Study of catalysis on stainless steels
The materials (mainly stainless steels) aging of the spent nuclear fuel reprocessing plant is the focus of an important R&D activity at CEA. The control of this aging will be achieved by a better understanding the corrosion mechanisms the stainless steels in nitric acid (the oxidizing agent used in the reprocessing steps).
The aim of the PhD is to develop a model of corrosion on a stainless steel in nitric acid as a function of temperature and the acid nitric concentration. This PhD represents a technological challenge because currently few studies exist on in situ electrochemical measurements in hot and concentrated nitric acid. The PhD student will carry out by coupling electrochemical measurements, chemical analyses (UV-visible-IR spectrometry...) and surfaces analyses (SEM, XPS,…). Based on these experimental results, a model will be developed, which will be incorporated in the future in a more global model of the industrial equipments aging of the plant.
The laboratory is specialized in the corrosion study in extreme conditions. It is composed of a very dynamic and motivated scientific team which has the habit to receive students.
Kinetics of the Melting Front in a Phase Change Material Used for Decay Heat Removal in an Innovative Nuclear Reactor
In the context of developing innovative sodium-cooled fast reactors (SFR), this PhD aims to explore the use of a phase change material (PCM) to remove residual power. The PCM studied in this project is Zamak, a metallic alloy that presents advantageous properties for such thermal applications. Some SFR designs incorporate passive safety systems intended to ensure the removal of residual power, which refers to the heat generated by delayed fission and radioactive decay of fuel isotopes after reactor shutdown. The use of PCM is a promising option, as they can absorb and store heat through a melting process and subsequently release it gradually during a solidification process.
The core of this PhD focuses on Computational Fluid Dynamics (CFD) modeling of the Zamak melting process and the scaling of this model for use in a system-size calculation tool. The main challenge lies in predicting the behavior of the melting front, its stability, and its impact on the kinetics of residual power removal. This melting front is influenced by numerous factors such as the wetting angle and the physico-chemical properties of the PCM-wall or PCM-surrounding gas interface, which will be examined throughout the thesis. The research will thus involve developing a CFD model that integrates these aspects, using a porous enthalpy approach, allowing predictive simulations of the PCM's behavior in the residual power removal system. A scaling analysis will then be conducted.
The PhD candidate will be part of a research team on innovative reactors at the IRESNE institute located at the CEA Cadarache site. Career opportunities after the thesis include academic research, R&D, and the nuclear industry, as well as sectors utilizing PCM technologies.
Tri-axial cell investigations and consideration of the influence of the behaviour of the agglomerates (U-Pu)O2 microstructure on the simulation of fuel shaping
The research topic concerns the influence of the behaviour of the (U-Pu)O2 agglomerate microstructure on the simulation of fuel shaping through triaxial cell investigations. It is based on multi-scale experimental and numerical studies in order to propose simulations of the shaping of actinide fuels, taking into account the breakage and rearrangement of agglomerates in the behaviour laws VER on homogenised VER. To this end, investigations in triaxial cells are envisaged, on the one hand on VER using X-CT tomography on simulating inactive model powders and on the other hand on industrial-sized samples on real active powders. Fracture tests using X-ray tomography will also be carried out on inactive materials and without tomography on active materials, in order to compare experimental and numerical results in the case of damage to pre-sintered fuels. A comparison will also be planned to take into account the impact of the proposed approach on the parameters of the models currently used for macroscopic simulations of fuel shaping on an industrial scale.
A revolution in intervention in complex environments: AI and Digital twins in synergy for innovative and effective solutions.
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.
Atomistic investigation of the diffusion of small xenon clusters in the metallic nuclear fuel UMo
This project is centered on the application of atomistic methods in order to investigate the stability and diffusion of intra-granular xenon clusters within the metallic nuclear fuel UMo.
Uranium – molybdenum alloys UMo present excellent thermal properties and a good uranium density. For those reasons, they are considered as nuclear fuel candidates for research reactors. It is therefore crucial to deploy new computational methodologies in order to investigate the evolution of their thermophysical properties under irradiation conditions.
During this PhD project, you will be in charge of validating (and, if necessary, recalibrating) the atomistic computational models for UMo that have been published in the literature. You will then apply those to the simulation of the stability and diffusion of small xenon clusters (typically up to 5 xenon atoms) within UMo crystals. Those computations will be performed leveraging accelerated molecular dynamics methods, and systematically compared to the results obtained for the reference nuclear fuel UO2. The results will also be analyzed by comparison to experimental measurements performed within the department, as well as used as reference data for larger-scale nuclear fuel performance codes. The results of your research will be published in scientific journals, and you are expected to attend international conferences to present your findings.
Those different investigations will allow you to acquire a set of competences applicable to many areas of materials science: ab initio calculations, machine-learning adjustment of interatomic potentials, classical and accelerated molecular dynamics, as well as many elements of statistical physics and condensed matter physics, which are among the areas of expertise of the PhD advisors.
The PhD will be based in the Fuel Behavior Modeling Laboratory (IRESNE Institute, CEA Cadarache), a dynamic research environment within which you will have the opportunity to interact with other PhD students. You will also benefit from a rich collaborative network (experimental researchers from the department, ISAS Institute at CEA Saclay, CINAM Laboratory in Marseille), that will allow you to become a member of the nuclear materials research community.
Optimization by Artificial Intelligence of In Situ Characterization of Pure Beta Radionuclides in Complex 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.
Mesoscopic simulations and development of simplified models for the mechanical behaviour of irradiated concrete
In nuclear power plants, the concrete biological shield serves as a support for the reactor vessel and as a protective shield against radiation. Over the long term, prolonged exposure to neutron radiation can cause the concrete aggregates to expand through amorphisation, leading to micro-cracking and degradation of its mechanical properties. This is an important issue in studies aimed at extending the life of power plants. At the mesoscale, these phenomena can be modelled by separating the behaviour of the aggregates, the cementitious matrix and the interfacial transition zones. However, it is difficult to describe the initiation and propagation of microcracks in such complex heterogeneous multi-cracked systems. The aim of this thesis, carried out as part of a Franco-Czech ANR project, is to develop a high-performance numerical simulation tool for analysing the effects of neutron irradiation on concrete at the mesoscopic scale. A coupled thermo-hydro-mechanical approach will be used in which the behaviour of the matrix will take into account shrinkage, creep and micro-cracking. The simulations will be validated using experimental data obtained on tested samples, and the numerical tool will then be used to estimate the impact of various factors on the behaviour and performance of concrete subjected to neutron irradiation.
This research project is aimed at a PhD student wishing to develop their skills in materials science, with a strong focus on multiphysical and multiscale modelling and numerical simulations.
Ultrasound-assisted decontamination of Hg-bearing solids
Mercury is one of the most dangerous pollutants. Yet, it has been widely used in the industry, in particular in electrolysers (chlor-alkali process), resulting in many contaminated facilities. Existing methods to stabilise or decontaminate are either energy-consuming or limited in terms of speciation. The aim here is to develop a new method combining leaching and ultrasonic irradiation, to decontaminate porous solids (e.g. mortar). The characterisation of solids and liquids before/after decontamination will be performed using SEM-EDX, XRD and XRF.
The PhD study will be performed in Marcoule centre, located 30 minutes from Avignon. The two host laboratories are the Laboratory of Supercritical Processes and Decontamination (DMRC/STDC/LPSD) and the Laboratory of Sonochemistry in Complex Fluids (ICSM//LSFC). Marcoule site is served by bus and hosts many PhDs and post-docs. The candidate should hold a master degree with a chemical engineering background and desirable skills in analytical chemistry and inorganic chemistry. The candidate will gain initial experience in the field of decontamination, which is one of the major problems associated with the circular energy economy. Depending on the focus of the thesis, they will be able to pursue a career in academia or industry.
Cohesive powder simulation: link between atomic and grain scale
Nuclear fuel is produced through a powder metallurgy process that involves several stages of the granular medium preparation (grinding, mixing, pressing and sintering). The powders used during these stages exhibit strong cohesion between the grains, making their flow behavior complex. Predicting powder behavior is a critical industrial challenge to quickly adapt to raw material changes, optimize product quality, and enhance production rates.
This thesis aims to establish a link between the properties of powders and their behavior during flow and pressing. Grain cohesion is a key factor that influences both the flow and densification of granular materials. This cohesion is governed by several interparticle forces, such as van der Waals forces, capillary interactions, and electrostatic forces. Understanding these interactions at the atomic scale is essential for accurately predicting and modeling powder behavior. The thesis seeks to address two central questions: How do the surface properties of grains at the atomic level influence the cohesive forces at the grain scale? And how can we scale up from the atomic level to the grain scale to simulate powders more realistically?
Multi-scale simulation approaches are crucial for bridging the gap between microscopic phenomena and the macroscopic behavior of granular materials. Current Discrete Element Method (DEM) simulations rarely incorporate fundamental interactions, such as van der Waals forces, electrostatic forces, and capillary effects, into their contact models. Recent research (1) (2) has explored the impact of cohesion using a simplified approach, treating it as an attractive force or cohesive energy. Simulation methods like Molecular Dynamics (MD) or Coarse-graining enable the modeling of material behavior at finer scales, based on these local structural and chemical properties. A deeper understanding of cohesion at small scales will enhance the predictive capabilities of DEM simulations and clarify the relationship between powder properties and their overall behavior.The main goal of this thesis is to better understand the relationships between atomic-scale interactions and grain-scale cohesion and to assess the consequences for simulations of powder pressing and flow.
The primary goal of this thesis is to make connections between the atomic-scale interactions and grain-scale cohesion and to simulate the powder flow and compaction processes.
One of the main challenges in this project is the development of DEM contact laws that incorporate complex atomic-scale interactions. This will require close collaboration between experts in atomic-level simulations and those working on DEM modeling. Additionally, validating these models through experimental comparisons is essential to ensure their accuracy and relevance for industrial applications.
The PhD candidate will be based at the IRESNE Institute (CEA-Cadarache) within the Laboratory of Numerical Methods and Physical Components on the PLEIADES platform, part of the Department of Fuel Studies. They will collaborate with the Fuel Behavior Modeling Laboratory and will have access to state-of-the-art modeling and simulation tools, as well as a collaborative environment with the Mechanics and Civil Engineering Laboratory at the University of Montpellier.
Bibliography
1. Sonzogni, Max. Modélisation du calandrage des électrodes Li-ion en tant que matériau granulaire cohésif : des propriétés des grains aux performances de l'électrode. s.l. : Thèse, 2023.
2. Tran, Trieu-Duy. Cohesive strength and bonding structure of agglomerates composed. 2023.
Study of the corrosion behaviour in NaCl-MgCl2-CeCl3 of a nickel-based alloy in the presence of fission products (Te, S) for molten salt reactor
Access to clean and affordable energy seems more crucial than ever in the current context of climate emergency. Several avenues have been explored for years, but many technological barriers remain to be overcome in order to realise them, as they represent significant technological breakthroughs. Whether it's for energy storage or 4th generation nuclear reactors, the molten salt medium used as a heat transfer fluid and/or fuel is highly corrosive, making the choice of structural materials very complex.
The objective of the proposed PhD project within the Service of Corrosion and Material Behaviour (S2CM) is the comprehensive study of the behaviour of promising nickel-based alloys in the NaCl-MgCl2-CeCl3 ternary system, representative of the salt used in the French molten salt reactor concept, at 600°C. By "comprehensive", this refers to everything from specimen preparation to the multi-scale and multi-technique characterisation of corrosion products. This topic has therefore a strong experimental character and focuses on understanding corrosion mechanisms. The influence of fission products, such as tellurium or sulphur, on corrosion mechanisms will be specifically studied.