MARCELO DA SILVA ROCHA

MARCELO DA SILVA ROCHA

(Fonte: Lattes)

Resumo

Possui graduação em Engenharia Civil pela Universidade Federal de Juiz de Fora (1996), mestrado em Engenharia Civil pela Universidade Estadual de Campinas (1998) e doutorado em Engenharia Mecânica pela Universidade de São Paulo (2005). Realizou estágio de pós-doutorado em Engenharia Mecânica na Universidade de São Paulo (2007) e em Engenharia Nuclear no Instituto de Pesquisas Energéticas e Nucleares (2009). Atualmente é Pesquisador Adjunto do Centro de Engenharia Nuclear (CEENG) do Instituto de Pesquisas Energéticas e Nucleares (IPEN-CNEN). Atua como docente e pesquisador nas áreas de termohidráulica de reatores, energias renováveis, interação fluido-estrutura e aplicações de nanotecnologia. (Texto extraído do Currículo Lattes em 16 nov. 2021)

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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.