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Multiphase Science and Technology

年間 4 号発行

ISSN 印刷: 0276-1459

ISSN オンライン: 1943-6181

SJR: 0.144 SNIP: 0.256 CiteScore™:: 1.1 H-Index: 24

Indexed in

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

巻 24, 発行 2, 2012, pp. 89-103
DOI: 10.1615/MultScienTechn.v24.i2.10
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要約

Measurement of the density of multiphase flow is crucial in multiphase flow meters. While radioactive-based measuring methods are known to give good results, end users prefer nonradioactive devices for obvious reasons. This paper shows the capability of an online neural network algorithm, that uses the differential pressure along a vertical pipe and across a venturi meter, in predicting the average density of the multiphase flow. A multiphase flow loop was constructed to conduct the training and evaluating experiments needed for the algorithm. Experimental results performed on the multiphase flow loop demonstrate that the density measurement can be achieved with good accuracy for liquid and gas velocities for up to 4 and 25 m/s (at 30° C and 7 bars), respectively, while covering different complex flow regimes including annular flow, slug flow, and dispersed flow. The neural network algorithm that was developed for this purpose gave very good results in measuring the flow density.

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  1. Ma Ming, Lu Jiacai, Tryggvason Gretar, Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system, Physics of Fluids, 27, 9, 2015. Crossref

  2. Obie Ogheneochuko, Lucas Gary P., Description of the Design and Experimental Characterization of a Novel Densitometry System for Measuring Density in Single Phase and Multiphase Pipe Flows, IEEE Sensors Journal, 18, 11, 2018. Crossref

  3. Jung Hokyo, Yoon Serin, Kim Youngjae, Lee Jun Ho, Park Hyungmin, Kim Dongjoo, Kim Jungwoo, Kang Seongwon, Development and evaluation of data-driven modeling for bubble size in turbulent air-water bubbly flows using artificial multi-layer neural networks, Chemical Engineering Science, 213, 2020. Crossref

  4. Frank Michael, Drikakis Dimitris, Charissis Vassilis, Machine-Learning Methods for Computational Science and Engineering, Computation, 8, 1, 2020. Crossref

  5. Hotvedt Mathilde, Grimstad Bjarne, Ljungquist Dag, Imsland Lars, On gray-box modeling for virtual flow metering, Control Engineering Practice, 118, 2022. Crossref

  6. Hotvedt M., Grimstad B., Imsland L., Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter - a Case Study, IFAC-PapersOnLine, 53, 2, 2020. Crossref

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