3 and water at room temperature. The predictive models generated in the structuring of the methodology were trained with 70% of data based on viscosity parameters, pipe diameter, volume fraction and surface velocities of the working fluids stored in a database. The remaining information, equivalent to 30% of the total, was used to develop the automatic model validation. The flow patterns identified by the intelligent system for oil and water flow, without considering the predominant substance, are churning, dispersed, very fine dispersion, transition flow, intermittent, and annular

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Section: Introductionmentioning

confidence: 99%

Flow pattern identification of liquid-liquid (oil and water) in vertical pipelines using machine learning techniques

Ruiz-Diaz

Camperos

Hernández-Cely

2022

J. Phys.: Conf. Ser.

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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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Theoretical and experimental analyses of two-phase flow condensation heat transfer inside the horizontal micro-fin tube

Chen

Lei

et al. 2022

Applied Thermal Engineering

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Visualization of flow patterns during condensation in dimpled surface tubes of different materials

Cited by 14 publications

References 27 publications

Flow pattern identification of liquid-liquid (oil and water) in vertical pipelines using machine learning techniques

Flow pattern identification of liquid-liquid (oil and water) in vertical pipelines using machine learning techniques

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

Theoretical and experimental analyses of two-phase flow condensation heat transfer inside the horizontal micro-fin tube

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