Role of surface properties of UO2 powder particles on their agglomeration suitability and rheological behaviour
This study aims to predict the powder flow behavior in the context of nuclear fuel fabrication. This issue is common to many industrial fields because poor powder flow can lead to process problems such as pipe clogging, reduced rates, or the presence of heterogeneities in the final product. The first objective of this PhD thesis is, on the one hand, to provide a more accurate description of the powder agglomerates and, on the other hand, to characterize their surface. Based on these surface and structural data of UO2 powder particles, the second objective of this work is to achieve a better understanding of the agglomeration/desagglomeration properties in order to correlate them with the flow properties.
The future PhD student will need to use and develop experimental methods (particle characterization tools, surface characterization analyzers, phenomenological modeling) at the IRESNE institute (CEA-Cadarache) in the fuel study department (DEC), specifically within a team dedicated to experiments on nuclear fuel.
This study, applied to UO2 powders, has a generic nature because it is suitable for the study of all granular media. At the end of the PhD, the doctoral candidate will communicate the results through publications and conference presentations. An expertise in granular media will be acquired, which is an attractive and valuable skill in many industrial fields such as agri-food, pharmaceutical industries, metallurgy, or building materials.
Simulation and measurement of thermomechanical behaviour of granular bed into a fluid media
For several applications, granular media deals with complex physical phenomena. The multi-scale aspect of their microstructure they contains makes the prediction and modeling of heat transfer and its properties non-trivial to compute. In the case of ceramic powders, highly polydisperse powders with grain size extending over several orders of magnitude, are commonly employed. The different pore sizes and the large amount of heat transfer surfaces make the evaluation and simulation of the thermal properties of powders complex to compute.
Homogenized empirical laws are commonly used for this purpose. They allow fast and cheap computation of equivalent properties but rely on a number of empirical parameters which limit their application domain. Explicit simulation tools like DEM/FFT method [1], provide a more detailed description of the microstructure of the granular shape and packing, with higher computational costs. We use this method to tackle the models and better understand the competition between the different modes of heat transfer in the packed beds (heat conduction in gas, heat conduction in grains, conductance at contact between grains, radiation, etc.).
In order to test and improve the simulations and the models, a previous phD thesis was carried out on the effect of grain size and atmosphere on the equivalent thermal conductivity [2]. It gave a better understanding of the competition between heat transfers of different porosity scales and the effect of gas penetration in the microstructure, and to the proposal of new equivalent thermal conductivity models.
The aim of this thesis consists in pursuing this work to study the influence of grain size distribution on the conductivity of the packed bed. It will involve experimental measurements to acquire reliable and controlled data, and a simulation/modeling work to better understand and model the thermal properties of these media. The thesis will involve a collaboration between the Departement of Nuclear Fuels (IRESNE institute, CEA Cadarache) and IUSTI institute in Marseille. The experimental part will take place at IUSTI in Marseille, and the simulations in CEA Cadarache. Specific attention will concern the analysis of measurement and simulation uncertainties.
This subject has applications to many industrial fields such as energy production and transformation, heat exchangers and process engineering. It will gives to the candidate skills at the end of the thesis which might be of a great interest whether in industry or in academic research.
[1] Calvet, T., Vanson, J. M., & Masson, R. (2022). A DEM/FFT approach to simulate the effective thermal conductivity of granular media. International Journal of Thermal Sciences, 172, 107339.
[2] Letessier, J., Gheribi, A. E., Vanson, J. M., Duguay, C., Rigollet, F., Ehret, N., ... & Gardarein, J. L. (2023). Thermal transport-porosity-microstructural characteristics: unpicking the relationship in ultra-porous a-Al2O3 powder. International Journal of Heat and Mass Transfer, 205, 123898.
Fragmentation metamodel for the simulation of the powder grinding process
The grinding process, used since antiquity to crush seeds and nuts, is vital across various industries, such as mining, civil engineering, and pharmacy. Current research aims to optimize this procedure by enhancing the properties of powders while reducing energy costs. Experimental methods for studying grinding face complexities due to dynamic forces and the continual changes in materials. Fortunately, recent advancements in simulation, using the Discrete Element Method (DEM), offer a perspective to investigate these mechanisms, especially in the context of co-grinding for nuclear fuel manufacturing.
This thesis topic specifically aims to accelerate the simulation of these mechanisms for industrial use. The objective is to develop a fragmentation metamodel based on artificial intelligence. To achieve this, it will be necessary to create a database simulating particle fragmentation and to define the essential features of the process. The approach will encompass several phases, including predicting a particle's fragmentation and learning the fragmentation mode using advanced techniques, such as neural networks.
The research will build upon previous works, notably those of D.-C. Vu (CEA thesis 2020-2023), and will be validated using experimental data associated with other academic endeavors. The doctoral candidate will have access to significant simulation resources, with access to the computing resources of the IRESNE Institute (CEA-Cadarache) and other platforms. In essence, this thesis project aims to merge expertise in grinding with artificial intelligence techniques to innovate in the field of particle fragmentation.
Manufacturing of a UO2 fuel from granules: study of the influence of granules characteristics on microstructure and properties of the fuel
In UO2 fuel manufacturing, granules may be used as raw materials. This study aims at determining the influence of granules characteristics on final microstructure of sintered pellets. This study is mainly based on experiments in a CEA laboratory (CEA Cadarache).
Calculation of the thermal conductivity of UO2 nuclear fuels and the influence of irradiation defects
Atomistic simulations of the behaviour of nuclear fuel under irradiation can give access to its thermal properties and their evolution with temperature and irradiation. Knowledge of the thermal conductivity of 100% dense oxide can now be obtained by molecular dynamics and the interatomic force constants[1] at the single crystal scale, but the effect of defects induced by irradiation (irradiation loop, cluster of gaps) or even grain boundaries (ceramic before irradiation) remain difficult to evaluate in a coupled way.
The ambition is now to include defects in the supercells and to calculate their effect on the force constants. Depending on the size of the defects considered, we will use either the DFT (Density Functional Theory) or an empirical or numerical potential to perform the molecular dynamics. AlmaBTE allows the calculation of phonon scattering by point defects and the calculation of phonon scattering by dislocations and their transmission at an interface have also recently been implemented. Thus, the chaining atomistic calculations/AlmaBTE will make it possible to determine the effect of the polycrystalline microstructure and irradiation defects on the thermal conductivity. At the end of this thesis, the properties obtained will be used in the existing simulation tools in order to estimate the conductivity of a volume element (additional effect of the microstructure, in particular of the porous network, Fast Fourier Transform method), data which will finally be integrated into the simulation of the behavior of the fuel element under irradiation.
The work will be carried out at the Nuclear Fuel Department of the CEA, in a scientific environment characterised by a high level of expertise in materials modelling, in close collaboration with other CEA teams in Grenoble and in the Paris region who are experts in atomistic calculations. The results will be promoted through scientific publications and participation in international congresses.
Study of restructuring phenomena in UO2 fuel using High Resolution Electron Back Scatter Diffraction
Electron BackScatter Diffraction (EBSD) has helped to highlight microstructural changes in UO2 fuel pellets, such as the subdivision of grains into weakly disoriented sub-grains, either after compression tests at high temperature or after irradiation. However, the analyses carried out to date by this method present several limitations.
The main objective of this thesis is to improve their angular and spatial resolution. To this end, high-resolution EBSD (HR-EBSD) and Transmission Kikuchi Diffraction (HR-TKD) will be developed and applied to different restructured fuels. The new data acquired by these methods will improve our knowledge of the defect population (dislocations) and the local mechanical state of the fuel. They will be used in crystalline plasticity models.
This subject will enable the PhD student to acquire skills in advanced microstructural characterisation of materials, exploitation of experimental data and micromechanical modelling, which will be applicable to a wide range of materials.
The thesis work will mainly take place at the Fuel Research Department (IRESNE INstitute, CEA Cadarache) in a laboratory for the characterisation and study of the properties of fresh and irradiated fuels. It will also take place over a shorter period of time at the LEM3 laboratory of the University of Loraine based in Metz, which supervises this thesis in collaboration with the CEA and which has internationally recognized experience in the characterization of materials using the considered techniques in this study.
which thermomechanical coupling is required to describe nuclear fuel fragmentation ?
When subjected to a temperature ramp, irradiated nuclear fuel undergoes fragmentation, which increases with increasing temperature ramp. This intuitive result cannot, however, be described by the fuel behavior codes existing at the CEA, because the latter calculate the stress state of the fuel pellet statically, that is to say with equations, which do not depend on the time. The aim of the thesis is to go beyond this limitation by proposing a modeling, in elastic mechanics, which can account for the effect of the speed of temperature rise. To do this, we will adopt a thermodynamic approach, using the Onsager formalism. The thesis work will consist of writing a theoretical model of mechanical thermal coupling based on time-dependent equations, then applying it to a simplified simulation of the fuel pellet. The candidate must have knowledge of mechanics and thermodynamics, ideally in the thermodynamics of irreversible processes. He will benefit from a high-level scientific environment with skills on thermomechanical codes and the behavior of the fuel pellet during a thermal transient at the Fuel Research Department (IRESNE Institute, CEA Cadarache) and skills on the Onsager formalism with his thesis director.
Mutiscale modelling of the impact of the dislocation climbing on the mechanical beahviour of UO2 at high temperature
Reducing greenhouse gas emissions requires the development of low-carbon energy production systems, including nuclear power. The acceptability of nuclear power requires a high level of safety, and therefore in-depth knowledge of fuel behavior under irradiation to support the development of Scientific Computing Tools (SCTs). A key challenge for these SCTs is to enhance fuel performance, particularly in terms of flexibility with regard to the energy mix and behavior in design basis accidents.
Uranium dioxide (UO2), with its polycrystalline microstructure, is used as the constituent material of fuel pellets in nuclear power reactors. The mechanical behavior of UO2, coupled with irradiation effects, plays an important role in assessing the integrity of the fuel's first containment barrier. One of the challenges of understanding the mechanical behavior of irradiated fuel is to be able to compute the stresses and strains in the grains and at their interfaces with a physically based modelling at the scale of polycrystalline microstructural heterogeneities.
The main objective of the thesis will be to provide reference simulations in support of multi-scale modeling of the dislocation climbing mechanism, a major phenomenon underlying the mechanical behavior of fuel at high temperatures. The development of a coupling between a dislocation dynamics (DD) code and a finite element (FE) code will be carried out in order to best describe the diffusion and dislocation climbing mechanisms. Calculations based on this coupling will then be used to quantify the impact of dislocation climbing on the microstructure and viscoplastic behavior of UO2 fuel. Ultimately, this work will improve the micromechanical modeling using the finite element method implemented in SCTs of the PLEIADES simulation platform developed in the partnership between CEA, EDF and FRAMATOME.
This thesis will be carried out as part of a collaboration between CEA/IRESNE's DEC and Aix Marseille University's IM2NP. The DRMP at CEA/ISAS and the UMET at the University of Lille will also be involved in this collaboration. The thesis work will be carried out at IRESNE in Cadarache, within the Laboratoire de Modélisation du Comportement des Combustibles, in an environment providing access to a high expertise in multi-scale materials modeling. The research work will be promoted through publications and participation in international conferences in the materials field.
Application of generative artificial intelligence to the atomic-scale modeling of nuclear materials
Artificial Intelligence (AI) plays nowadays a key role in the design of innovative materials for the transition to low-carbon electricity production. In particular, AI generative methods that have led to well-known text/image generation tools can be used as well in the modeling of nuclear materials, to enhance reactor efficiency and safety. Over recent years, our laboratory has been actively working on such methods to accelerate the calculation of atomic-scale properties – a fundamental step in advancing our understanding of the physical phenomena resulting from the irradiation of these materials. Some of them are chemically disordered, which entails a random distribution of the chemical elements on the crystal lattice and inherent challenges in dealing with the astronomical number of resulting atomic configurations. The goal of the generative methods currently under investigation is to generate a set of representative configurations, for a rapid and accurate estimation of the desired property.
The objective of this thesis is to continue the development of these methods and apply them to determine the properties of crystal defects and fission gases that underlie the irradiation-induced microstructure evolution. The work will focus on actinide mixed oxides and high-entropy multicomponent alloys. The former are used to optimize the consumption of fissile material and move towards the closure of the fuel cycle, while the latter are currently seen as a highly promising alternative to conventional alloys for improving structural material properties. This project represents a cornerstone of our research efforts, as it will produce a significant amount of data for multiscale models that simulate the behavior of these materials in nuclear reactors.
The work will take place at the Nuclear Fuel Department of the IRESNE Institute at the CEA center of Cadarache in the south of France, in a team consisting of several materials modeling experts, in close collaboration with another CEA team in the Paris region specialized in artificial intelligence. The findings will be disseminated through scientific publications and participation in both national and international conferences. This PhD thesis will enable the candidate to acquire essential skills in materials science, advanced machine learning methods, data analysis, and software development, which will be valuable for a future career in academic or industrial research in the fields of AI and materials engineering.
References:
https://doi.org/10.1039/D3CP02790B
https://doi.org/10.1126/science.aaw1147
https://doi.org/10.3390/e23010098
Portable GPU-based parallel algorithms for nuclear fuel simulation on exascale supercomputers
Dans le cadre de cette thèse, nous cherchons un candidat francophone uniquement.