ROBERTO NAVARRO DE MESQUITA

ROBERTO NAVARRO DE MESQUITA

(Fonte: Lattes)

Resumo

Possui graduação em Física pela Universidade Estadual de Campinas (1987), mestrado em Física pela Universidade Estadual de Campinas (1991) e doutorado em Engenharia Mecânica pela Universidade de São Paulo (2002). Atualmente é tecnologista pleno da Comissão Nacional de Energia Nuclear. Tem experiência na área de Ciência da Computação, com ênfase em Sistemas de Inteligência Artificial, atuando principalmente nos seguintes temas: inteligência artificial, diagnóstico de defeitos em tubos, correntes parasitas (ECT), reconhecimento de padrões em imagens. (Texto extraído do Currículo Lattes em 27 dez. 2021)

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CFD Simulation of isothermal upward two-phase flow in a vertical annulus using interfacial area transport equation
2023 - CERAVOLO, FLAVIO E.; ROCHA, MARCELO da S.; MESQUITA, ROBERTO N. de; ANDRADE, DELVONEI A. de
This work presents a numerical simulation of a vertical, upward, isothermal two-phase flow of air bubbles and water in an annular channel applying a Computational Fluid Dynamics (CFD) code. For this, the Two-Fluid model is applied considering interfacial force correlations, namely: drag, lift, wall lubrication, turbulent dispersion, and virtual mass. The turbulence k-ε model effects and the influence of One-group Interfacial Area Transport Equation (IATE) are taken into account, in this case, the influence of two source term correlations for the bubble breakup and coalescence IATE is analysed. The work assesses whether the code properly represents the physical phenomenon by comparing the simulation results with experimental data obtained from the literature. Six flow conditions are evaluated based on two superficial liquid velocities and three void fractions in the bubbly flow regimen. The annular channel adopted has an outer pipe with an internal diameter of 38.1 mm and an inner cylinder of 19.1 mm. To represent this geometry, a three-dimensional mesh was generated with 160,000 elements, after a mesh sensitivity study. The void fraction distribution, taken radially to the flow section, is the main parameter analysed as well as interfacial area concentration, interfacial gas velocity, and bubble sizes distribution. The CFD model implemented in this work demonstrates satisfactory agreement with the reference experimental data but indicates the need for further improvement in the phase interaction models.
Classification of two-phase flow instability phases using convolutional neural networks
2021 - SCHOTT, S.M.C.; SILVA, M.C.B. da; MESQUITA, R.N. de
Development of a real-time focus estimaton software to be applied in two-phase flow imaging using intelligent processing
2019 - SILVA, MARCONES C.B. da; SCHOTT, SANDRO M.C.; MESQUITA, ROBERTO N. de
Image processing has been an increasing research area in the last decades, especially due to crescent technological growth allied with lowering production costs. Many scientific applications have searched for establishment of quality norms associated with possible information obtainment from images. A common need from different applications has been the standardization of focus quality metric. The development of new methods for measuring the focus adjustment in order to obtain image quality metric analysis has enabled more reliable and precise data in many different industry and science sectors. Some examples are industrial equipment parts inspection using computational vision to defects classification. This work presents the initial steps to develop a methodology to estimate focus in real time in two-phase flow experiments inside tube with cylindrical geometry. This methodology is initially based on a software module using artificial intelligence methods to estimate image focus. This module is developed in LabVIEW platform using Fuzzy Logic inference base in different traditional digital focus metrics and integrated with digital cameras to increment precision on focus adjustment during two-phase flow experiments. This method will be calibrated to be used on void fraction estimation through image analysis in the natural circulation loop located at the Nuclear Engineering Center (CEN) do Instituto de Pesquisas Energéticas e Nucleares (IPEN). A set of the initial developed software modules will be presented with their respective functionalities, initial results and experimental focus estimated errors.
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.
Self-organizing maps applied to two-phase flow on natural circulation loop studies
2015 - CASTRO, LEONARDO F.; CUNHA, KELLY de P.; ANDRADE, DELVONEI A. de; SABUNDJIAN, GAIANE; TORRES, WALMIR M.; MACEDO, LUIZ A.; ROCHA, MARCELO da S.; MASOTTI, PAULO H.F.; MESQUITA, ROBERTO N. de
Study of the natural circulation phenomenon for nuclear reactors
2010 - CONTI, THADEU das N.; SABUNDJIAN, GAIANE; UMBEHAUN, PEDRO E.; MESQUITA, ROBERTO N.
Study of the natural circulation phenomenon for nuclear reactors
2010 - CONTI, THADEU das N.; SABUNDJIAN, GAIANE; TORRES, WALMIR M.; MACEDO, LUIZ A.; ANDRADE, DELVONEI A.; UMBEHAUN, PEDRO E.; MESQUITA, ROBERTO N.
Thermal hydraulic phenomenology in a natural circulation circuit
2010 - TORRES, WALMIR M.; MACEDO, LUIZ A.; MESQUITA, ROBERTO N.; MASOTTI, PAULO H.F.; SABUNDJIAN, GAIANE; ANDRADE, DELVONEI A.; UMBEHAUN, PEDRO E.