Accident Management Actions In An Upper-Head Small-Break Loss-Of-Coolant Accident With High-Pressure Safety Injection Failed

Since the Three Mile Island accident, an important focus of pressurized water reactor (PWR) transient analyses has been a small-break loss-of-coolant accident (SBLOCA). In 2002, the discovery of thinning of the vessel head wall at the Davis Besse nuclear power plant reactor indicated the possibility of an SBLOCA in the upper head of the reactor vessel as a result of circumferential cracking of a control rod drive mechanism penetration nozzle - which has cast even greater importance on the study of SBLOCAs. Several experimental tests have been performed at the Large Scale Test Facility to simulate the behavior of a PWR during an upper-head SBLOCA. The last of these tests, Organisation for Economic Co-operation and Development Nuclear Energy Agency Rig of Safety Assessment (OECD/NEA ROSA) Test 6.1, was performed in 2005. This test was simulated with the TRACE 5.0 code, and good agreement with the experimental results was obtained. Additionally, a broad analysis of an upper-head SBLOCA with high-pressure safety injection failed in a Westinghouse PWR was performed taking into account different accident management actions and conditions in order to check their suitability. This issue has been analyzed also in the framework of the OECD/NEA ROSA project and the Code Applications and Maintenance Program (CAMP). The main conclusion is that the current emergency operating procedures for Westinghouse reactor design are adequate for these kinds of sequences, and they do not need to be modified.

Bubble growth by injection of gas into viscous liquids in cylindrical and conical tubes.

The laminar low Mach number flow of a gas in a tube is analyzed for very small and very large values of the inlet-to-wall temperature ratio. When this ratio tends to zero, pressure forces confine the cold gas to a thin core around the axis of the tube. This core is neatly bounded by an ablation front that consumes it at a finite distance from the tube inlet. When the temperature ratio tends to infinity, the temperature of the gas increases smoothly from the wall to the axis of the tube and the shear stress and heat flux are positive at the wall despite the fact that the viscosity and thermal conductivity of the gas scaled with their inlet values tend to zero at the wall.

Low-injection behaviour of a solar cell with a metallic grating back-reflector

Recent studies have dealt with the possibility of increasing light absorption by using the so-called electric field enhancement taking place within the grooves of metallic gratings. In order to evaluate the potential improvements derived from the absorption increase, we employ a simplified model to analyze the low-injection behaviour of a solar cell with a metallic grating back-reflector.

TechnoFusion: new Spanish singular scientific-technical facility for fusion research

Technofusion is the scientific&technical installation for fusion research in Spain, based on three pillars: • It is an open facility to European users. • It is a facility with instrumentation not accesible to small research groups. • It is designed to be closely coordiated with the European Fusion Program. With a budget of 80-100 M€ over five years, several top laboratories will be constructed

Microstructure, magnetic and mechanical properties of Ni-Zn ferrites prepared by Powder Injection Moulding

Nowadays, the electronic industry demands small and complex parts as a consequence of the miniaturization of electronic devices. Powder injection moulding (PIM) is an emerging technique for the manufacturing of magnetic ceramics. In this paper, we analyze the sintering process, between 900 °C and 1300 °C, of Ni–Zn ferrites prepared by PIM. In particular, the densification behaviour, microstructure and mechanical properties of samples with toroidal and bar geometry were analyzed at different temperatures. Additionally, the magnetic behaviour (complex permeability and magnetic losses factor) of these compacts was compared with that of samples prepared by conventional powder compaction. Finally, the mechanical behaviour (elastic modulus, flexure strength and fracture toughness) was analyzed as a function of the powder loading of feedstock. The final microstructure of prepared samples was correlated with the macroscopic behaviour. A good agreement was established between the densities and population of defects found in the materials depending on the sintering conditions. In general, the final mechanical and magnetic properties of PIM samples were enhanced relative those obtained by uniaxial compaction.

Determination of Dipyrone in pharmaceutical preparations based on the chemiluminescent reaction of the quinolinic hydrazide-H2O2-vanadium (IV) system and flow injection analysis

A rapid, economic and sensitive chemiluminescent method involving flow-injection analysis was developed for the determination of dipyrone in pharmaceutical preparations. The method is based on the chemiluminescent reaction between quinolinic hydrazide and hydrogen peroxide in a strongly alkaline medium, in which vanadium(IV) acts as a catalyst. Principal chemical and physical variables involved in the flow-injection system were optimized using a modified simplex method. The variations in the quantum yield observed when dipyrone was present in the reaction medium were used to determine the concentration of this compound. The proposed method requires no preconcentration steps and reliably quantifies dipyrone over the linear range 1–50 µg/mL. In addition, a sample throughput of 85 samples/h is possible. Copyright © 2011 John Wiley & Sons, Ltd.

Development of an adequate model for verification of design safety-margins of the HTTR nuclear test facility

This work is based on the prototype High Engineering Test Reactor (HTTR) of the Japan Agency of Energy Atomic (JAEA). Its objective is to describe an adequate deterministic model to be used in the assessment of its design safety margins via damage domains. The concept of damage domain is defined and it is shown its relevance in the ongoing effort to apply dynamic risk assessment methods and tools based on the Theory of Stimulated Dynamics (TSD). To illustrate, we present results of an abnormal control rod (CR) withdrawal during subcritical condition and its comparison with results obtained by JAEA. No attempt is made yet to actually assess the detailed scenarios, rather to show how the approach may handle events of its kind

Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions

The aim of this research was to implement a methodology through the generation of a supervised classifier based on the Mahalanobis distance to characterize the grapevine canopy and assess leaf area and yield using RGB images. The method automatically processes sets of images, and calculates the areas (number of pixels) corresponding to seven different classes (Grapes, Wood, Background, and four classes of Leaf, of increasing leaf age). Each one is initialized by the user, who selects a set of representative pixels for every class in order to induce the clustering around them. The proposed methodology was evaluated with 70 grapevine (V. vinifera L. cv. Tempranillo) images, acquired in a commercial vineyard located in La Rioja (Spain), after several defoliation and de-fruiting events on 10 vines, with a conventional RGB camera and no artificial illumination. The segmentation results showed a performance of 92% for leaves and 98% for clusters, and allowed to assess the grapevine’s leaf area and yield with R2 values of 0.81 (p < 0.001) and 0.73 (p = 0.002), respectively. This methodology, which operates with a simple image acquisition setup and guarantees the right number and kind of pixel classes, has shown to be suitable and robust enough to provide valuable information for vineyard management.

Semi-supervised subspace clustering and applications to neuroscience

Machine learning techniques are used for extracting valuable knowledge from data. Nowa¬days, these techniques are becoming even more important due to the evolution in data ac¬quisition and storage, which is leading to data with different characteristics that must be exploited. Therefore, advances in data collection must be accompanied with advances in machine learning techniques to solve new challenges that might arise, on both academic and real applications. There are several machine learning techniques depending on both data characteristics and purpose. Unsupervised classification or clustering is one of the most known techniques when data lack of supervision (unlabeled data) and the aim is to discover data groups (clusters) according to their similarity. On the other hand, supervised classification needs data with supervision (labeled data) and its aim is to make predictions about labels of new data. The presence of data labels is a very important characteristic that guides not only the learning task but also other related tasks such as validation. When only some of the available data are labeled whereas the others remain unlabeled (partially labeled data), neither clustering nor supervised classification can be used. This scenario, which is becoming common nowadays because of labeling process ignorance or cost, is tackled with semi-supervised learning techniques. This thesis focuses on the branch of semi-supervised learning closest to clustering, i.e., to discover clusters using available labels as support to guide and improve the clustering process. Another important data characteristic, different from the presence of data labels, is the relevance or not of data features. Data are characterized by features, but it is possible that not all of them are relevant, or equally relevant, for the learning process. A recent clustering tendency, related to data relevance and called subspace clustering, claims that different clusters might be described by different feature subsets. This differs from traditional solutions to data relevance problem, where a single feature subset (usually the complete set of original features) is found and used to perform the clustering process. The proximity of this work to clustering leads to the first goal of this thesis. As commented above, clustering validation is a difficult task due to the absence of data labels. Although there are many indices that can be used to assess the quality of clustering solutions, these validations depend on clustering algorithms and data characteristics. Hence, in the first goal three known clustering algorithms are used to cluster data with outliers and noise, to critically study how some of the most known validation indices behave. The main goal of this work is however to combine semi-supervised clustering with subspace clustering to obtain clustering solutions that can be correctly validated by using either known indices or expert opinions. Two different algorithms are proposed from different points of view to discover clusters characterized by different subspaces. For the first algorithm, available data labels are used for searching for subspaces firstly, before searching for clusters. This algorithm assigns each instance to only one cluster (hard clustering) and is based on mapping known labels to subspaces using supervised classification techniques. Subspaces are then used to find clusters using traditional clustering techniques. The second algorithm uses available data labels to search for subspaces and clusters at the same time in an iterative process. This algorithm assigns each instance to each cluster based on a membership probability (soft clustering) and is based on integrating known labels and the search for subspaces into a model-based clustering approach. The different proposals are tested using different real and synthetic databases, and comparisons to other methods are also included when appropriate. Finally, as an example of real and current application, different machine learning tech¬niques, including one of the proposals of this work (the most sophisticated one) are applied to a task of one of the most challenging biological problems nowadays, the human brain model¬ing. Specifically, expert neuroscientists do not agree with a neuron classification for the brain cortex, which makes impossible not only any modeling attempt but also the day-to-day work without a common way to name neurons. Therefore, machine learning techniques may help to get an accepted solution to this problem, which can be an important milestone for future research in neuroscience. Resumen Las técnicas de aprendizaje automático se usan para extraer información valiosa de datos. Hoy en día, la importancia de estas técnicas está siendo incluso mayor, debido a que la evolución en la adquisición y almacenamiento de datos está llevando a datos con diferentes características que deben ser explotadas. Por lo tanto, los avances en la recolección de datos deben ir ligados a avances en las técnicas de aprendizaje automático para resolver nuevos retos que pueden aparecer, tanto en aplicaciones académicas como reales. Existen varias técnicas de aprendizaje automático dependiendo de las características de los datos y del propósito. La clasificación no supervisada o clustering es una de las técnicas más conocidas cuando los datos carecen de supervisión (datos sin etiqueta), siendo el objetivo descubrir nuevos grupos (agrupaciones) dependiendo de la similitud de los datos. Por otra parte, la clasificación supervisada necesita datos con supervisión (datos etiquetados) y su objetivo es realizar predicciones sobre las etiquetas de nuevos datos. La presencia de las etiquetas es una característica muy importante que guía no solo el aprendizaje sino también otras tareas relacionadas como la validación. Cuando solo algunos de los datos disponibles están etiquetados, mientras que el resto permanece sin etiqueta (datos parcialmente etiquetados), ni el clustering ni la clasificación supervisada se pueden utilizar. Este escenario, que está llegando a ser común hoy en día debido a la ignorancia o el coste del proceso de etiquetado, es abordado utilizando técnicas de aprendizaje semi-supervisadas. Esta tesis trata la rama del aprendizaje semi-supervisado más cercana al clustering, es decir, descubrir agrupaciones utilizando las etiquetas disponibles como apoyo para guiar y mejorar el proceso de clustering. Otra característica importante de los datos, distinta de la presencia de etiquetas, es la relevancia o no de los atributos de los datos. Los datos se caracterizan por atributos, pero es posible que no todos ellos sean relevantes, o igualmente relevantes, para el proceso de aprendizaje. Una tendencia reciente en clustering, relacionada con la relevancia de los datos y llamada clustering en subespacios, afirma que agrupaciones diferentes pueden estar descritas por subconjuntos de atributos diferentes. Esto difiere de las soluciones tradicionales para el problema de la relevancia de los datos, en las que se busca un único subconjunto de atributos (normalmente el conjunto original de atributos) y se utiliza para realizar el proceso de clustering. La cercanía de este trabajo con el clustering lleva al primer objetivo de la tesis. Como se ha comentado previamente, la validación en clustering es una tarea difícil debido a la ausencia de etiquetas. Aunque existen muchos índices que pueden usarse para evaluar la calidad de las soluciones de clustering, estas validaciones dependen de los algoritmos de clustering utilizados y de las características de los datos. Por lo tanto, en el primer objetivo tres conocidos algoritmos se usan para agrupar datos con valores atípicos y ruido para estudiar de forma crítica cómo se comportan algunos de los índices de validación más conocidos. El objetivo principal de este trabajo sin embargo es combinar clustering semi-supervisado con clustering en subespacios para obtener soluciones de clustering que puedan ser validadas de forma correcta utilizando índices conocidos u opiniones expertas. Se proponen dos algoritmos desde dos puntos de vista diferentes para descubrir agrupaciones caracterizadas por diferentes subespacios. Para el primer algoritmo, las etiquetas disponibles se usan para bus¬car en primer lugar los subespacios antes de buscar las agrupaciones. Este algoritmo asigna cada instancia a un único cluster (hard clustering) y se basa en mapear las etiquetas cono-cidas a subespacios utilizando técnicas de clasificación supervisada. El segundo algoritmo utiliza las etiquetas disponibles para buscar de forma simultánea los subespacios y las agru¬paciones en un proceso iterativo. Este algoritmo asigna cada instancia a cada cluster con una probabilidad de pertenencia (soft clustering) y se basa en integrar las etiquetas conocidas y la búsqueda en subespacios dentro de clustering basado en modelos. Las propuestas son probadas utilizando diferentes bases de datos reales y sintéticas, incluyendo comparaciones con otros métodos cuando resulten apropiadas. Finalmente, a modo de ejemplo de una aplicación real y actual, se aplican diferentes técnicas de aprendizaje automático, incluyendo una de las propuestas de este trabajo (la más sofisticada) a una tarea de uno de los problemas biológicos más desafiantes hoy en día, el modelado del cerebro humano. Específicamente, expertos neurocientíficos no se ponen de acuerdo en una clasificación de neuronas para la corteza cerebral, lo que imposibilita no sólo cualquier intento de modelado sino también el trabajo del día a día al no tener una forma estándar de llamar a las neuronas. Por lo tanto, las técnicas de aprendizaje automático pueden ayudar a conseguir una solución aceptada para este problema, lo cual puede ser un importante hito para investigaciones futuras en neurociencia.

Conceptual design of the liquid metal laboratory of the TECHNOFUSION facility

The application of liquid metal technology in fusion devices requires R&D related to many phenomena: interaction between liquid metals and structural material as corrosion, erosion and passivation techniques; magneto-hydrodynamics; free surface fluid-dynamics and any other physical aspect that will be needed for their safe reliable operation. In particular, there is a significant shortage of experimental facilities dedicated to the development of the lithium technology. In the framework of the TECHNOFUSION project, an experimental laboratory devoted to the lithium technology development is proposed, in order to shed some light in the path to IFMIF and the design of chamber's first wall and divertors. The conceptual design foresee a development in two stages, the first one consisting on a material testing loop. The second stage proposes the construction of a mock-up of the IFMIF target that will allow to assess the behaviour of a free-surface lithium target under vacuum conditions. In this paper, such conceptual design is addressed.

Geochemical monitoring in the Hontomín (Burgos, Spain) injection site: preliminary results and perspectives

CO2 capture and storage (CCS) projects are presently developed to reduce the emission of anthropogenic CO2 into the atmosphere. CCS technologies are expected to account for the 20% of the CO2 reduction by 2050. One of the main concerns of CCS is whether CO2 may remain confined within the geological formation into which it is injected since post-injection CO2 migration in the time scale of years, decades and centuries is not well understood. Theoretically, CO2 can be retained at depth i) as a supercritical fluid (physical trapping), ii) as a fluid slowly migrating in an aquifer due to long flow path (hydrodynamic trapping), iii) dissolved into ground waters (solubility trapping) and iv) precipitated secondary carbonates. Carbon dioxide will be injected in the near future (2012) at Hontomín (Burgos, Spain) in the frame of the Compostilla EEPR project, led by the Fundación Ciudad de la Energía (CIUDEN). In order to detect leakage in the operational stage, a pre-injection geochemical baseline is presently being developed. In this work a geochemical monitoring design is presented to provide information about the feasibility of CO2 storage at depth.

Characterization of an 241AmBe neutron irradiation facility by different spectrometric techniques

An automated panoramic irradiator with a 3 Ci 241Am-Be neutron source is installed in a bunker-type large room at the Universidad Politécnica de Madrid (UPM). It was recently modified and a neutron spectrometry campaign was organized to characterize the neutron fields in different measurement points along the irradiation bench. Four research groups working with different Bonner Sphere Spectrometers (BSS) and using different spectral unfolding codes took part to this exercise. INFN-LNF used a BSS formed by 9 spheres plus bare detector, with cylindrical, almost point like, 6LiI(Eu) scintillator (4 mm x 4 mm, from Ludlum); UAZ-UPM employed a similar system but with only 6 spheres plus bare detector; UAB worked with a 3He filled proportional counter at 8kPa filling pressure, cylindrical 9 mm x 10 mm (05NH1 from Eurisys) with 11 spheres configuration; and CIEMAT used 12 spheres with an spherical 3He SP9 counter (Centronic Ltd., UK) with very high sensitivity due to the large diameter (3.2 cm) and the filling pressure of the order of 228 kPa. Each group applied a different spectral unfolding method: INFN and UAB worked with FRUIT ver. 3.0 with their own response matrixes; UAZ-UPM used the BUNKIUT unfolding code with the response matrix UTA4 and CIEMAT employed the GRAVEL-MAXED-IQU package with their own response matrix. The paper shows the main results obtained in terms of neutron spectra at fixed distances from the source as well as total neutron fluence rate and ambient dose equivalent rate H*(10) determined from the spectra. The latter are compared with the readings of a common active survey-meter (LB 6411). The small differences in the results of the various groups are discussed.

Hydrogeochemistry of surface and spring waters in the surroundings of the CO2 injection site at Hontomín–Huermeces (Burgos, Spain)

In this paper the very first geochemical and isotopic data related to surface and spring waters and dissolved gases in the area of Hontomín–Huermeces (Burgos, Spain) are presented and discussed. Hontomín–Huermeces has been selected as a pilot site for the injection of pure (>99%) CO2. Injection and monitoring wells are planned to be drilled close to 6 oil wells completed in the 1980s for which detailed stratigraphical logs are available, indicating the presence of a confined saline aquifer at the depth of about 1500 m into which less than 100,000 tons of iquid CO2 will be injected, possibly starting in 2013. The chemical and features of the spring waters suggest that they are related to a shallow hydrogeological system as the concentration of the Total Dissolved Solids approaches 800 mg/L with a Ca2+(Mg2+)-HCO3− composition, similar to that of the surface waters. This is also supported by the oxygen and hydrogen isotopic ratios that have values lying between those of the Global and the Mediterranean Meteoric Water Lines. Some spring waters close to the oil wells are haracterized by relatively high concentrations of NO3− (up to 123 mg/L), unequivocally suggesting an anthropogenic source that adds to the main water–rock interaction processes. The latter can be referred to Ca-Mg-carbonate and, at a minor extent, Al-silicate dissolution, being the outcropping sedimentary rocks characterized by Palaeozoic to Quaternary rocks. Anomalous concentrations of Cl−, SO42−, As, B and Ba were measured in two springs discharging a few hundred meters from the oil wells and in the Rio Ubierna. These contents are significantly higher than those of the whole set of the studied waters and are possibly indicative of mixing processes, although at very low extent, between deep and shallow aquifers. No evidence of deep-seated gases interacting with the Hontomín–Huermeces waters was recognized in the chemistry of the disolved gases. This is likely due to the fact that they are mainly characterized by an atmospheric source as highlighted by the high contents of N2, O2 and Ar and by N2/Ar ratios that approach that of ASW (Air Saturated Water) and possibly masking any contribution related to a deep source. Nevertheless, significant concentrations (up to 63% by vol.) of isotopically negative CO2 (<−17.7‰ V-PDB) were found in some water samples, likely related to a biogenic source. The geochemical and isotopic data of this work are of particular importance when a monitoring program will be established to verify whether CO2 leakages, induced by the injection of this greenhouse gas, may be affecting the quality of the waters in the shallow hydrological circuits at Hontomín–Huermeces. In this respect, carbonate chemistry, the isotopic carbon of dissolved CO2 and TDIC (Total Dissolved Inorganic Carbon) and selected trace elements can be considered as useful parameters to trace the migration of the injected CO2 into near-surface environments.

CO2 soil flux baseline at the technological development plant for CO2 injection at Hontomin (Burgos, Spain)

From the end of 2013 and during the following two years, 20 kt of CO2sc are planned to be injected in a saline reservoir (1500 m depth) at the Hontomín site (NE Spain). The target aquifers are Lower Jurassic limestone formations which are sealed by Lower Cretaceous clay units at the Hontomín site (NE Spain). The injection of CO2 is part of the activities committed in the Technology Development phase of the EC-funded OXYCFB300 project (European Energy Program for Recovery – EEPR, http://www.compostillaproject.eu), which include CO2 injection strategies, risk assessment, and testing and validating monitoring methodologies and techniques. Among the monitoring works, the project is intended to prove that present-day technology is able to monitor the evolution of injected CO2 in the reservoir and to detect potential leakage. One of the techniques is the measurement of CO2 flux at the soil–atmosphere interface, which includes campaigns before, during and after the injection operations. In this work soil CO2 flux measurements in the vicinity of oil borehole, drilled in the eighties and named H-1 to H-4, and injection and monitoring wells were performed using an accumulation chamber equipped with an IR sensor. Seven surveys were carried out from November 2009 to summer 2011. More than 4000 measurements were used to determine the baseline flux of CO2 and its seasonal variations. The measured values were low (from 5 to 13 g m−2 day−1) and few outliers were identified, mainly located close to the H-2 oil well. Nevertheless, these values cannot be associated to a deep source of CO2, being more likely related to biological processes, i.e. soil respiration. No anomalies were recognized close to the deep fault system (Ubierna Fault) detected by geophysical investigations. There, the CO2 flux is indeed as low as other measurement stations. CO2 fluxes appear to be controlled by the biological activity since the lowest values were recorded during autumn-winter seasons and they tend to increase in warm periods. Two reference CO2 flux values (UCL50 of 5 g m−2 d−1 for non-ploughed areas in autumn–winter seasons and 3.5 and 12 g m−2 d−1 for in ploughed and non-ploughed areas, respectively, in spring–summer time, and UCL99 of 26 g m−2 d−1 for autumn–winter in not-ploughed areas and 34 and 42 g m−2 d−1 for spring–summer in ploughed and not-ploughed areas, respectively) were calculated. Fluxes higher than these reference values could be indicative of possible leakage during the operational and post-closure stages of the storage project.

Adaptive neuro-fuzzy inference systems (ANFIS) for supervised classification of competence in MIS based on psychomotor skills

INTRODUCTION: Objective assessment of motor skills has become an important challenge in minimally invasive surgery (MIS) training.Currently, there is no gold standard defining and determining the residents' surgical competence.To aid in the decision process, we analyze the validity of a supervised classifier to determine the degree of MIS competence based on assessment of psychomotor skills METHODOLOGY: The ANFIS is trained to classify performance in a box trainer peg transfer task performed by two groups (expert/non expert). There were 42 participants included in the study: the non-expert group consisted of 16 medical students and 8 residents (< 10 MIS procedures performed), whereas the expert group consisted of 14 residents (> 10 MIS procedures performed) and 4 experienced surgeons. Instrument movements were captured by means of the Endoscopic Video Analysis (EVA) tracking system. Nine motion analysis parameters (MAPs) were analyzed, including time, path length, depth, average speed, average acceleration, economy of area, economy of volume, idle time and motion smoothness. Data reduction was performed by means of principal component analysis, and then used to train the ANFIS net. Performance was measured by leave one out cross validation. RESULTS: The ANFIS presented an accuracy of 80.95%, where 13 experts and 21 non-experts were correctly classified. Total root mean square error was 0.88, while the area under the classifiers' ROC curve (AUC) was measured at 0.81. DISCUSSION: We have shown the usefulness of ANFIS for classification of MIS competence in a simple box trainer exercise. The main advantage of using ANFIS resides in its continuous output, which allows fine discrimination of surgical competence. There are, however, challenges that must be taken into account when considering use of ANFIS (e.g. training time, architecture modeling). Despite this, we have shown discriminative power of ANFIS for a low-difficulty box trainer task, regardless of the individual significances between MAPs. Future studies are required to confirm the findings, inclusion of new tasks, conditions and sample population.