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Updated: Oct 10, 2025

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole
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Machine learning applications to predict two-phase flow patterns.

Harold Brayan Arteaga-Arteaga1, Alejandro Mora-Rubio1, Frank Florez1

  • 1Department of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia.

Peerj. Computer Science
|December 15, 2021
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Summary

Artificial intelligence models accurately classify two-phase flow patterns in pipes. The Extra Trees model achieved 98.8% accuracy, identifying key variables like superficial velocities and pipe inclination.

Keywords:
Deep learningExtra treesFeature extractionFlow patterns classificationMachine learning

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Area of Science:

  • * Fluid Dynamics
  • * Artificial Intelligence
  • * Machine Learning

Background:

  • * Two-phase flow patterns in pipes are influenced by physical variables (velocity, viscosity, density, surface tension) and pipe characteristics (inclination, diameter).
  • * Accurate classification of these flow patterns is crucial for various industrial applications.

Purpose of the Study:

  • * To evaluate the performance of different artificial intelligence (AI) models for classifying two-phase flow patterns.
  • * To identify the most effective AI algorithms and key influencing variables for this classification task.

Main Methods:

  • * Utilized 12 databases comprising 9,029 samples, with the Shoham (1982) dataset (5,675 samples, six flow patterns) as the primary source.
  • * Trained and tested various machine learning and deep learning models, including Extra Trees.
  • * Employed extensive metrics to validate model performance and identify significant features.

Main Results:

  • * Gas and liquid superficial velocities, pipe angle of inclination, and diameter were identified as the most relevant features for model training.
  • * The Extra Trees model demonstrated superior performance, achieving a classification accuracy of 98.8%.
  • * The study successfully identified optimal AI alternatives for two-phase flow pattern classification.

Conclusions:

  • * AI, particularly the Extra Trees algorithm, offers a highly accurate solution for classifying two-phase flow patterns.
  • * Understanding the influence of physical and structural variables is key to developing effective AI models for fluid dynamics.
  • * This research provides valuable insights for optimizing processes involving two-phase flow in pipes.