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Técnicas de inteligencia artificial para la caracterización hidrodinámica de flujos bifásicos líquido-gas: una visión general y análisis bibliométrico

dc.contributor.authorGómez Camperos, July Andrea
dc.contributor.authorHernández Cely, Marlon Mauricio
dc.contributor.authorPardo García, Aldo
dc.coverage.spatialColombiaen_US
dc.date.accessioned2025-10-21T19:24:44Z
dc.date.available2025-10-21T19:24:44Z
dc.date.issued2024-07-08
dc.identifier.issn2311-5521en_US
dc.identifier.urihttps://repositorioinstitucional.ufpso.edu.co/xmlui/handle/20.500.14167/5850
dc.description.abstractEstimar con precisión e instantáneamente las características hidrodinámicas en flujos bifásicos líquido-gas es crucial para industrias como la del petróleo, el gas y otros sectores de flujo multifásico, ya que les permite reducir costos y emisiones, aumentar la eficiencia y mejorar la seguridad operativa. Este tipo de flujo implica un deslizamiento constante entre las fases gaseosa y líquida causado por una interfaz deformable, lo que resulta en cambios en la fracción volumétrica del gas y la creación de estructuras conocidas como patrones de flujo. Los métodos empíricos y numéricos utilizados para la predicción suelen presentar imprecisiones significativas durante los procesos de ampliación. Actualmente, se aplican diferentes metodologías basadas en inteligencia artificial (IA) para predecir las características hidrodinámicas en flujos bifásicos líquido-gas, lo cual se corroboró con el análisis bibliométrico, donde se encontró que las técnicas de IA se han aplicado en el reconocimiento de patrones de flujo, la determinación de la fracción volumétrica para cada fluido y la estimación del gradiente de presión. Los resultados revelaron que un total de 178 palabras clave en 70 artículos, 29 de los cuales alcanzaron el umbral (aprendizaje automático, patrón de flujo, flujo bifásico, inteligencia artificial y redes neuronales como el alto predominio), se publicaron principalmente en Flow Measurement and Instrumentation. Esta revista cuenta con el mayor número de artículos publicados relacionados con el tema estudiado, con nueve artículos. El autor más relevante es Efteknari-Zadeh, E., del Instituto de Óptica y Electrónica Cuántica.en_US
dc.language.isoen_USen_US
dc.relationhttps://www.mdpi.com/2311-5521/9/7/158en_US
dc.titleTécnicas de inteligencia artificial para la caracterización hidrodinámica de flujos bifásicos líquido-gas: una visión general y análisis bibliométricoen_US
dc.title.translatedArtificial Intelligence Techniques for the Hydrodynamic Characterization of Two-Phase Liquid–Gas Flows: An Overview and Bibliometric Analysisen_US
dc.contributor.cvlacJuly Andrea Gómez Camperos [0001164287]en_US
dc.contributor.cvlacMarlon Mauricio Hernández Cely [0001354455]en_US
dc.contributor.cvlacAldo Pardo García [0000173878]en_US
dc.contributor.googlescholarMarlon Mauricio Hernández Cely [RQyKQW4AAAAJ]en_US
dc.contributor.googlescholarAldo Pardo García [xKOlNlQAAAAJ]en_US
dc.contributor.orcidJuly Andrea Gómez Camperos [0000-0003-3894-144X]en_US
dc.contributor.orcidMarlon Mauricio Hernández Cely [0000-0001-7564-4044]en_US
dc.contributor.orcidAldo Pardo García [0000-0003-2040-9420]en_US
dc.contributor.researchgateMarlon Mauricio Hernández Cely [Marlon-Hernandez-Cely]en_US
dc.contributor.researchgateAldo Pardo García [Aldo-Pardo]en_US
dc.contributor.scopusJuly Andrea Gómez Camperos [57220550902]en_US
dc.contributor.scopusMarlon Mauricio Hernández Cely [57195293394]en_US
dc.contributor.scopusAldo Pardo García [8646739700]en_US
dc.description.abstractenglishAccurately and instantly estimating the hydrodynamic characteristics in two-phase liquid– gas flow is crucial for industries like oil, gas, and other multiphase flow sectors to reduce costs and emissions, boost efficiency, and enhance operational safety. This type of flow involves constant slippage between gas and liquid phases caused by a deformable interface, resulting in changes in gas volumetric fraction and the creation of structures known as flow patterns. Empirical and numerical methods used for prediction often result in significant inaccuracies during scale-up processes. Different methodologies based on artificial intelligence (AI) are currently being applied to predict hydrodynamic characteristics in two-phase liquid–gas flow, which was corroborated with the bibliometric analysis where AI techniques were found to have been applied in flow pattern recognition, volumetric fraction determination for each fluid, and pressure gradient estimation. The results revealed that a total of 178 keywords in 70 articles, 29 of which reached the threshold (machine learning, flow pattern, two-phase flow, artificial intelligence, and neural networks as the high predominance), were published mainly in Flow Measurement and Instrumentation. This journal has the highest number of published articles related to the studied topic, with nine articles. The most relevant author is Efteknari-Zadeh, E, from the Institute of Optics and Quantum Electronics.en_US
dc.subject.proposalRedes neuronales artificialesen_US
dc.subject.proposalPatrón de flujoen_US
dc.subject.proposalAprendizaje automáticoen_US
dc.subject.proposalInteligencia artificialen_US
dc.subject.proposalFlujo bifásicoen_US
dc.subject.proposalLíquido-gasen_US
dc.subject.proposalIdentificación del régimen de flujoen_US
dc.subject.keywordsArtificial neural networksen_US
dc.subject.keywordsFlow patternen_US
dc.subject.keywordsMachine learningen_US
dc.subject.keywordsArtificial intelligenceen_US
dc.subject.keywordsTwo-phase flowen_US
dc.subject.keywordsLiquid–gasen_US
dc.subject.keywordsFlow regime identificationen_US
dc.publisher.facultyFacultad ingenieríasen_US
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTen_US
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dc.identifier.DOIhttps://doi.org/10.3390/fluids9070158en_US


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