Machine learning applications to predict two-phase flow patterns
| Autor: | Frank Florez, Melvin Robinson, Simon Orozco-Arias, Reinel Tabares-Soto, Pablo Guillen-Rondon, Alejandro Mora-Rubio, Nicolas Murcia-Orjuela, Cristhian Eduardo Diaz-Ortega, Mario Alejandro Bravo-Ortiz, Melissa delaPava, Harold Brayan Arteaga-Arteaga |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Flow patterns classification
General Computer Science Extra trees business.industry Computer science Deep learning Feature extraction Data Mining and Machine Learning Data Science QA75.5-76.95 Machine learning computer.software_genre Physics::Fluid Dynamics Algorithms and Analysis of Algorithms Artificial Intelligence Electronic computers. Computer science Artificial intelligence Two-phase flow business computer |
| Zdroj: | PeerJ Computer Science, Vol 7, p e798 (2021) PeerJ Computer Science |
| ISSN: | 2376-5992 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
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