The development of mobile manipulators capable of adapting to new conditions is a major step forward in the development of new means of production, whether for industrial or agricultural applications. Such technologies enable repetitive tasks to be carried out with precision and without the constraints of limited workspace. Nevertheless, the efficiency of such robots depends on their adaptation to the variability of the evolutionary context and the task to be performed. This thesis therefore proposes to design mechanisms for adapting the sensory-motor behaviors of this type of robot, in order to ensure that their actions are appropriate to the situation. It envisages extending the reconfiguration capabilities of perception and control approaches through the contribution of Artificial Intelligence, here understood in the sense of deep learning. The aim is to develop new decision-making architectures capable of optimizing robotic behaviors for mobile handling in changing contexts (notably indoor-outdoor), and for carrying out a range of precision tasks.
The durability of materials used in many areas of energy production is limited by their degradation in the operating environment, which is often oxidising and at high temperature. This is particularly true of High Temperature Electrolysers (HTE) for the production of ‘green’ hydrogen, or the fuel cladding used in nuclear reactors to produce electricity. Anti-corrosion coatings can/should be applied to improve the lifespan of these installations, thereby conserving resources. A process for synthesising coatings using a reactive vapour route with liquid organometallic precursors (DLI - MOCVD) appears to be a very promising process.
The aim of this thesis is to model and simulate the DLI-MOCVD coating synthesis process for the two applications proposed above. Simulation results (deposition rate, deposit composition, spatial homogeneity) will be compared with experimental results from large-scale ‘pilot’ reactors at the CEA in order to optimise the model's input parameters. On the basis of this CFD simulation/experiments dialogue, the optimum conditions for deposition on a scale 1 component will be proposed. A coupling between CFD simulations and Machine Learning will be developed to accelerate the change of scale and the optimisation of scale 1 deposits.
In many industrial sectors, rapid transient phenomena are involved in accident scenarios. An example in the nuclear industry is the Loss of Primary Coolant Accident, in which an expansion wave propagates through the primary circuit of a Pressurized Water Reactor, potentially vaporizing the primary fluid and causing structural damage. Nowadays, the simulation of these fast transient phenomena relies mainly on "explicit" time integration algorithms, as they enable robust and efficient treatment of these problems, which are generally highly non-linear. Unfortunately, because of the stability constraints imposed on time steps, these approaches struggle to calculate steady-state regimes. Faced with this difficulty, in many cases, the kinematic quantities and internal stresses of the steady state of the system under consideration at the time of occurrence of the simulated transient phenomenon are neglected.
Furthermore, the applications in question involve solid structures interacting with the fluid, undergoing large-scale deformation and possibly fragmenting. A immersed boundary technique known as MBM (Mediating Body Method [1]) recently developed at the CEA enables structures with complex geometries and/or undergoing large deformations to be processed efficiently and robustly. However, this coupling between fluid and solid structure has only been considered in the context of "fast" transient phenomena treated by "explicit" time integrators.
The final objective of the proposed thesis is to carry out a nominal regime calculation followed by a transient calculation in a context of fluid/immersed-structure interaction. The transient phase of the calculation is necessarily based on "explicit" time integration and involves the MBM fluid/structure interaction technique. In order to minimize numerical disturbances during the transition between nominal and transient regimes, the calculation of the nominal regime should be based on the same numerical model as the transient calculation, and therefore also rely on an adaptation of the MBM method.
Recent work defined an efficient and robust strategy for calculating steady states for compressible flows, based on "implicit" time integration. However, although generic, this approach has so far only been tested in the case of perfect gases, and in the absence of viscosity.
On the basis of this initial work, the main technical challenges of this thesis are 1) to validate and possibly adapt the methodology for more complex fluids (in particular water), 2) to introduce and adapt the MBM method for fluid-structure interaction in this steady-state calculation strategy, 3) to introduce fluid viscosity, in particular within the framework of the MBM method initially developed for non-viscous fluids. At the end of this work, implicit/explicit transition demonstration calculations with fluid-structure interaction will be implemented and analyzed.
An internship can be arranged in preparation for thesis work, depending on the candidate's wishes.
[1] Jamond, O., & Beccantini, A. (2019). An embedded boundary method for an inviscid compressible flow coupled to deformable thin structures: The mediating body method. International Journal for Numerical Methods in Engineering, 119(5), 305-333.
The vitrification of nuclear waste is a solution currently adopted for the storage of nuclear waste. The vitroceramic materials considered for this application consist of a glass matrix and inclusions of crystalline phases. Rich in radioactive elements, these inclusions undergo self-irradiation resulting in their swelling, which may cause cracking of the glass matrix. It is necessary to know the maximum amount of inclusions below which the material does not crack. An experimental study on radioactive materials, produced and monitored over time, is excessively expensive and the development of a numerical approach could make it possible to better target the materials to be studied.
Following Gérald Feugueur's thesis work on the subject, which highlighted the difficulty of current models in dissociating crack initiation and propagation, the main goal is to develop and test an improved phase field model incorporating an elasticity-independent crack nucleation criterion, based on regularized models of softening plasticity. The model will be implemented using the finite element method (FEniCS code) and an alternative method using Fourier transforms (AMITEX code). Following cross-validation, the most efficient implementation will be selected for application to large-scale 3D microstructures. Close exchanges with CEA Marcoule will enable us to characterize the microstructure of the materials, and an experiment currently underway should enable us to analyze the potential cracking of these materials under self-irradiation.
Inertial Fusion on the Laser Megajoule facilty requires to form a spherical DT layer at cryogenic temperature. A major topic of interest for fusion experiments is the characterization of the layer quality and thickness. The characterization will be done using two technics : optical shadowgraphy and X-ray phase contrast analysis. A cryostat developed by CEA is already available to work on future target designs and layer characterization.
The objectives of the PhD are to understand and model (theorically and numerically) the physics of the layer observation and to develop the characterization test bench in the cryostat’s environment and the image analysis for the 3D description of the layer.
The student will have to learn to use the cryostat, its command system and its simple actual characterization system. After a bibliography research, he will have to study the physics governing the characterization (multiple reflections, refractions, phase contrast, …) and develop the acquisition and image analysis system allowing the 3D description of the layer using images obtained during experiments with the cryostat. Lastly, the coupling between the command of the cryostat and the characterization will be developed. For all these developments, the student will have access to extensive bibliographical data and the expertise of the host team
Sans objet (candidats français uniquement pour cette thèse)
In the event of a large-scale radiological emergency involving sources of external irradiation, methods are needed to identify which members of the population have been exposed and require priority care. To date, there are no operational methods for such sorting. Smartphone touch screen lenses retain traces of ionizing radiation through the formation of so-called “radiation-induced” defects.Measuring and quantifying these punctual defects, in particular by electron paramagnetic resonance (EPR) spectroscopy, makes itpossible to estimate the dose deposited in the glass, and thus the exposure associated with irradiation. The thesis work proposed herefocuses in particular on the alkali-aluminosilicate glasses used in cell phone touch screens, which are currently the best candidates fordeveloping new measurement capabilities in the context of accidents involving large numbers of victims.
We will focus in particular on identifying point defects as a function of the glass model used in smartphones by simulating EPR spectra in order to optimize the proposed dosimetry method.