This thesis concerns the study of different ways of separating lithium isotopes by laser. The work will be conducted both theoretically and experimentally. The objective is to determine an optimal way as well as its performances.
In France, the reference way remains the reprocessing of spent fuel and the recovery of certain materials such as uranium and plutonium through the elaboration of MOX fuels and its recycling. However, the direct storage of fuels (UOX and MOX) in deep geological repository is also being studied in order to ensure that French storage concepts (Cigéo) are suitable for spent fuels as requested and included in the National Plan for the Management of Radioactive Materials and Waste (PNGMDR). Therefore, it is essential to study the alteration mechanisms of the spent fuel matrices in the presence of environmental materials that are similar, on a laboratory scale, to the current storage concept of radioactive waste in deep geological disposal: HA cells dug in the Callovo-Oxfordian (COx) clay whose low-alloy steel liner is isolated from the clay by a cimentious bentonitic grout called MREA. There is various objectives : on the one hand, to determine the impact of the environment on the alteration mechanisms of the fuel matrix as well as on the radionuclides release, and on the other hand, to develop a geochemical model to account for the main physicochemical processes involved. These studies are carried out at the ATALANTE facility (DHA) of the CEA Marcoule, where leaching experiments and characterizations of MOX fuels are achievable. This work is performed as part of the COSTO project and is supported by Andra and EDF.
In France, high-level nuclear waste is vitrified. The components of the waste are integrated in a homogeneous vitreous matrix. However, platinum group metals (PGM) Pd, Rh and Ru are very poorly soluble in the glass melt and they form particles, combined or not with oxygen or tellurium.
Ru and Rh may reduce in their metallic state during glass processing. They are then more electrically conductive and their effect on the physical properties of the glass melt may affect the vitrification process control. Hence, the knowledge of the speciation and the morphology of the PGM elements is essential for the control of the process.
Thereby, this PhD will be split in 2 interdependent approaches: the first one by thermodynamic Calphad calculations and the other one by experimentations. First, the experimental approache will aim to understand and quantify the reduction of (Ru,Rh)O2 and the solubilisation of Ru and Rh in Pd-Te thanks to elaborations and characterizations (SEM and XRD mainly) of glasses with PGM particles. The results will complete a Calphad database. Calculations will help to discuss experimental results and will enable to predict the PGM state in the glass melt during the industrial vitrification. Secondly, electrical conductivity measurements at high temperature will be implemented on the glasses previously made to determine the impact of Ru and Rh speciation on the global conductivity of the melt.
The applicants must be rigorous, autonomous and have good communication and writing skills. Knowledge and experience in the field of glass or thermodynamics would be a plus.
Waste management is an important area of research for nuclear energy which is an essential building block for the development of low-carbon energies. This thesis focuses on understanding the mechanical and thermal behavior of a particular type of waste: bituminous matrices. This understanding is essential to contribute to its nuclear safety. In this context, we propose an experimental study of the mechanical behavior of a bituminous mix composed of salt grains of various sizes and chemical natures, as well as gas bubbles. More specifically, the aim is to characterize the impact of these elements on the rheology of the material, and to study the effects of salt sedimentation or bubble migration. To this end, bituminous mixes of various types will be synthesized. They will then be characterized in terms of rheology and imaging (2D by Optical Microscopy (OM) or Scanning Electron Microscopy (SEM), and 3D by X-ray tomography) over time. Rheological and sedimentation models will be developed on the basis of experimental results and implemented in existing codes developed at CEA.
The applicant will have access to a high-level analytical platform and a dedicated laboratory infrastructure that will enable him/her to acquire expertise in the field of materials analysis and properties that can be leveraged for his/her professional project.
As a research organization in the nuclear field and alternative energies, the CEA participates in fundamental studies involving dense suspensions. Inorganic particles (glass, zeolite, sludge, salts, or cement/sand) suspended in fluids, sometimes with very high viscosity like bitumen, are part of the systems under study for various applications. These include optimizing the filling of glass packages (Dem N' Melt process) or cement packages, where flow properties need to be optimized to ensure homogeneity of waste drums. Besides to addressing the recovery (historical sludges), treatment, and conditioning of waste in glass or bituminous matrices, concentrated suspensions of glass grains are being studied for high-temperature electrolysis production of dihydrogen.
In this optic, the research will initially focus on model concentrated suspensions, characterizing their flow properties under shear and compression. This latter type of mechanical test can trigger the appearance of frictional regimes, liquid/solid phase separation, and various non-linear responses that will need to be modeled. After this first stage, the topology, particle size distribution, and polydispersity of the solid particles will be varied to be as close as possible to the suspensions encountered in industry.
Energy production from renewable sources requires efficient storage systems to address imbalances between supply and demand. This project aims to develop cost-effective supercapacitors using composite electrodes derived from industrial by-products. Mineral binders, such as geopolymers or Alkali Activated Materials (AAM), made conductive by dispersing carbon black, are being studied for energy storage or heat generation applications. Based on a recently filed patent, we propose a detailed study of these conductive composites. Their performance will be evaluated depending on formulation and shaping parameters. Additionally, the porous network and the dispersion of conductive charges in the material will be thoroughly characterized. Finally, material shaping tests will be conducted, and supercapacitors will be assembled to study the impact of the process (3D printing) and geometries.
In various industrial applications, fully metallic seals are employed to guarantee a high level of sealing of mechanical assemblies under severe thermodynamics conditions. Their performance is entirely controlled by the mechanical behaviour of the contacting interface between the facing rough surfaces of the seal and the flange, similar to a fracture, anisotropic and multi-scale by nature. The objective of the thesis is to improve our comprehension and predicting capabilities of the sealing mechanisms of gases in a rough fracture using a numerical approach coupled with experiments.
The work takes place in the continuity of previous studies performed at the laboratory. It will focus first on the conception of an experimental apparatus that will be used to press two metallic rough surfaces against each other with a given force, having the possibility to measure the corresponding leakage rate as well. The experiments will be performed during loading and unloading of the contact to characterise the hysteresis phenomenon brought by the permanent deformation of the sealing material at first loading. The results obtained will be compared to numerical ones in various configurations using models developed at the laboratory, in order to validate these latter. By experience, it is known that the flow simulation gives satisfactory results, but discrepancies persist in the contact mechanics model. Thus, it should be improved regarding the plastic effects specifically encountered in contact, considering the finite thickness of the sealing liner and optimizing the computational cost. Afterwards, the preceding results will be transposed to the industrial case of the HELICOFLEX metal seal, using a two-scale modelling strategy, coupling the macroscopic information at the seal scale to the microscopic one at the roughness scale.
As part of the reprocessing of uranium oxide spent fuel, the final high-level liquid waste is packaged in glass using a two-stage process, calcination followed by vitrification. Calcination gradually transforms the liquid waste into a dry residue, which is mixed with preformed glass in a melting furnace. The calciner consists of a rotating tube heated by a resistance furnace. The calcined solutions consist of nitric acid and compounds in their nitrate form or insolubles in the form of metal alloys. In order to improve control of the calciner, it is proposed to model it.
The modelling will consist of creating and then coupling three models:
- A thermodynamic model to represent the transformations undergone by the material. This part will almost certainly involve ATD and ATG measurements, coupled with a design of experiments type approach (1st year).
- A material flow model. The literature already contains very simplified principles for representing the flow in a rotating tube calciner, but we will have to be innovative, in particular by defining tests to characterise the flow of the material during the calcination process (2nd year).
A thermal model that will take into account exchanges between the furnace and the calciner tube as well as exchanges between the material and the tube. The exchange coefficients will have to be characterised (1st year).
Combining these three models (3rd year) will give rise to an initial simplified calcination model. This model will be used to help control the calcination stage and also to train operators to control this apparatus.
You will be working in the LDPV, a multidisciplinary team (process, chemistry, fluid mechanics, modelling, mechanics, induction) comprising 16 engineers and technicians. A team with 30 years' experience in vitrification processes, recognised both nationally and internationally.
Scientific Context
The operation of complex equipment, particularly in the nuclear sector, relies on quick and secure access to heterogeneous data. Advances in generative AI, combined with Digital Twins (DT), offer innovative solutions to enhance human-system interactions. However, integrating these technologies into critical environments requires tailored approaches to ensure intuitiveness, security, and efficiency.
Proposed Work
This thesis aims to develop a generative AI architecture enriched with domain-specific data and accessible via mixed reality, enabling a glovebox operator to ask natural language questions. The proposed work includes:
A review of the state-of-the-art on Retrieval-Augmented Generation (RAG), ASR/TTS technologies, and Digital Twins.
The development and integration of a chatbot for nuclear operations.
The evaluation of human-AI interactions and the definition of efficiency and adoption metrics.
Expected Outcomes
The project aims to enhance safety and productivity through optimized interactions and to propose guidelines for the adoption of such systems in critical environments.
Before, during, and after... the characterization of the radiological state is essential at all stages of the decommissioning scenario of a nuclear facility. Can we intervene directly on-site, or is teleoperation necessary? Has the contamination of a given area been completely eliminated? How should we categorize a particular nuclear waste to optimize its future management?
In-situ non-destructive nuclear measurements aim to evaluate the radiological state of processes and equipment in real time, while meeting criteria of efficiency, safety, flexibility, and reliability, and reducing costs through rapid, precise, and non-invasive analyses. While characterization techniques for gamma emitters are well mastered, those for pure beta emitters remain a significant challenge due to the low range of beta radiation in matter and the ambient gamma noise, which makes in-situ detection particularly complex.
The integration of artificial intelligence (AI) tools, such as machine learning or deep learning, in this field opens new perspectives. These technologies enable the automation of the analysis of large amounts of data while extracting complex information that is often difficult to interpret manually, particularly for deconvoluting continuous beta radiation spectra. Initial results obtained in the framework of L. Fleres' thesis have shown that AI can effectively predict and quantify the beta-emitting radionuclides present in a mixture. Although promising, this approach, tested in laboratory conditions, still needs to be qualified in real-world field conditions.
The proposed thesis aims to continue and refine these developments. It will involve integrating new algorithms, exploring various neural network architectures, and enriching learning databases to improve the performance of current systems for the in-situ characterization of beta emitters. This will include scenarios where the beta/gamma signal-to-noise ratio is degraded, as well as the detection of low levels of activity in the presence of natural radioactivity. Other research avenues will include the detection of low-energy radionuclides and the adaptation of deconvolution tools for large-surface detectors.
The characterization methodology developed at the end of the project will have strong potential for industrial valorization, particularly in the fields of decontamination and decommissioning. The doctoral candidate will join a team with extensive experience in the implementation of non-destructive radiological characterization techniques and methods in-situ and will have the opportunity to evaluate the proposed solutions on some of the largest decommissioning projects in the world.
Desired Profile: The ideal candidate holds a degree from an engineering school or a Master's (M2) with solid knowledge of nuclear measurement, particularly regarding the physical phenomena related to the interactions of ionizing radiation with matter. Skills in statistical data processing methods and programming (Python, C++) would also be appreciated.