Classification of natural circulation two-phase flow patterns using fuzzy inference on image analysis

Mesquita, Roberto Navarro de; Masotti, P.; L., Penha, Rosani,; Andrade, Delvonei Alves de; Sabundjian, Gaiane; Torres, Walmir M.; Macedo, Luiz A.

doi:10.1016/j.nucengdes.2012.06.014

Cited by 19 publications

(12 citation statements)

References 24 publications

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“…Some works have been done to determine flow patterns using fuzzy inference on image analysis. In [99], the authors implemented image pattern classification systems based on simple features using a fuzzy inference system. The TPF regime identification system to manage heat transfer of the nuclear reactor coolant was designed in [23].…”

Section: Flow Regime Identificationmentioning

confidence: 99%

Computer methods for non-invasive measurement and control of two-phase flows: a review study

Wajman

2019

ITC

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Nowadays, the application of advanced technologies in modern production systems is the main trend of development and technological progress in many industrial sectors. It is due to the still growing trends of energy-saving and production quality enhancement. Wherever in the production process the phase mixture is transported and it is not optimal or not economic, there is a need to develop a system which would be able to prevent any construction disaster, unexpected production line stopping or situation where for reasons of bad flow parameters, the final product is defective. This paper studies various sensors, measurement techniques and computer methods for signal processing and analysis to diagnose and control two-phase flows. Due to the possibility that the invasive measurement disturbs the process and changes it parameters and behavior especially in the location just after the measurement point and simultaneously does not provide any information about these changes it is unreliable for the diagnosis or control. Therefore, the non-invasive techniques commonly used for measurement of flows parameters are described. Depending on the industrial demands many of applications examples for non-invasive two-phase flows measurement and monitoring are given. This description for identifying the flow parameters is divided into features categories of this phenomena as void fraction distribution, velocity profile and flow regime. However, from these methods the high accuracy and short processing time is expected. The continued observation and monitoring of the process abnormalities can provide valuable information about its dynamic state and allow for real-time and automatic monitoring and control. Therefore, the development of advanced process control is one of the most important challenges to keep the flow regime on the given level and for instant and long-term energy saving, quality improvement.

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Section: Flow Regime Identificationmentioning

confidence: 99%

Computer methods for non-invasive measurement and control of two-phase flows: a review study

Wajman

2019

ITC

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“…A few examples are: support vector machines [16,17], artificial neural networks [18][19][20] and fuzzy inference systems [21,22].…”

Section: Introductionmentioning

confidence: 99%

Multiphase flow parameter estimation based on laser scattering

Vendruscolo

Fischer

Martelli

et al. 2015

Meas. Sci. Technol.

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The flow of multiple constituents inside a pipe or vessel, known as multiphase flow, is commonly found in many industry branches. The measurement of the individual flow rates in such flow is still a challenge, which usually requires a combination of several sensor types. However, in many applications, especially in industrial process control, it is not necessary to know the absolute flow rate of the respective phases, but rather to continuously monitor flow conditions in order to quickly detect deviations from the desired parameters. Here we show how a simple and low-cost sensor design can achieve this, by using machine-learning techniques to distinguishing the characteristic patterns of oblique laser light scattered at the phase interfaces. The sensor is capable of estimating individual phase fluxes (as well as their changes) in multiphase flows and may be applied to safety applications due to its quick response time.

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“…Assim, é aprofundada a estrutura física do Circuito de Circulação A aquisição das imagens de escoamento bifásico utilizadas nesse trabalho, formando uma base de dados, foi realizada em um período anterior ao início desse trabalho. Essas imagens foram base de outros trabalhos previamente publicados pela equipe do Centro de Engenharia Nuclear (De Mesquita et al, 2010, 2012. Portanto, esse trabalho foi realizado com maior ênfase nos aspectos computacionais para o desenvolvimento das técnicas de classificação de imagens.…”

Section: Formação Da Base De Dadosunclassified

Classificação de padrões de escoamento bifásico por meio de redes neurais convolucionais

Schott¹

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VI RESUMO SCHOTT, Sandro. M. C. Classificação de padrões de escoamento bifásico por meio de redes neurais convolucionais. 142 p. Dissertação (Mestrado em Tecnologia Nuclear) Instituto de Pesquisas Energéticas e Nucleares -IPEN-CNEN/SP. São Paulo. Sistemas passivos, como circulação natural, têm sido cada vez mais utilizados para refrigeração de reatores nucleares. A capacidade de um fluido de transferir calor está fortemente relacionada com seu padrão de escoamento, especialmente quando em escoamento bifásico. Estes padrões vêm sendo utilizados em experimentos e modelos de predição de parâmetros que medem esta capacidade. Uma das técnicas não invasivas que vêm sendo utilizadas é a automatização da determinação do padrão de escoamento por meio de imagens. Este trabalho aplicou Redes Neurais Convolucionais para a classificação de imagens de diferentes padrões de escoamento bifásico relacionados à instabilidade chugging da circulação natural. Estas redes, que têm sido o estado-da-arte em classificação de imagens, não se baseiam em características pré-escolhidas, permitindo investigação de novas características para essa tarefa. São comparadas arquiteturas destas redes com diferentes graus de complexidade. Atualmente, a aplicação destas redes ao problema de escoamento bifásico é uma área pouco explorada.No subconjunto de teste, foi obtido um F1-Score médio ponderado de 0,99 e uma acurácia de 99,5%. Os resultados do trabalho mostram que as redes neurais convolucionais apresentam um bom desempenho preditivo e que detêm recursos ainda não explorados para fins de classificação de padrões em imagens de escoamento bifásico.

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Classification of natural circulation two-phase flow patterns using fuzzy inference on image analysis

Cited by 19 publications

References 24 publications

Computer methods for non-invasive measurement and control of two-phase flows: a review study

Computer methods for non-invasive measurement and control of two-phase flows: a review study

Multiphase flow parameter estimation based on laser scattering

Classificação de padrões de escoamento bifásico por meio de redes neurais convolucionais

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