PAULO HENRIQUE FERRAZ MASOTTI

PAULO HENRIQUE FERRAZ MASOTTI

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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. de
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 (< 0.004%) due robustness obtained through integration between Artificial Neural Networks with Randomized Hough Transforms.
Two-phase flow bubble detection method applied to natural circulation system using fuzzy image processing
2018 - BUENO, R.C.; MASOTTI, P.H.F.; JUSTO, J.F.; ANDRADE, D.A.; ROCHA, M.S.; TORRES, W.M.; MESQUITA, R.N. de
Natural 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.
Classification of natural circulation two-phase flow image patterns based on self-organizing maps of full frame DCT coefficients
2018 - MESQUITA, ROBERTO N. de; CASTRO, LEONARDO F.; TORRES, WALMIR M.; ROCHA, MARCELO da S.; UMBEHAUN, PEDRO E.; ANDRADE, DELVONEI A.; SABUNDJIAN, GAIANE; MASOTTI, PAULO H.F.
Many of the recent nuclear power plant projects use natural circulation as heat removal mechanism. The accuracy of heat transfer parameters estimation has been improved through models that require precise prediction of two-phase flow pattern transitions. Image patterns of natural circulation instabilities were used to construct an automated classification system based on Self-Organizing Maps (SOMs). The system is used to investigate the more appropriate image features to obtain classification success. An efficient automated classification system based on image features can enable better and faster experimental procedures on two-phase flow phenomena studies. A comparison with a previous fuzzy inference study was foreseen to obtain classification power improvements. In the present work, frequency domain image features were used to characterize three different natural circulation two-phase flow instability stages to serve as input to a SOM clustering algorithm. Full-Frame Discrete Cosine Transform (FFDCT) coefficients were obtained for 32 image samples for each instability stage and were organized as input database for SOM training. A systematic training/test methodology was used to verify the classification method. Image database was obtained from two-phase flow experiments performed on the Natural Circulation Facility (NCF) at Instituto de Pesquisas Energéticas e Nucleares (IPEN/CNEN), Brazil. A mean right classification rate of 88.75% was obtained for SOMs trained with 50% of database. A mean right classificationrate of 93.98% was obtained for SOMs trained with 75% of data. These mean rates were obtained through 1000 different randomly sampled training data. FFDCT proved to be a very efficient and compact image feature to improve image-based classification systems. Fuzzy inference showed to be more flexible and able to adapt to simpler statistical features from only one image profile. FFDCT features resulted in more precise results when applied to a SOM neural network, though had to be applied to the full original grayscale matrix for all flow images to be classified.
Thermal hydraulic phenomenology for the heating process in a natural circulation facility
2009 - TORRES, WALMIR M.; MACEDO, LUIZ A.; MESQUITA, ROBERTO N.; MASOTTI, PAULO H.F.; LIBARDI, ROSANI M.P.; SABUNDJIAN, GAIANE; ANDRADE, DELVONEI A.; UMBEHAUN, PEDRO E.; CONTI, THADEU N.; FILHO, MAURO F.S.; MELO, GABRIEL R.
Comparison between experimental data and numerical modeling for the natural circulation phenomenon
2009 - SABUNDJIAN, GAIANE; ANDRADE, DELVONEI A.; UMBEHAUN, PEDRO E.; TORRES, WALMIR M.; MACEDO, LUIZ A.; CONTI, THADEU N.; MESQUITA, ROBERTO N.; MASOTTI, PAULO H.F.; PENHA, ROSANI M.L.; SILVA FILHO, MAURO F.; MELO, GABRIEL R.; BRAZ FILHO, FRANCISCO A.; BORGES, EDUARDO M.
Frequency analysis for the thermal hydraulic characterization of a natural circulation circuit
2011 - TORRES, WALMIR M.; MACEDO, LUIZ A.; SABUNDJIAN, GAIANE; ANDRADE, DELVONEI A.; UMBEHAUN, PEDRO E.; CONTI, THADEU N.; MESQUITA, ROBERTO N.; MASOTTI, PAULO H.; ANGELO, GABRIEL
A RELAP5 study to identify flow regime in natural circulation phenomenon
2011 - SABUNDJIAN, GAIANE; TORRES, WALMIR M.; MACEDO, LUIZ A.; MESQUITA, ROBERTO N.; ANDRADE, DELVONEI A.; UMBEHAUN, PEDRO E.; CONTI, THADEU N.; MASOTTI, PAULO H.F.; BELCHIOR JUNIOR, ANTONIO; ANGELO, GABRIEL
Development of a labview web-based simulator for RELAP5
2011 - MACEDO, LUIZ A.; TORRES, WALMIR M.; SABUNDJIAN, GAIANE; ANDRADE, DELVONEI A.; BELCHIOR JUNIOR, ANTONIO; UMBEHAUN, PEDRO E.; CONTI, THADEU N.; MESQUITA, ROBERTO N. de; MASOTTI, PAULO H.F.; ANGELO, GABRIEL
Two-phase flow patterns recognition and parameters estimation through natural circulation test loop image analysis
2009 - MESQUITA, R.N.; LIBARDI, R.M.P.; MASOTTI, P.H.F.; SABUNDJIAN, G.; ANDRADE, D.A.; UMBEHAUN, P.E.; TORRES, W.M.; CONTI, T.N.; MACEDO, L.A.
Classification of natural circulation two-phase flow patterns using fuzzy inference on image analysis
2012 - MESQUITA, R.N. de; MASOTTI, P.H.F.; PENHA, R.M.L.; ANDRADE, D.A.; SABUNDJIAN, G.; TORRES, W.M.; MACEDO, L.A.