Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PhD : selection by topics

Mesoscopic scale simulation of the Barkhausen effect for the characterization of steels

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Simulation et Modélisation en Electro-magnétisme

01-09-2018

SL-DRT-18-0642

anastasios.skarlatos@cea.fr

Barkhausen noise is more and more used as a measure of the health state of magnetic materials. It is indeed strongly correlated to materials microstructure, stress level and chemical composition for instance. In spite of its great practical interest, this measure is often hard to interpret due to the large number of underlying physical phenomena. The development of efficient and accurate modelling tools is thus necessary to enhance the understanding of measurement and access to more quantitative estimations of characteristic quantities, such as the level of stress or a rate of chemical component. From the modelling point of view, the problem to solve is complex due to its multi-scale nature. Existing approaches can be divided in two families: those based on Monte Carlo methods to get a very fine description at the spin level, and those labelled as mesoscopic ones, aiming at solving a magnetostatic problem at the scale of the magnetic domains. In these latter approaches, Maxwell equations are solved considering a simplified configuration of domains in terms of geometry and displacements of domain walls. This PhD subject consists in implementing an optimized simulation tool for the characterization of steels, based on a mesoscopic approach. This tool will exploit empirical considerations on the distribution and dynamic behavior of domain walls in view of deriving macroscopic signals measured in practice and studying the statistics of characteristic parameters involved. Magnetostatic simulations will be carried out with a 3D numerical solver based on Finite Integration Technique (FIT) developed at CEA LIST. The representativeness of the unitary calculation will be the key to the validity of the statistical procedure leading to macroscopic signals. Theoretical results will be compared to experimental data obtained in laboratory controlled conditions by partners of laboratoire Roberval (Université de Technologie de Compiègne, UTC), involved in the PhD work.

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Thermal modelling of sugar alcohol cristallisation for energy storage systems based on phase change material

Département Thermique Biomasse et Hydrogène (LITEN)

Laboratoire Stockage Thermique

01-09-2018

SL-DRT-18-0650

fabrice.bentivoglio@cea.fr

Heat represents 50% of the finale energy consumption in the France energetic mix. It has been identified, in the framework of a general orientation law in 2015 (Loi TECV), as a major source of CO2 emission reduction in particular through the development of urban heating networks that allow massive integration of renewable energy such as biomass, solar or waste incineration. In order to adapt the fluctuant consumption of a urban heating network to renewable energy with low flexibility, next generation urban heating network will combined smart meters for precise diagnostic, smart management systems for a better decision process and a key technological component to shift production and match with consumption: heat storage. Phase Change Material (PCM) heat storage, that allows higher storage density (kWh.m3) than classical hot water tanks, has been identified as a promising concept in particular for heating network sub-station, located inside residential buildings then requiring low volume storage. Current studies on PCM storage for urban heating networks mainly consider paraffin, fatty acids and fatty alcohols as PCM. These PCM families can reach a storage density 50% higher than water. Using Sugar alcohol family (or polyols), a storage density 2 or 3 times higher than water can be reached. In addition sugar alcohol are cheap, safe (even edible) and non-corrosive. These ?ideal? PCM only show one major drawback: a very low nucleation rate and and a very low crystallisation speed that make them unusable in a storage system without adding specific system to help crystallisation. The Laboratoire de Stockage Thermique (LITEN ? CEA Grenoble) develops PCM storage systems for urban heating application, using sugar alcohol and based on tube and shell heat exchangers technology. The specific system used to force crystallisation is based on bubbling that generate mechanical shear stress. The principle has been successfully tested at laboratory scale (500g), around a single finned tube (1kg) and finally in a pre-industrial scale prototype (400kg). The results obtained are very promising. However, complex phenomenon have been brought out, such as a high crystallization delay or a strong coupling between thermal and statistical aspects. The objective of this PhD is, based on new experimental results that will be obtained in the facilities yet available in the laboratory, to propose a model of the heat released by a sugar alcohol in a tube and shell PCM heat storage using bubbling to activate the crystallisation. The model will be based on CFD and 2D models that have yet been developed in the laboratory by previous PhD student but for classical PCM (PCM with high crystallization rate and crystallisation speed such as paraffin). These models are based on enthalpy-porosity Voller formulation. The PhD work will starts with a bibliographic study on the crystallization of sugar alcohol and about the effect of bubbling on this crystallisation, in order to develop a model coupling thermal and kinetic aspect. Then the model will be implemented in a 2D or a CFD code and validated against experimental measurements, first at laboratory scale and then at the scale of a pre-industrial prototype.

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Understanding the aging phenomena of an innovative photovoltaic module for a specific application, and setting up

Département des Technologies Solaires (LITEN)

Laboratoire Modules Photovoltaïques Silicium

01-09-2018

SL-DRT-18-0652

benoit.braisaz@cea.fr

The main failures observed on photovoltaic modules (PV modules) are associated with the reliability and durability of both interconnect and encapsulation processes, and materials. The main failures are: delamination, yellowing, corrosion, cell cracking and breakage of interconnections. The improvement of PV module lifetime belongs to the development of the next PV module generation. The analysis of module failures requires the implementation of relevant accelerated aging tests intended to study the behavior over time, and advanced characterization Tools for physicochemical understanding of degradations. In the case of innovative modules that can be used as structural materials (eg solar road), the modules undergo combined climatic constraints: mechanical pressures, temperature variations, current variations, low temperature (<-5 ° C) and high (> 60 ° C), freezing, humidity, standing water (dew) and runoff (rain), illumination (solar and UV), salt (salting of roads), and intermittent shading. The understanding of the physicochemical mechanisms at the origin of the observed degradations will make it possible to identify the phenomena involved and on the other hand, to reproduce them during accelerated tests under controlled conditions in order to evaluate the most durable solutions. For this purpose the LEPMI and DURASOL material characterization platforms will be solicited.

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Recycling of polymer composites by means of a supercritical fluid

Département Thermique Biomasse et Hydrogène (LITEN)

Laboratoire de ThermoConversion de la Bioressource

01-10-2018

SL-DRT-18-0660

anne.roubaud@cea.fr

The subject of this PhD is to study and develop a supercritical fluid process for the recycling of carbon reinforced plastic composite, both resins and fibers. Use of composite materials is increasing in a wide range of applications : industrials, sporting, automotive, aeronautical, marine?but the lack of recycling for those non biodegradable materials is an environmental burden. Since 20 years several treatments have been developed for composites wastes, mechanical, thermal processes like pyrolysis and thermochemical like solvolysis. This process is able to breakdown the composite polymer matrix and hence allow the recovery of the fibers. Supercritical fluids are used due to their high diffusivity in porous materials combined to their chemical reactivity. The objective of this work is to define the necessary process conditions for deconstruction of composite resins of carbon fibers reinforced plastic composites. The aim is to recycle the fibers and also to allow a further chemical valorization of resins decomposition products. This work include a detailed analysis of the organic molecules produced and the development of a chemical mechanism for this depolymerisation. Mechanical properties of fibers after treatment will be determined to validate the recycling interest. This work will provide finally a first technico-economical evaluation of the proposed process.

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Optimal Battery Management Algorithms for Switched Cell Architecture Systems

Département Systèmes

Laboratoire Electronique Energie et Puissance

01-10-2018

SL-DRT-18-0663

vincent.heiries@cea.fr

Although having benefited from major advances in recent years, batteries still suffer from certain limitations, notably in terms of energy density, lifetime and sometimes safety. In this context, the patented switch-cell battery architecture proposed and developed in the L2EP laboratory represents a major innovation in this field and allows us to go beyond some of these limitations. Today, batteries are essentially composed of a series of cells through which the same current is flowing. These systems are thus limited by the weakest cell in series. One of the advantages of the switched cell architecture is that each cell can be exploited in a differentiated way and thus get the most out of each cell. A first objective of the thesis is precisely to propose an algorithm allowing to exploit at best the energy of all the cells of the battery in order to increase the autonomy of the system while maximizing its lifetime. A second objective of this thesis is the development of innovative estimation algorithms for SoX indicators (SoC: State of Charge; SoH: State of Health, SoE: State of Energy) of accumulators based on an optimal use of the new capabilities offered by the switched cell architecture. Indeed, this architecture brings new functionalities that open the door to the implementation of new algorithms within the Battery Management System. In particular, cell capacity estimation could be greatly improved by on-line correction of the estimator. This procedure can be made possible by a controlled load-discharge profile of individual cells.

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Accurate and robust estimation of the PEMFC ageing state Bayesian observers using a model-based approach

Département de l'Electricité et de l'Hydrogène pour les Transports (LITEN)

Laboratoire Electronique avancée, Energie et Puissance

01-10-2018

SL-DRT-18-0665

vincent.heiries@cea.fr

PHM (Prognostics and Health Management) represents a real opportunity to improve fuel cell performance and extend the life of fuel cells. This field of study has recently gained much interest. The main goal is to make optimum use of the data measured by all available sensors in order to evaluate the specific indicators of PEMFC ageing and possibly modify the operation of the fuel cell in order to optimize its lifetime. The proposed PhD is part of a model-based approach and will be based on the expertise in fuel cell modelling developed at the Modelling Laboratory. An on-line estimator of the ageing state of the fuel cell will be developed. The proposed observer presents the characteristic of combining a state model derived from the MEPHYSTO fuel cell model with the different data sensors available (voltage, current, pressure, temperature). The envisaged method makes it possible to jointly estimate the state variables, and in particular the ageing state, as well as to update the model parameters. Given the nature of the state variables to be estimated, we will move towards sophisticated observers adapted to non-linear and non-Gaussian problems in order to obtain a solution approaching the optimal Bayesian estimate.

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