Two-Phase Slug Flow Characterization Using Artificial Neural Networks

Gas-liquid two-phase flows are present in nature and in different industrial activities alike, such as the chemical, petroleum, and nuclear industries. In this type of flow, the liquid and gas phases assume different spatial configurations inside the pipe, called flow patterns. The mathematical mode...

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Bibliographic Details
Published in:	IEEE transactions on instrumentation and measurement Vol. 65; no. 3; pp. 494 - 501
Main Authors:	Cozin, Cristiane, Vicencio, Fernando E. C., Arinos de Almeida Barbuto, Fausto, Morales, Rigoberto E. M., da Silva, Marco Jose, Arruda, Lucia Valeria R.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01-03-2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Artificial neural networks Bubbles Computational modeling Flow characterization Learning theory Liquids Mathematical model Mathematical models Mean square errors Neural networks Numerical models Slug flow Time series Time series analysis Void fraction wire mesh sensor slug flow Flow characterization wire mesh sensor neural networks
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Summary:	Gas-liquid two-phase flows are present in nature and in different industrial activities alike, such as the chemical, petroleum, and nuclear industries. In this type of flow, the liquid and gas phases assume different spatial configurations inside the pipe, called flow patterns. The mathematical modeling of slug flow comprises from simple steady-state models to more complex models for transient regimes. Those models require closure relationships, e.g., empirical correlations and statistical distributions of characteristic flow parameters. In this paper, a model based on artificial neural networks (ANNs) for predicting the two-phase slug flow behavior is proposed. With this ANN model, the parameters that characterize the flow are extracted from the time series of void fractions obtained experimentally. The variables of interest are superficial velocities of the fluids, liquid slug and gas bubble lengths, and the bubble translational velocity and their standard deviations. The knowledge and understanding of those parameters will improve the characterization of the intermittent slug flows and will also provide information on the development of physical models that describe this phenomenon, such as the unit cell models, the drift flux model and the slug tracking model. In general, the estimation models based on ANNs showed good results compared with reference values obtained experimentally. The results show that the estimation models present a mean square error below 2%. The methodology presented here, combining experimentally obtained void fraction time series and ANN, is an appropriated method to infer flow parameters and thus to support slug flow characterization.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0018-9456 1557-9662
DOI:	10.1109/TIM.2015.2510578