Visual Experimental Study on Two Phase Flow Patterns of the Evaporative Cooling System

Shi, Yu; Cao, Rui; Ruan, Lin

doi:10.1109/access.2021.3050240

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…Given the considerable number of variables, determining the flow pattern is not a trivial task, however, its accurate classification is essential for the proper design and planning of systems with pipelines ( Priyanka, Maheswari & Thangavel, 2018 ; Herrán, de la Cruz & de Andrés, 2010 ). Multiphase flow is present in different industrial processes with gas or oil pipelines, condensation, or boiling systems, even in nuclear reactor cooling systems ( Wang et al, 1990 ; Ghiaasiaan, 2007 ; Al-Zaidi, Mahmoud & Karayiannis, 2018 ; Shi, Cao & Ruan, 2021 ). They are also present in oil transportation systems where oil is extracted and transported through a network of pipes to processing plants ( Bannwart et al, 2004 , Al-Safran & Al-Qenae, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Machine learning applications to predict two-phase flow patterns

Arteaga-Arteaga

Mora-Rubio

Florez

et al. 2021

PeerJ Computer Science

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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%.

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