Detection of Horizontal Two-Phase Flow Patterns Through a Neural Network Model



Document title: Detection of Horizontal Two-Phase Flow Patterns Through a Neural Network Model
Journal: Journal of the Brazilian Society of Mechanical Sciences
Database: PERIÓDICA
System number: 000312133
ISSN: 0100-7386
Authors: 1

2
Institutions: 1Universidade de Sao Paulo, Sao Carlos, Sao Paulo. Brasil
2Commissariat a l'Energie Atomique et aux Energies Alternatives, Grenoble, Isere. Francia
Year:
Season: Mar
Volumen: 24
Number: 1
Pages: 70-75
Country: Brasil
Language: Inglés
Document type: Artículo
Approach: Experimental
English abstract One of the main problems related to the transport and manipulation of multiphase fluids concerns the existence of characteristic flow patterns and its strong influence on important operation parameters. A good example of this occurs in gas-liquid chemical reactors in which maximum efficiencies can be achieved by maintaining a finely dispersed bubbly flow to maximize the total interfacial area. Thus, the ability to automatically detect flow patterns is of crucial importance, especially for the adequate operation of multiphase systems. This work describes the application of a neural model to process the signals delivered by a direct imaging probe to produce a diagnostic of the corresponding flow pattern. The neural model is constituted of six independent neural modules, each of which trained to detect one of the main horizontal flow patterns, and a last winner-take-all layer responsible for resolving when two or more patterns are simultaneously detected. Experimental signals representing different bubbly, intermittent, annular and stratified flow patterns were used to validate the neural model
Disciplines: Física y astronomía,
Matemáticas
Keyword: Dinámica de fluidos,
Matemáticas aplicadas,
Flujo multifásico,
Patrones de flujo,
Redes neuronales,
Señales,
Análisis
Keyword: Physics and astronomy,
Mathematics,
Fluid dynamics,
Applied mathematics,
Multiphase flow,
Flow pattern,
Neural networks,
Signals,
Analysis
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