PAULO HENRIQUE FERRAZ MASOTTI
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Artigo IPEN-doc 25565 Two-phase flow void fraction estimation based on bubble image segmentation using Randomized Hough Transform with Neural Network (RHTN)2020 - SERRA, PEDRO L.S.; MASOTTI, PAULO H.F.; ROCHA, MARCELO S.; ANDRADE, DELVONEI A. de; TORRES, WALMIR M.; MESQUITA, ROBERTO N. deThe 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 (< 0.004%) due robustness obtained through integration between Artificial Neural Networks with Randomized Hough Transforms.Artigo IPEN-doc 25071 Two-phase flow bubble detection method applied to natural circulation system using fuzzy image processing2018 - BUENO, R.C.; MASOTTI, P.H.F.; JUSTO, J.F.; ANDRADE, D.A.; ROCHA, M.S.; TORRES, W.M.; MESQUITA, R.N. deNatural circulation cooling systems are currently used in new nuclear reactors. Over the last decades, research in these systems has focused in the study of flow and heat transfer parameters. A particular area of interest is the estimation of two-phase flow parameters by image processing and pattern recognition using intelligent processing. Several methods have been proposed to identify objects of interest in bubbly two-phase images. Edge detection is an important task to estimate flow parameters, in which the bubbles are segmented to obtain several features, such as void fraction, area, and diameter. However, current methods face difficulties in determining those parameters in high bubble-density two-phase flow images. Here, a new edge detection method is proposed to segment bubbles in natural circulation instability images. The new method (Fuzzy Contrast Standard Deviation – FUZCON) uses Fuzzy Logic and image standard deviation estimates of locally measured contrast levels. Images were obtained through an experimental circuit made of glass, which enables imaging flow patterns of natural circulation cycles at ambient pressure. The results indicated important improvements on edge detection efficiency for high void fraction estimation on high-density two-phase flow bubble images, when compared to classical detectors, without the need to use smoothing algorithms or human intervention.