Recent progress in research on tungsten materials for nuclear fusion applications in Europe

The current magnetic confinement nuclear fusion power reactor concepts going beyond ITER are based on assumptions about the availability of materials with extreme mechanical, heat, and neutron load capacity. In Europe, the development of such structural and armour materials together with the necessary production, machining, and fabrication technologies is pursued within the EFDA long-term fusion materials programme. This paper reviews the progress of work within the programme in the area of tungsten and tungsten alloys. Results, conclusions, and future projections are summarized for each of the programme´s main subtopics, which are: (1) fabrication, (2) structural W materials, (3) W armour materials, and (4) materials science and modelling. It gives a detailed overview of the latest results on materials research, fabrication processes, joining options, high heat flux testing, plasticity studies, modelling, and validation experiments.

Robust thermal face recognition using region classifiers

This paper presents a robust approach for recognition of thermal face images based on decision level fusion of 34 different region classifiers. The region classifiers concentrate on local variations. They use singular value decomposition (SVD) for feature extraction. Fusion of decisions of the region classifier is done by using majority voting technique. The algorithm is tolerant against false exclusion of thermal information produced by the presence of inconsistent distribution of temperature statistics which generally make the identification process difficult. The algorithm is extensively evaluated on UGC-JU thermal face database, and Terravic facial infrared database and the recognition performance are found to be 95.83% and 100%, respectively. A comparative study has also been made with the existing works in the literature.

Optimal laser intensity profiles for a uniform target illumination in direct-drive inertial confinement fusion

A numerical method providing the optimal laser intensity profiles for a direct-drive inertial confinement fusion scheme has been developed. The method provides an alternative approach to phase-space optimization studies, which can prove computationally expensive. The method applies to a generic irradiation configuration characterized by an arbitrary number NB of laser beams provided that they irradiate the whole target surface, and thus goes beyond previous analyses limited to symmetric configurations. The calculated laser intensity profiles optimize the illumination of a spherical target. This paper focuses on description of the method, which uses two steps: first, the target irradiation is calculated for initial trial laser intensities, and then in a second step the optimal laser intensities are obtained by correcting the trial intensities using the calculated illumination. A limited number of example applications to direct drive on the Laser MegaJoule (LMJ) are described.

Efficient Monte Carlo methods for multi-dimensional learning with classifier chains

Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance – at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest-performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for finding a good chain sequence and performing efficient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.

Simulating the remote handling of the blanket segments in DEMO fusion reactor with thermo-mechanical meshfree multibody dynamics

The present study shows a first approach to the simulation of the remote handling oper- ation which takes into account the thermal and flexible behavior of the blanket segments and its implications on the remote handling equipment, in order to validate and improve its design.

Multisensor data fusion for accurate modelling of mobile objects

In the last decade, multi-sensor data fusion has become a broadly demanded discipline to achieve advanced solutions that can be applied in many real world situations, either civil or military. In Defence,accurate detection of all target objects is fundamental to maintaining situational awareness, to locating threats in the battlefield and to identifying and protecting strategically own forces. Civil applications, such as traffic monitoring, have similar requirements in terms of object detection and reliable identification of incidents in order to ensure safety of road users. Thanks to the appropriate data fusion technique, we can give these systems the power to exploit automatically all relevant information from multiple sources to face for instance mission needs or assess daily supervision operations. This paper focuses on its application to active vehicle monitoring in a particular area of high density traffic, and how it is redirecting the research activities being carried out in the computer vision, signal processing and machine learning fields for improving the effectiveness of detection and tracking in ground surveillance scenarios in general. Specifically, our system proposes fusion of data at a feature level which is extracted from a video camera and a laser scanner. In addition, a stochastic-based tracking which introduces some particle filters into the model to deal with uncertainty due to occlusions and improve the previous detection output is presented in this paper. It has been shown that this computer vision tracker contributes to detect objects even under poor visual information. Finally, in the same way that humans are able to analyze both temporal and spatial relations among items in the scene to associate them a meaning, once the targets objects have been correctly detected and tracked, it is desired that machines can provide a trustworthy description of what is happening in the scene under surveillance. Accomplishing so ambitious task requires a machine learning-based hierarchic architecture able to extract and analyse behaviours at different abstraction levels. A real experimental testbed has been implemented for the evaluation of the proposed modular system. Such scenario is a closed circuit where real traffic situations can be simulated. First results have shown the strength of the proposed system.

Microstructural and mechanical characterization of tungsten based materials for fusion reactors

El wolframio (W) y sus aleaciones se consideran los mejores candidatos para la construcción del divertor en la nueva generación de reactores de fusión nuclear. Este componente va a recibir las cargas térmicas más elevadas durante el funcionamiento del reactor ya que estará en contacto directo con el plasma. En los últimos años, después de un profundo análisis y siguiendo una estrategia de reducción de costes, la Organización de ITER tomó la decisión de construir el divertor integramente de wolframio desde el principio. Por ello, el wolframio no sólo actuará como material en contacto con el plasma (PFM), sino que también tendría aplicaciones estructurales. El wolframio, debido a sus excelentes propiedades termo-físicas, cumple todos los requerimientos para ser utilizado como PFM, sin embargo, su inherente fragilidad pone en peligro su uso estructural. Por tanto, uno de los principales objetivos de esta tesis es encontrar una aleación de wolframio con menor fragilidad. Durante éste trabajo, se realizó la caracterización microstructural y mecánica de diferentes materiales basados en wolframio. Sin embargo, ésta tarea es un reto debido a la pequeña cantidad de material suministrado, su reducido tamaño de grano y fragilidad. Por ello, para una correcta medida de todas las propiedades físicas y mecánicas se utilizaron diversas técnicas experimentales. Algunas de ellas se emplean habitualmente como la nanoindentación o los ensayos de flexión en tres puntos (TPB). Sin embargo, otras fueron especificamente desarrolladas e implementadas durante el desarrollo de esta tesis como es el caso de la medida real de la tenacidad de fractura en los materiales masivos, o de las medidas in situ de la tenacidad de fractura en las láminas delgadas de wolframio. Diversas composiciones de aleaciones de wolframio masivas (W-1% Y2O3, W-2% V-0.5% Y2O3, W-4% V-0.5% Y2O3, W-2% Ti-1% La2O3 y W-4% Ti-1% La2O3) se han estudiado y comparado con un wolframio puro producido en las mismas condiciones. Estas aleaciones, producidas por ruta pulvimetalúrgica de aleado mecánico (MA) y compactación isostática en caliente (HIP), fueron microstructural y mecánicamente caracterizadas desde 77 hasta 1473 K en aire y en alto vacío. Entre otras propiedades físicas y mecánicas se midieron la dureza, el módulo elástico, la resistencia a flexión y la tenacidad de fractura para todas las aleaciones. Finalmente se analizaron las superficies de fractura después de los ensayos de TPB para relacionar los micromecanismos de fallo con el comportamiento macroscópico a rotura. Los resultados obtenidos mostraron un comportamiento mecánico frágil en casi todo el intervalo de temperaturas y para casi todas las aleaciones sin mejoría de la temperatura de transición dúctil-frágil (DBTT). Con el fin de encontrar un material base wolframio con una DBTT más baja se realizó también un estudio, aún preliminar, de láminas delgadas de wolframio puro y wolframio dopado con 0.005wt.% potasio (K). Éstas láminas fueron fabricadas industrialmente mediante sinterizado y laminación en caliente y en frío y se sometieron posteriormente a un tratamiento térmico de recocido desde 1073 hasta 2673 K. Se ha analizado la evolución de su microestructura y las propiedades mecánicas al aumentar la temperatura de recocido. Los resultados mostraron la estabilización de los granos de wolframio con el incremento de la temperatura de recocido en las láminas delgadas de wolframio dopado con potasio. Sin embargo, es necesario realizar estudios adicionales para entender mejor la microstructura y algunas propiedades mecánicas de estos materiales, como la tenacidad de fractura. Tungsten (W) and tungsten-based alloys are considered to be the best candidate materials for fabricating the divertor in the next-generation nuclear fusion reactors. This component will experience the highest thermal loads during the operation of a reactor since it directly faces the plasma. In recent years, after thorough analysis that followed a strategy of cost reduction, the ITER Organization decided to built a full-tunsgten divertor before the first nuclear campaigns. Therefore, tungsten will be used not only as a plasma-facing material (PFM) but also in structural applications. Tungsten, due to its the excellent thermo-physical properties fulfils the requirements of a PFM, however, its use in structural applications is compromised due to its inherent brittleness. One of the objectives of this phD thesis is therefore, to find a material with improved brittleness behaviour. The microstructural and mechanical characterisation of different tunsgten-based materials was performed. However, this is a challenging task because of the reduced laboratory-scale size of the specimens provided, their _ne microstructure and their brittleness. Consequently, many techniques are required to ensure an accurate measurement of all the mechanical and physical properties. Some of the applied methods have been widely used such as nanoindentation or three-point bending (TPB) tests. However, other methods were specifically developed and implemented during this work such as the measurement of the real fracture toughness of bulk-tunsgten alloys or the in situ fracture toughness measurements of very thin tungsten foils. Bulk-tunsgten materials with different compositions (W-1% Y2O3, W-2% V- 0.5% Y2O3, W-4% V-0.5% Y2O3, W-2% Ti-1% La2O3 and W-4% Ti-1% La2O3) were studied and compared with pure tungsten processed under the same conditions. These alloys, produced by a powder metallurgical route of mechanical alloying (MA) and hot isostatic pressing (HIP), were microstructural and mechanically characterised from 77 to 1473 K in air and under high vacuum conditions. Hardness, elastic modulus, flexural strength and fracture toughness for all of the alloys were measured in addition to other physical and mechanical properties. Finally, the fracture surfaces after the TPB tests were analysed to correlate the micromechanisms of failure with the macroscopic behaviour. The results reveal brittle mechanical behaviour in almost the entire temperature range for the alloys and micromechanisms of failure with no improvement in the ductile-brittle transition temperature (DBTT). To continue the search of a tungsten material with lowered DBTT, a preliminary study of pure tunsgten and 0.005 wt.% potassium (K)-doped tungsten foils was also performed. These foils were industrially produced by sintering and hot and cold rolling. After that, they were annealed from 1073 to 2673 K to analyse the evolution of the microstructural and mechanical properties with increasing annealing temperature. The results revealed the stabilisation of the tungsten grains with increasing annealing temperature in the potassium-doped tungsten foil. However, additional studies need to be performed to gain a better understanding of the microstructure and mechanical properties of these materials such as fracture toughness.

Real-Time Adaptive Multi-Classifier Multi-Resolution Visual Tracking Framework for Unmanned Aerial Vehicles

This paper presents a novel robust visual tracking framework, based on discriminative method, for Unmanned Aerial Vehicles (UAVs) to track an arbitrary 2D/3D target at real-time frame rates, that is called the Adaptive Multi-Classifier Multi-Resolution (AMCMR) framework. In this framework, adaptive Multiple Classifiers (MC) are updated in the (k-1)th frame-based Multiple Resolutions (MR) structure with compressed positive and negative samples, and then applied them in the kth frame-based Multiple Resolutions (MR) structure to detect the current target. The sample importance has been integrated into this framework to improve the tracking stability and accuracy. The performance of this framework was evaluated with the Ground Truth (GT) in different types of public image databases and real flight-based aerial image datasets firstly, then the framework has been applied in the UAV to inspect the Offshore Floating Platform (OFP). The evaluation and application results show that this framework is more robust, efficient and accurate against the existing state-of-art trackers, overcoming the problems generated by the challenging situations such as obvious appearance change, variant illumination, partial/full target occlusion, blur motion, rapid pose variation and onboard mechanical vibration, among others. To our best knowledge, this is the first work to present this framework for solving the online learning and tracking freewill 2D/3D target problems, and applied it in the UAVs.