Two-phase flow void fraction estimation based on bubble image segmentation using Randomized Hough Transform with Neural Network (RHTN)

The International Atomic Energy Agency (IAEA) has been encouraging the use of passive cooling systems in new designs of nuclear power plants. Next nuclear reactor generations are intended to have simpler and robust safety resources. Natural Circulation based systems hold an undoubtedly prominent pos...

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Bibliographic Details
Published in:Progress in nuclear energy (New series) 2020-01, Vol.118, p.103133, Article 103133
Main Authors: Serra, Pedro L.S., Masotti, Paulo H.F., Rocha, Marcelo S., de Andrade, Delvonei A., Torres, Walmir M., de Mesquita, Roberto N.
Format: Article
Language:English
Subjects:
Artificial neural networks
Bubbles
Circuits
Cooling systems
Depth of field
Flow stability
Fuzzy logic
Glass
Heat transfer
Hough transformation
Image acquisition
Image analysis
Image detection
Image segmentation
Industrial safety
Masks
Natural circulation
Neural networks
Nuclear engineering
Nuclear power plant
Nuclear power plants
Nuclear reactors
Nuclear safety
Parameter estimation
Passive systems
Pattern recognition
Randomization
Randomized Hough Transform
Stability analysis
Tubes
Two phase flow
Void fraction
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Summary:The International Atomic Energy Agency (IAEA) has been encouraging the use of passive cooling systems in new designs of nuclear power plants. Next nuclear reactor generations are intended to have simpler and robust safety resources. Natural Circulation based systems hold an undoubtedly prominent position among these. The study of limiting conditions of these systems has led to instability behavior analysis where many different two-phase flow patterns are present. Void fraction is a key parameter in thermal transfer analysis of these flow instability conditions. This work presents a new method to estimate void fraction from images captured of an experimental two-phase flow circuit. The method integrates a set of Artificial Neural Networks with a modified Randomized Hough Transform to make multiple scans over acquired images, using crescent-sized masks. This method was called Randomized Hough Transform with Neural Network (RHTN). Each different mask size is chosen according with bubble sizes, which are the main ‘objects of interest’ in this image analysis. Images are segmented using fuzzy inference with different parameters adjusted based on acquisition focus. Void fraction calculation considers the volume of the imaged geometrical section of flow inside cylindrical glass tubes considering the acquisition depth-of-field used. The bubble volume is estimated based on geometrical parameters inferred for each detected bubble. The image database is obtained from experiments performed on a vertical two-phase flow circuit made of cylindrical glass where flow-patterns visualization is possible. The results have shown that the estimation method had good agreement with increasing void fraction experimental values. RHTN has been very efficient as bubble detector with very low ‘false-positive’ cases (
ISSN:0149-1970
1878-4224
DOI:10.1016/j.pnucene.2019.103133