Open AccessMachine learning applications to predict two-phase flow patterns
Author(s) -
Harold Brayan Arteaga-Arteaga,
Alejandro Mora-Rubio,
Frank Florez,
Nicolas Murcia-Orjuela,
Cristhian Eduardo Diaz-Ortega,
Simón Orozco-Arias,
Melissa delaPava,
Mario Alejandro Bravo-Ortíz,
Melvin Robinson,
Pablo Guillén-Rondon,
Reinel Tabares-Soto
Publication year - 2021
Publication title -
peerj. computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.806
H-Index - 24
ISSN - 2376-5992
DOI - 10.7717/peerj-cs.798
Subject(s) - flow (mathematics) , artificial intelligence , computer science , machine learning , set (abstract data type) , two phase flow , fidelity , work flow , mathematics , industrial engineering , engineering , geometry , telecommunications , programming language
Recent advances in artificial intelligence with traditional machine learning algorithms and deep learning architectures solve complex classification problems. This work presents the performance of different artificial intelligence models to classify two-phase flow patterns, showing the best alternatives for this specific classification problem using two-phase flow regimes (liquid and gas) in pipes. Flow patterns are affected by physical variables such as superficial velocity, viscosity, density, and superficial tension. They also depend on the construction characteristics of the pipe, such as the angle of inclination and the diameter. We selected 12 databases (9,029 samples) to train and test machine learning models, considering these variables that influence the flow patterns. The primary dataset is Shoham (1982), containing 5,675 samples with six different flow patterns. An extensive set of metrics validated the results obtained. The most relevant characteristics for training the models using Shoham (1982) dataset are gas and liquid superficial velocities, angle of inclination, and diameter. Regarding the algorithms, the Extra Trees model classifies the flow patterns with the highest degree of fidelity, achieving an accuracy of 98.8%.