WALMIR MAXIMO TORRES
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Artigo IPEN-doc 30386 Verification and validation of seven turbulence models for a natural circulation loop under transient conditions2024 - ANGELO, G.; ANGELO, E.; SCURO, N.L.; TORRES, W.M.; ANDRADE, D.A.A numerical study of the vertical heater, vertical cooler (VHVC) natural circulation loop (NCL) at IPEN/CNEN-SP was conducted using a three-dimensional and transient mathematical model analyzed with the commercial software ANSYS CFX. The study focused on the stable and single-phase flow regime, with a Rayleigh number ranging from zero to 2.8×108. Seven turbulence models have been benchmarked: Zero Equation, Eddy Viscosity Transport Equation (EVTE), k−ω, k−ɛ, Shear Stress Transport (SST), Reynolds Stress (SSG), and Detached Eddy Simulation (DES). The results of these models were compared against each other and against experimental results obtained specifically for this purpose, focusing on the spatial distribution and temporal evolution of temperature at various points in the natural circulation loop. Among all tested models, the k−ɛ model demonstrated superior performance with the lowest average deviation, exhibiting lower initial turbulence production and buoyancy effects than the more complex models. This behavior suggests that the k−ɛ model is more accurate in predicting temperature distribution and is a better choice for transient flow analysis in natural circulation loops with similar geometries to those presented in this study.Artigo IPEN-doc 30370 Assessment of the IEA-R1 nuclear reactor using a nonstandard fuel assembly with six fuel plates of the Brazilian Multipurpose Reactor2024 - SOARES, HUMBERTO V.; TORRES, WALMIR M.; UMBEHAUN, PEDRO E.; BELCHIOR, ANTONIO; ANDRADE, DELVONEI A. deIn order to qualify the fuel plates of the Brazilian Multipurpose Reactor (RMB), a nonstandard Instrumented Fuel Assembly (IFA) was designed and is being constructed to be burned in the IEA-R1 nuclear research reactor. IFA has fuel plates of different uranium densities (10 fixed fuel plates of 3.0 gU/cm3 – IEA-R1 standard; 6 removable fuel plates of 3.7 gU/cm3 – RMB; and a central aluminum plate). This paper is the first step to demonstrate that IEA-R1 can safely operate with this IFA. To verify the IFA thermal behavior inside the IEA-R1 core during reactor operation and certify the no power peaks occurrence, the power distribution was calculated for each fuel plate. LEOPARD and HAMMER-TECHNION codes were utilized to calculate the core thermal neutron cross section and CITATION code to calculate the core power distribution. Calculations were performed for 5 MW reactor power considering the IFA placed in a core peripheral position. The RMB fuel plates average power was 4.73 % higher compared to IEA-R1 fuel plates. This was expected due to the higher density of uranium in these plates. The power of each IFA fuel plate was compared with a fresh IEA-R1 Fuel Assembly (FA) at the same core position. The power in the IFA hottest plate is only 6.79 % higher than the correspondent IEA-R1 fuel plate. The IFA power distribution was also compared to the hottest FA of the core. The power of each IFA fuel plate was below its correspondent hottest FA fuel plate. In addition, the total IFA power is 18.40 % less than the hottest FA in the core. No significant power peaks occur in the IFA during operation. As future works, thermal–hydraulic calculations will be performed considering this calculated power distribution and no hot spots are expected.Artigo IPEN-doc 27183 Total and partial loss of coolant experiments in an instrumented fuel assembly of IEA-R1 research reactor2020 - MAPRELIAN, EDUARDO; TORRES, WALMIR M.; BELCHIOR JUNIOR, ANTONIO; UMBEHAUN, PEDRO E.; BERRETTA, JOSE R.; SABUNDJIAN, GAIANEThe safety of nuclear facilities has been a growing global concern, mainly after the Fukushima nuclear accident. Studies on nuclear research reactor accidents such as the Loss of Coolant Accident (LOCA), many times considered a design basis accident, are important for ensure the integrity of the plant. A LOCA may lead to the partial or complete uncovering of the fuel assemblies and it is necessary to assure the decay heat removal as a safety condition. This work aimed to perform, in a safe way, partial and complete uncovering experiments for an Instrumented Fuel Assembly (IFA), in order to measure and compare the actual fuel temperatures behavior for LOCA in similar conditions to research reactors. A test section for experimental simulation of Loss of Coolant Accident named STAR was designed and built. The IFA was irradiated in the IEA-R1 core and positioned in the STAR, which was totally immersed in the reactor pool. Thermocouples were installed in the IFA to measure the clad and fluid temperatures in several axial and radial positions. Experiments were carried out for five levels of uncovering of IFA, being one complete uncovering and four partial uncovering, in two different conditions of decay heat. It was observed that the cases of complete uncovering of the IFA were the most critical ones, that is, those cases presented higher clad temperatures when compared with partial uncovering cases, for the specific conditions of heat decay intensity and dissipation analyzed. The maximum temperatures reached in all experiments were quite below the fuel blister temperature, which is around 500 °C. The STAR has proven to be a safe and reliable experimental apparatus for conducting loss of coolant experiments.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.Artigo IPEN-doc 24758 Classification of natural circulation two-phase flow image patterns based on self-organizing maps of full frame DCT coefficients2018 - 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.