2010
DOI: 10.1590/s1678-58782010000100003
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On the application of self-organizing neural networks in gas-liquid and gas-solid flow regime identification

Abstract: One of the main problems associated with the transport and manipulation of multiphase

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Cited by 17 publications
(5 citation statements)
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“…In forced convection flow the motion of electrolyte bulk exert drag force on the bubbles which is summed to buoyancy force to overcome the interfacial tension force at the vicinity of electrode-gas interphase 95 (b) Flow regime of gas-liquid phase in horizontal flow. 96 and lift force tries to push the bubble away from the electrode surface. The growing bubbles on the electrode surface more likely move from the nucleation vicinity along the electrode surface until they coalesce/collide with another bubble, at which time the volume of the collided bubbles reaches the critical diameter.…”
Section: Effect Of Bulk Flow On Bubblesmentioning
confidence: 99%
See 1 more Smart Citation
“…In forced convection flow the motion of electrolyte bulk exert drag force on the bubbles which is summed to buoyancy force to overcome the interfacial tension force at the vicinity of electrode-gas interphase 95 (b) Flow regime of gas-liquid phase in horizontal flow. 96 and lift force tries to push the bubble away from the electrode surface. The growing bubbles on the electrode surface more likely move from the nucleation vicinity along the electrode surface until they coalesce/collide with another bubble, at which time the volume of the collided bubbles reaches the critical diameter.…”
Section: Effect Of Bulk Flow On Bubblesmentioning
confidence: 99%
“…Figure 5. Flow patterns: (a) Flow regime of gas-liquid phases in vertical flow,95 (b) Flow regime of gas-liquid phase in horizontal flow 96. …”
mentioning
confidence: 99%
“…When an input pattern is presented to the network, the best-matching node (winner node) is found by the ED estimation. The weights of the winner have a chance to adjust in a gradient step [19][20][21]30]. After 600 training iterations, it takes 15.67 s to cluster the 50 training patterns into 5 Classes.…”
Section: Learning Performancesmentioning
confidence: 99%
“…In the oil field, machine learning has been widely used in research due to its great potential to solve problems [6][7][8][9][10]. This deep learning method has a great performance in price prediction, in which there is a study in the oil price forecast with this tool [11].…”
Section: Introductionmentioning
confidence: 99%