Control & optimization of fuel cell temperature
Proton exchange membrane fuel cells (PEMFC) represent a key technology for the development of clean and sustainable energy systems, particularly for heavy-duty transport applications where their energy density is very attractive. However, in order to represent a viable industrial alternative, a number of obstacles still need to be overcome, including operating costs and, above all, the durability of the systems under real-world conditions. Among the levers for action, optimizing operating conditions is a promising avenue for limiting the degradation phenomena occurring within the cell. The operating temperature is a particularly key parameter because it affects all aspects of the system, from the kinetics of degradation mechanisms to the thermal capacity that the system can dissipate, including the water balance within the fuel cell. Despite the influence of this parameter on durability, it is generally only optimized at the system level to achieve the best performance, the shortest possible response time and to limit the size of the thermal management system.
The aim of this thesis is to work on optimizing the temperature management of a fuel cell within a system, taking into account not only performance but also sustainability criteria. To do this, the impact of operating temperature on degradation mechanisms will be analyzed using various simulation tools already available at LITEN and the teams' fifteen years of experience in studying PEMFC fuel cell degradation. Various thermal architectures will be proposed and evaluated in conjunction with the work on temperature control optimization. The latter will be implemented on a real fuel cell system in order to demonstrate the relevance of the proposed solution using concrete experimental data.
Innovative techniques for evaluating critical steps and limiting factors for batteries formation
The battery manufacturing sector in Europe is currently experiencing strong growth. The electrical formation step that follows battery assembly and precedes delivery has received little academic attention, despite being crucial for battery performance (lifespan, internal resistance, defects, etc.). It is an essential time-consuming and costly step in the process (>30% of the cell manufacturing cost, and 25% of the equipment cost in a Gigafactory) that would greatly benefit from optimization.
In this thesis, we propose studying battery formation using innovative, complementary, operando non-intrusive techniques. The goal is to identify the limiting mechanisms of the electrolyte impregnation step (filling electrode pores) and of the initial charge. The candidate will implement experimental methods to monitor and analyze these mechanisms. He will also establish a methodology and protocols for studying these steps, combining electrochemical measurements with non-intrusive physical characterizations under operating conditions. The research will focus on optimizing formation time and quality control during this stage.
From angstroms to microns: a nuclear fuel microstructure evolution model whose parameters are calculated at the atomic scale
Controlling the behavior of fission gases in nuclear fuel (uranium oxide) is an important industrial issue, as fission gas release or precipitation limit the use of fuels at extended burn-ups. The gas behavior is strongly influenced by the material’s microstructure evolution due to the aggregation of irradiation-induced defects (gas bubbles, dislocation loops and lines). Cluster dynamics (CD) (a kind of rate theory model) is relevant for modelling the nucleation/growth of the defect clusters, there gas content and the gas release. The current model has been parameterized following a multiscale approach, based on atomistic calculations (ab initio or empirical potentials). This model has been successfully applied to annealing experiments of UO2 samples implanted with rare gas atoms and has emphasized the impact of the irradiation damage on gas release. The aim of this PhD thesis is now to improve the model, particularly the damage parameterization, and to extend its validation domain through in depth comparison of simulation with a large set of recently obtained experimental results, such as gas release measurement by annealing of sample implanted in ion beam accelerator, bubble and loop observation by transmission electrons microscopy of implanted or in-pile irradiated samples. This global analysis will finally yield an improved parameterization of the CD model.
The research subject combines a “theoretical” dimension (improving the model) with an “experimental” one (interpreting existing experiments or designing some new ones). The variety of techniques will introduce you into the experimental world and thus broaden your scientific skills. You will be welcomed at the Fuel Behavior Modeling Laboratory (part of the Institute for Research on Nuclear Systems for Low-Carbon Energy Production, IRESNE, CEA Cadarache), where you will benefit from an open environment rich in academic collaborations. You also have to manage collaborations for the experiments analysis, for the model development and for the specification of additional atomistic calculations. You will be at the interface of atomistic techniques, large-scale simulation and various experimental techniques. Therefore, You will develop a broad view of irradiation effects in materials and of multi-scale modelling in solids in general.
This project is an opportunity to contribute to the overall development of numerical physics applied to multi-scale modeling of materials, occupying a pivotal position and adopting a global viewpoint. This will allow experiencing yourself the way computed fundamental microscopic data finally helps solving complex practical issues.
Further readings:
Skorek et al. (2012). Modelling Fission Gas Bubble Distribution in UO2. Defect and Diffusion Forum, 323–325, 209.
Bertolus et al. (2015). Linking atomic and mesoscopic scales for the modelling of the transport properties of uranium dioxide under irradiation. Journal of Nuclear Materials, 462, 475–495.
Study of the behaviour of mixed oxide fuels with degrade isotopy at the beginning of life.
France has decided to adopt a 'closed' nuclear fuel cycle. This involves processing spent fuel to recover valuable materials such as uranium and plutonium, while other compounds such as fission products and minor actinides constitute final waste. UO2 fuel irradiated in pressurised water reactors (PWRs) is currently reprocessed to produce plutonium (PuO2), which is then reused in the form of mixed oxide (MOX) fuel. This fuel is then irradiated in PWRs, a process known as plutonium monorecycling. The CEA is currently studying the multi-recycling of materials using fuels containing Pu from the processing of spent MOX assemblies. However, this multi-recycled plutonium contains a higher proportion of highly alpha-active isotopes (Pu238, Pu240 and Pu241/Am241), resulting in more severe alpha self-irradiation than current MOX fuels experience [1]. This exacerbates certain physical phenomena [2-5], such as fuel swelling due to helium precipitation and the creation of crystal defects and decreased thermal conductivity [6-8], which can alter its behaviour in the reactor.
The proposed thesis will study the impact of these phenomena on the behaviour of MOX fuels at the beginning of the irradiation, using a combination of experimentation and modelling. Heat treatments will be employed to analyse the mechanisms of crystal defect healing and helium behaviour. Various experimental techniques will be employed to characterise the structure and microstructure (X-ray diffraction, scanning electron microscopy (SEM), Raman spectroscopy and microprobe analysis), defect densities (transmission electron microscopy (TEM)), helium release (KEMS), thermal gradient reproduction (CLASH laser) and thermal conductivity (LAF laser). The results will inform simulations modelling the microstructure and thermal properties.
This cross-disciplinary study will improve our understanding of the phenomena involved in the initial power-up of fuels damaged by alpha self-irradiation, particularly the impact of helium produced by decay.
You will be based at the Multi-Fuel Design and Irradiation Laboratory (LECIM) within the Research Institute for Nuclear Systems for Low-Carbon Energy Production at CEA/Cadarache. For the experimental part of the project, you will collaborate with the Chemical Analysis and Materials Characterisation Laboratory (LMAT) at CEA/Marcoule and the European Research Centre (JRC) in Karlsruhe. You will have the opportunity to publish your results through scientific publications and conference presentations. This role offers the chance to develop your expertise in a variety of techniques that can be applied across multiple fields of materials science and engineering.
[1]O. Kahraman, thésis, 2023.[2]M. Kato et al., J Nucl Mater, 393 (2009) 134–140.[3]L. Cognini et al., Nuclear Engineering and Design 340 (2018) 240–244.[4] T. Wiss et al., Journal of Materials Research 30 (2015) 1544–1554.[5]D. Staicu et al., J Nucl Mater 397 (2010) 8–18.[6] T. Wiss et al.,Front. Nucl. Eng. 4 (2025) 1495360.[7]E.P. Wigner, J. Appl. Phys. 17 (1946) 857–863.[8]D. Staicu et al., Nuclear Materials and Energy 3–4 (2015) 6–11.
Parallel simulation and adaptive mesh refinement for 3D solids mechanics problems
The challenge of this PhD thesis is to implement adaptive mesh refinement methods for non-linear 3D solids mechanics adapted to parallel computers.
This research topic is proposed as part of the NumPEx (Digital for Exascale) Priority Research Programs and Equipment (PEPR). It is part of the Exa-MA (Methods and Algorithms for Exascale) Targeted Project. The PhD will take place at CEA Cadarache, within the Institute for Research on Nuclear Energy Systems for Low-Carbon Energy Production (IRESNE), as part of the PLEIADES software platform development team, which specializes in fuel behavior simulation and multi-scale numerical methods.
In finite element simulation, adaptive mesh refinement (AMR) has become an essential tool for performing accurate calculations with a controlled number of unknowns. The phenomena to be taken into account, particularly in solids mechanics, are often complex and non-linear: contact between deformable solids, viscoplastic behaviour, cracking, etc. Furthermore, these phenomena require intrinsically 3D modelling. Thus, the number of unknowns to be taken into account requires the use of parallel solvers. One of the current computational challenges is therefore to combine adaptive mesh refinement methods and nonlinear solid mechanics for deployment on parallel computers.
The first research topic of this PhD thesis concerns the development of a local mesh refinement method (of block-structured type) for non-linear mechanics, with dynamic mesh adaptation. We will therefore focus on projection operators to obtain an accurate dynamic AMR solution during the evolution of refined areas.
The other area of research will focus on the effective treatment of contact between deformable solids in a parallel environment. This will involve extending previous work, which was limited to matching contact meshes, to the case of arbitrary contact geometries (node-to-surface algorithm).
The preferred development environment will be the MFEM tool. Finite element management and dynamic re-evaluation of adaptive meshes require assessing (and probably improving) the efficiency of the data structures involved. Large 3D calculations will be performed on national supercomputers using thousands of computing cores.
his will ensure that the solutions implemented can be scaled up to tens of thousands of cores.
Designing a hybrid CPU-GPU estimator for neutron transport: Advancing eco-efficient Monte Carlo simulations
Digital twins incorporating Monte Carlo simulation models are currently being developed for the design, operation, and decommissioning of nuclear facilities. These twins are capable of predicting physical quantities such as particle fluxes, gamma/neutron heating, and dose equivalent rates. However, the Monte Carlo method presents a major drawback: high computational time to achieve acceptable variance levels.
To enhance simulation efficiency, the eTLE estimator has been developed and integrated into the TRIPOLI-4® Monte Carlo code. Compared to the conventional TLE (Track Length Estimator), eTLE offers lower theoretical variance, particularly in highly absorbing media, by contributing to the detector response even when particles do not physically reach it. Nevertheless, its computational cost remains significant, especially when evaluating multiple detectors.
Two recent PhD works have proposed variants to overcome this limitation. The Forced Detection eTLE- (Guadagni, EPJ Plus 2021) employs preferential sampling that directs pseudo-particles toward the detector at each collision. It is particularly effective for small detectors and configurations with moderate shielding, especially for fast neutrons. The Split Exponential TLE (Hutinet & Antonsanti, EPJ Web 2024) is based on an asynchronous GPU approach, offloading straight-line particle transport to the graphics processor. Through multiple sampling, it maximizes GPU utilization and enables more efficient exploration of phase space.
The proposed thesis aims to combine these two approaches into a hybrid estimator named seTLE-DF. This new estimator could be used either directly or to generate importance maps without relying on auxiliary deterministic calculations. Its implementation will require dedicated GPU developments, particularly to optimize the geometry library and memory management in complex geometries.
This research topic aligns with green computing objectives, aiming to reduce the carbon footprint of high-performance computing. It relies on a hybrid CPU-GPU strategy, avoiding full porting of the Monte Carlo code to GPU. Solutions such as half-precision formats will be considered, and an energy impact assessment will be conducted before and after implementation. The future PhD student will be welcomed with the IRESNE Institute (CEA Cadarache)and will acquire strong expertise in neutron transport simulation, facilitating integration into major research institutions or companies within the nuclear sector.
Modeling of a non-equilibrium dispersed phase and its fragmentation
In the context of the sustainable use of nuclear energy to produce carbon-free electricity, fourth-generation reactors, also known as "fast neutron" reactors, are necessary to close the fuel cycle.
This thesis falls within the framework of safety studies associated with such sodium-cooled reactors, and more particularly the hypothetical situation of a molten core relocating by gravity towards the core catcher at the bottom of the reactor vessel. A jet of corium (mixture of molten fuel and structural elements of the core) then interacts violently with the coolant, inducing, among other things, the fragmentation of the corium jet into droplets coupled with film boiling of the coolant. Characteristics of the resulting dispersed phase of corium and its fragmentation are crucial for studying the risk of runaway and steam explosion.
The aim of this thesis is to model a dispersed phase and its fragmentation in a surrounding fluid, using an approach that is both efficient and able to account to the scale variations and thermal imbalances between the droplets and the carrier phase. The method considered to meet these objectives is the method of moments, which derives from a kinetic model. It requires adequate closure and numerical schemes that satisfy non-standard constraints, while offering, in return, a crucial cost/accuracy compromise in the context studied. The advancements will be a priori implemented in the CFD software SCONE, built on the CEA's open-source TRUST platform.
The main work location will be based at the LMAG (Laboratory of Severe Accidents Modeling) at the IRESNE Institute of CEA Cadarache. Part of the work will also be carried out at the EM2C Laboratory (Molecular and Macroscopic Energetics, Combustion) – CNRS/CentraleSupélec in Paris.
The future PhD will work in a scientific dynamic environment and will acquire skills enabling to aspire to academic and industrial R&D positions.
Keywords : Dispersed Phase, Fragmentation, Kinetic, Method of Moments, Multiphase, Numerical methods, Severe Accidents.
Design artificial intelligence tools for tracking Fission Product release out of nuclear fuel
The Laboratory for the Analysis of Radionuclide Migration (LAMIR), part of the Institute for Research on Nuclear Systems (IRESNE) at CEA Cadarache, has developed a set of advanced measurement methods to characterize the release of fission products from nuclear fuel during thermal transients. Among these innovative tools is an operando in situ imaging system that enables real-time observation of these phenomena. The large amount of data generated by these experiments requires dedicated digital processing techniques that account for both the specificities of nuclear instrumentation and the underlying physical mechanisms.
The goal of this PhD project is to develop an optimized data processing approach based on state-of-the-art Artificial Intelligence (AI) methods.
In the first phase, the focus will be on processing thermal sequence images to detect and analyze material movements, aiming to identify an optimal image-processing strategy defined by rigorous quantitative criteria.
In the second phase, the methodology will be extended to all experimental data collected during a thermal sequence. The long-term objective is to create a real-time diagnostic tool capable of supporting experiment monitoring and interpretation.
This PhD will be carried out within a collaborative framework between LAMIR, which has recognized expertise in nuclear fuel behavior analysis and imaging, and the Institut Fresnel in Marseille, known for its strong background in image analysis and artificial intelligence.
The candidate will benefit from a multidisciplinary and stimulating research environment, with opportunities to present and publish their work at national and international conferences and in peer-reviewed journals.
Development of manganese-doped uranium oxide fuel: sintering mechanisms and microstructural changes
This PhD project focuses on developing nuclear fuels with improved properties through the addition of a dopant, for use in pressurized water reactors.
In nuclear reactors, the fuel consists of uranium dioxide (UO2) pellets stacked inside zirconium alloy cladding. These pellets, in contact with the cladding, must withstand extreme conditions of temperature and pressure. One of the challenges is to limit chemical interactions that may occur during the migration of fission products from the center to the periphery of the pellet and with the cladding. A notable example of such a phenomenon is the stress corrosion assisted by iodine, which can occur during accidental transients.
One strategy is to dope the UO2 ceramic with a metal oxide in order to control the material’s microstructure and also to modify its thermochemical behavior, thereby limiting both the mobility and corrosive nature of fission gases. Among the possible dopants, manganese oxide (MnO) represents a promising option and a potential alternative to chromium oxide (Cr2O3), which is currently a mature solution for the industry.
This PhD will explore the role of manganese in the sintering of UO2, particularly the microstructure and final properties of the fuel. The work will take place at the CEA Cadarache center, within the Institute for research on nuclear systems for low-carbon energy production (IRESNE).
During these three years, you will be hosted in the Laboratory for the study of uranium-based fuels (LCU) within the fuel study department (DEC), in close connection with the Laboratory for fuel behavior modeling (LM2C).
This research, combining experimentation and modeling, will be structured around three main topics:
• Study of the influence of manufacturing conditions on the microstructure of Mn-doped UO2,
• Investigation of the impact of doping on defect formation in UO2 and the associated properties,
• the contribution to the thermodynamic modelling of the system, based on experimental tests.
During this PhD, you will gain solid experience in the fabrication and advanced characterization of innovative materials, particularly in the field of ceramics for the nuclear industry. Your work could lead to publications, patents, and participation in national and international conferences.
You will also acquire numerous technical skills applicable across various research and industrial fields, including energy, microelectronics, chemical and pharmaceutical industries.
Atomistic investigation of the thermophysical properties of 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 for the CEA to deploy new computational methodologies in order to investigate the evolution of their thermo-physical properties under irradiation conditions.
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.
The first step of your work will involve continuing the development of atomic-scale computational models for UMo, as initiated within the host laboratory. These models use machine learning methods to develop interatomic potentials and will be validated by comparison with existing experimental data for this material. They will then be used to assess the temperature-dependent evolution and the impact of defect accumulation (both point and extended defects) on several thermophysical properties critical to fuel modeling, such as elastic properties, density, thermal expansion, as well as thermal properties like specific heat and thermal conductivity. In collaboration with other researchers in the department, you will format these results for integration into the Scientific Computing Tools used to simulate the behavior of nuclear fuels.
In a second phase, you will be responsible for extending the validity of your models to account for the formation of fission gases such as xenon within UMo single crystals. This will enable you to simulate the stability of xenon clusters in UMo crystals. These calculations, performed using classical molecular dynamics methods, will be systematically compared with experimental observations obtained via transmission electron microscopy.
The results obtained during the various stages of this project will be completely innovative and will be the subject of scientific publications as well as presentations at international scientific conferences. Besides, this work will enable you to complement your training by acquiring skills applicable to many areas of materials science, including ab initio calculations, machine learning-based interatomic potential fitting, classical molecular dynamics, use of CEA supercomputers, and key concepts in statistical physics and condensed matter physics—fields in which the supervising team members are recognized experts.
You will join the Fuel Behavior Modeling Laboratory at the Research Institute for Nuclear Systems for Low-Carbon Energy Production (IRESNE, CEA Cadarache), a dynamic research team where you will have regular opportunities to interact with fellow PhD students and researchers. This environment also provides extensive opportunities for national and international collaboration, including with:
• Developers and users of the MAIA fuel performance code (dedicated to research reactor fuel studies),
• Experimental researchers from the Nuclear Fuel Studies Department,
• Teams from other CEA centers (Saclay, CEA/DAM),
• International partners.
This rich and multidisciplinary context will enable you to fully engage with the scientific community focused on nuclear materials science.
[1] Dubois, E. T., Tranchida, J., Bouchet, J., & Maillet, J. B. (2024). Atomistic simulations of nuclear fuel UO2 with machine learning interatomic potentials. Physical Review Materials, 8(2), 025402.
[2] Chaney, D., Castellano, A., Bosak, A., Bouchet, J., Bottin, F., Dorado, B., ... & Lander, G. H. (2021). Tuneable correlated disorder in alloys. Physical Review Materials, 5(3), 035004.