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This Master’s Thesis aims to produce a tubular permanent magnet actuator (TPMA) design capable of replacing the hydraulic dampers present in active train suspensions. This design needs to be optimised to provide the best possible performance.
In the first part of this thesis, a state of the art of the available TPMA topologies is presented as
well as their operating principle. 
In the second part the analytical model of each topology is developed in order to obtain the behaviour of the magnetic flux within each actuator. This model contains the solution of the Laplace and Poisson equations from Maxwell’s equations. The specific boundary conditions for each of the
topologies are exposed in order to obtain the specific solutions. Then the results are compared in order to keep the best topology for the rest of the thesis.
The third part is devoted to the modelling of the thrust produced by the actuator. The thrust
produced by the actuator is dependent on the type of current injected. Thus three types of current are compared: single-phase, two-phase and three-phase.
The fourth part concerns the optimisation of the actuator design. In a first step, the optimisation is
done by the particle swarm optimisation (PSO) method. This first optimisation has only one objective, to maximise the thrust produced by the actuator. The dimensions to be optimised are the radii of the actuator and the pole pitch of the magnets. In a second step, a new objective, that of minimising the force variation, is added to the optimisation. This multi-objective optimisation problem is solved using a method derived from PSO: vector evaluated particle swarm optimisation. Finally, the different designs obtained by the two optimisations are compared.
The last part is devoted to the presentation of some improvements that could be made to the analytical model of the actuator to make it even more accurate.

Tubular permanent magnet actuator --- Linear machine --- Particle swarm optimisation --- Dual Halbach array --- Analytical model --- Ingénierie, informatique & technologie > Ingénierie mécanique

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This master thesis focuses on thin film solar cells based on Transparent Conductor Oxide (TCO) that are able to generate an electric current while maintaining their transparency to visible light for photovoltaic and UV detectors applications. The studied heterostructure is based on Nickel Oxide (NiO), which is a new interesting material that exhibit an p-type behaviour, and Zinc Oxide (ZnO), a well-known n-type material subjected to many scientific researches in many fields. Moreover, dip-coating deposition technique is used in this master thesis to synthesised the heterojunction. This fabrication process is a soft chemical deposition technique where sol-gel solutions are used as precursors for the deposition. All the depositions are performed on a transparent glass substrate coated by a Fluorine Tin Oxide (FTO) thin film that plays the role of an ohmic back contact. Gold pads are deposited by sputtering on the top of the NiO (p-type side) to form the top contacts. The resulting heterostructure is characterised by different methods and the obtained I-V curves are compared to a simple analytical model.

Photoelectrical --- Photovoltaic --- Solar cell --- TCO --- Sputtering --- PN junction --- Semiconductor --- Transparent oxide --- NiO --- ZnO --- Dip-coating --- Heterojunction --- Analytical model --- Experimental --- FTO --- gas sensor --- Rectifying --- Diode --- J-V curves --- I-V curves --- Ingénierie, informatique & technologie > Multidisciplinaire, généralités & autres --- Physique, chimie, mathématiques & sciences de la terre > Physique

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This Special Issue book contains selected papers from works presented at the 9th EASN (European Aeronautics Science Network) International Conference on Innovation in Aviation & Space, which was held in Athens, Greece from the 3rd until the 6th of September, 2019. About 450 participants contributed to a high-level scientific gathering, providing some of the latest research results on the topic, as well as some of the latest relevant technological advancements. Eight interesting articles, which cover a wide range of topics including characterization, analysis and design, as well as numerical simulation, are contained in this Special Issue.

Technology: general issues --- History of engineering & technology --- electric propulsion --- aircraft --- CENTRELINE --- DC link voltage level --- analytical model --- design space exploration --- gas-turbine performance --- turboshaft --- axial compressor --- blade --- FEM --- CFD --- erosion --- wear --- stall margin --- compressor surge --- brownout --- aircraft thermal management --- hybrid electric propulsion --- surface heat exchanger --- superhydrophobic --- coating --- anti-icing --- spray-coat --- aeronautical --- gas turbine engine --- performance model --- gas path analysis --- robust estimation --- identification --- regularization --- fuzzy set --- membership function --- CubeSat --- CFRP --- structural integration --- functional integration --- structural battery --- embedded battery --- carbon fibre thermoplastic composite --- PEEK matrix --- woven --- aging --- mechanical testing --- static and fatigue --- STOL aircraft --- propeller --- Pareto sets --- propeller optimization --- n/a

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The Journal of Manufacturing and Materials Processing (JMMP) aims to provide an international forum for the documentation and dissemination of recent, original, and significant research studies in the analysis of processes, equipment, systems, and materials related to material heat treatment, solidification, deformation, addition, removal, welding, and accretion for the industrial fabrication and production of parts, components, and products. The JMMP was established in 2017 and has published more than 300 contributions. It has been listed in the ESCI, Inspec (IET), and Scopus (Elsevier). In celebration of the anniversary of the JMMP, the Editorial Office has put together this Special Issue, which includes several representative papers that reflect the vibrant growth and dynamic trend of research in this field.

History of engineering & technology --- metal additive manufacturing --- analytical model --- temperature prediction --- FEA --- melt pool geometry --- sustainability --- bimetallic object --- cutting force --- uncertainty --- machining power --- precision injection molding --- quality control --- process monitoring --- process fingerprint --- product fingerprint --- flexible abrasive tools --- finishing --- rounding edge --- superalloys --- coordinate metrology --- on-machine measurement --- ball dome artefact --- calibration --- machine tool --- additive manufacturing --- laser powder bed fusion --- process optimization --- orthogonal cutting --- brittle materials --- cohesive elements --- nickel-based superalloys --- high temperature mechanical properties --- creep resistance --- fatigue --- SLM --- AlSi10Mg --- post treatment --- residual stress --- surface roughness --- discrete element method --- seed cracks --- meso-micro machining --- micro abrasive-waterjet technology --- stacking cutting --- micro milling --- taper compensation --- flexure --- subtractive machining --- additive machining --- micrograph

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Third millennium engineering address new challenges in materials sciences and engineering. In particular, the advances in materials engineering combined with the advances in data acquisition, processing and mining as well as artificial intelligence allow for new ways of thinking in designing new materials and products. Additionally, this gives rise to new paradigms in bridging raw material data and processing to the induced properties and performance. This present topical issue is a compilation of contributions on novel ideas and concepts, addressing several key challenges using data and artificial intelligence, such as:- proposing new techniques for data generation and data mining;- proposing new techniques for visualizing, classifying, modeling, extracting knowledge, explaining and certifying data and data-driven models;- processing data to create data-driven models from scratch when other models are absent, too complex or too poor for making valuable predictions;- processing data to enhance existing physic-based models to improve the quality of the prediction capabilities and, at the same time, to enable data to be smarter; and- processing data to create data-driven enrichment of existing models when physics-based models exhibit limits within a hybrid paradigm.

plasticity --- machine learning --- constitutive modeling --- manifold learning --- topological data analysis --- GENERIC --- soft living tissues --- hyperelasticity --- computational modeling --- data-driven mechanics --- TDA --- Code2Vect --- nonlinear regression --- effective properties --- microstructures --- model calibration --- sensitivity analysis --- elasto-visco-plasticity --- Gaussian process --- high-throughput experimentation --- additive manufacturing --- Ti–Mn alloys --- spherical indentation --- statistical analysis --- Gaussian process regression --- nanoporous metals --- open-pore foams --- FE-beam model --- data mining --- mechanical properties --- hardness --- principal component analysis --- structure–property relationship --- microcompression --- nanoindentation --- analytical model --- finite element model --- artificial neural networks --- model correction --- feature engineering --- physics based --- data driven --- laser shock peening --- residual stresses --- data-driven --- multiscale --- nonlinear --- stochastics --- neural networks --- n/a --- Ti-Mn alloys --- structure-property relationship