WALMIR MAXIMO TORRES
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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 25814 Procedures for manufacturing an instrumented nuclear fuel element2019 - DURAZZO, M.; UMBEHAUN, P.E.; TORRES, W.M.; SOUZA, J.A.B.; SILVA, D.G.; ANDRADE, D.A.The IEA-R1 is an open pool research reactor that operated for many years at 2 MW. The reactor uses plate type fuel elements which are formed by assembling eighteen parallel fuel plates. During the years of reactor operation at 2 MW, thermohydraulic safety margins with respect to design limits were always very high. However, more intense oxidation on some external fuel plates was observed when the reactor power was increased to 5 MW. At this new power level, the safety margins are significantly reduced due to the increase of the heat flux on the plates. In order to measure, experimentally, the fuel plate temperature under operation, an instrumented fuel element was constructed to obtain temperature experimental data at various positions of one or more fuel plates in the fuel element. The manufacturing method is characterized by keeping the original fuel element design specifications. Type K stainless sheathed thermocouples are mounted into supports pads in unrestricted positions. During the fuel element assembling, the supports pads with the thermocouples are mechanically fixed by interference between two adjacent fuel plates. The thermocouple wires are directed through the space existing at the bottom of the mounting slot where the fuel plate is fixed to the side plates. The number of thermocouples installed is not restricted and depends only on adaptations that can be made on the mounting slots of the standard fuel element side plates. This work describes the manufacturing procedures for assembling such an instrumented fuel element.