Bottom-up study of Ionic Transport in Unsaturated Hierarchical Nanoporous Materials : application to cement-based materials
Ion transport is critical in determining the durability of cement-based materials and, therefore, the extension of service life of concrete (infra)structures. Transport phenomena determine the containment capacity of concrete, which is crucial in the design and asset management of concrete infrastructures for energy production. Under most service conditions, concrete exists in unsaturated conditions. Anomalous transport has been associated with cement-based materials, and the reasons behind such deviations from the expected behavior of other porous materials may stem from nanoscale processes.
Research efforts have aimed to correlating material composition and microstructure to transport properties and durability. However, to date, the majority of predictive modeling of durability does not explicitly account for nanoscale processes, which are fundamental in determining transport properties. Recent advances have been made in quantifying the behavior of confined water in various phases present in cement systems. Calcium silicate hydrates (C-S-H) are the main hydrated phase in cement-based materials and present nanopores in the micro and mesopore range. The effects of desaturation remain however to be fully worked out. A fundamental understanding of transport processes requires a multiscale framework in which information from the molecular scale reverberates across other relevant scales (in particular, the mesoscale associated with C-S-H gel porosity (~nm), capillary porosity, and interfacial transition zone (~µm) up to the macroscopic scale of industrial application in cement-based materials).
The goal of this PhD work is to evaluate the ionic transport of chlorides, a critical species for the durability of concrete, under non-saturated conditions by combining small-scale simulations, multiscale modelling and experimentation in a bottom-up approach. The work will focus on the C-S-H. The project aims to characterize the effects of desaturation on the nanoscale processes driving transport of chlorides.
PRObablistic on-edge learning for SPINtronic-based neuromorphic systems
The hired joint UGA – KIT PHD candidate should be able to cover the work of the workpackage 1 and 2. He/she will also participate to technical meetings and have a good understanding on how the tasks of the other technical workpackages are executed, mainly by the partners with internal effort. As a whole, the PHD candidate will develop and optimize compact Computing in Memory architectures, provide high level models for further integration in large scale designs, perform validation of all proofs of concepts of new architectural implementations. He/she will be involved also in the design of algorithmic implementations of Bayesian Neural Networks adapted to the architecture. More in details, he/she will work on the following directions:
Design and optimization of the probabilistic neural networks, will be executed mostly in SPINTEC Laboratory in Grenoble, that will include:
1. full design stack of hardware accelerator without selector transistor for frequent Read and Write operations.
2. Design and validate an innovative architectural approach able to compensate for sneaky paths phenomena.
3. High-level modeling of the full crossbar architecture that includes the stochastic component.
4. Propose a full simulation and validation flow scalable to scaled to realistic architecture size and parameters that implement Bayesian tasks.
5. Perform Delay, power consumption and area overhead figures of merit
Modeling of complexation equilibria of actinides in nitric medium. Application to the PUREX process
The PAREX+ code is a major tool in the field of separation chemistry. It allows for the modelling and simulation of separation processes base on solvent extraction. In this code, the distribution of interest species between the aqueous and organic phases is calculated at every point in the process, both in steady and transitory states. The aim of this thesis is to improve this distribution model. To achieve this, a better understanding of the phenomena involved in the organic and aqueous phases is necessary, as well as a new approach to incorporate them into the model. This thesis thus combines experimental work and modeling. The student will join a supervisory team composed of experts in separation chemistry and modeling. His work will be valued through the publication of papers and participation in international conferences. At the end of this thesis, the student will have solid knowledge in the field of solvent extraction and its modeling, which he can leverage with industry or research organizations in the nuclear field or in other areas of separation chemistry (separation of rare earths or hydrometallurgy).
Thermo-chemo-mechanical modeling of sintering : effect of atmosphere and the differential densification on pellet shrinkage
Uranium dioxide (UO2) fuels used in nuclear power plants are ceramics, for which solid-phase sintering is a key manufacturing step. The sintering stage involves heat treatment under controlled partial O2 pressure that induces coarsening of UO2 grain and then consolidation and densification of the material. Densification induces macroscopic shrinkage of the pellet. If the compact (powder obtained by pressing, manufacturing step before sintering) is highly heterogeneous density, a difference in densification within the pellet may occur, leading to differential shrinkage and the appearance of defects.
The PhD thesis aims at developing a Thermo-chemo-mechanical modeling of sintering to simulate the impact of the gas composition and properties on the pellet densification. This scale will enable us to take into account not only the density gradients resulting from pressing, but also the oxygen diffusion kinetics that have a local impact on the densification rate, which in turn impacts the transport process. Therefore, a multiphysics coupling phenomenon has to be modelled and simulated.
This thesis will be conducted within the MISTRAL joint laboratory (Aix-Marseille Université/CNRS/Centrale Marseille CEA-Cadarache IRESNE institute). The PhD student will leverage his results through publications and participation in conferences and will have gained strong skills and expertise in a wide range of academic and industrial sectors.
Innovative modeling for multiphysics simulations with uncertainty estimates applied to sodium-cooled fast reactors
Multiphysics modeling is crucial for nuclear reactor analysis, yet uncertainty propagation across different physical domains—such as thermal, mechanical, and neutronic behavior—remains underexplored due to its complexity. This PhD project aims to address this challenge by developing innovative methods for integrating uncertainty quantification into multiphysics models.
The key objective is to propose optimal modeling approaches tailored to different precision requirements. The project will explore advanced techniques such as reduced-order modeling and polynomial chaos expansion to identify which input parameters most significantly impact reactor system outputs. A key aspect of the research is the comparison between "high-fidelity" models, developed using the CEA reference simulation tools, and "best-estimate" models designed for industrial use. This comparative analysis will highlight how these errors propagate through different models and simulation approaches.
The models will be validated using experimental data from SEFOR, a sodium-cooled fast reactor. These experiments provide valuable benchmarks for testing multiphysics models in realistic reactor conditions. This research directly addresses the growing need for reliable, efficient modeling tools in the nuclear industry, aiming to improve reactor safety and performance.
The candidate will work in a dynamic environment at the CEA, benefiting from access to advanced simulation resources and opportunities for collaboration with other researchers and PhD students. The project offers the possibility of presenting results at national and international conferences, with strong career prospects in nuclear reactor design, safety analysis, and advanced simulation.
oxygen ordering in zirconium: mechanisms, kinetics and associated mechanical properties
The aim of this work is to study the properties of binary zirconium-oxygen (Zr-O) alloys, particularly in the context of nuclear applications. Traditionally, oxygen is considered to be in solid solution in the zirconium matrix, without the formation of ordered compounds such as Zr6O or Zr3O. However, recent studies suggest that at temperatures below 600°C, ordered compounds can form, affecting the solubility limit of oxygen. These compounds, observed after heat treatments, could modify the mechanical properties of Zr-O alloys, particularly at room temperature and up to 350°C. The proposed thesis seeks to understand these mechanisms through X-ray diffraction and electron microscopy experiments, in order to study the arrangement of oxygen, the thermal stability of the compounds and their impact on plastic deformation. The aim is to optimise the use of these alloys in nuclear reactors.
Quantification and Optimization of the Mechanical State of Nb3Sn Superconductors during the Heat Treatment
In agreement with the CERN’s advertised will for the implementation of a super-collider, FCC type, high field superconducting electromagnets, based on Nb3Sn, are being developed. In the framework of the HFM (High Field Magnets) European collaboration, the LEAS at CEA Paris-Saclay is designing, manufacturing, and testing superconducting magnet demonstrators generating up to 16 T. Nb3Sn conductors require a heat treatment at 650 °C. During this heat treatment, several physico-chemical phenomena lead to the formation of the Nb3Sn superconducting phase. These phenomena induce a mechanical state impacting the superconducting properties of the material. A study of the different phenomena inducing dimensional changes inside the conductors would allow estimating the stresses inside the Nb3Sn superconducting phase following the heat treatment. The goal of this thesis is to study, using modeling and experiments, the thermomechanical state of the conductors during the heat treatment in order to estimate the internal stresses and their impact on the superconducting performances. The results will allow the improvement of the Nb3Sn superconducting properties in view of the production of high field magnets for future accelerators.
Digital reconstruction of an industrial tank for the improvement of real-time monitoring instrumentation
In the context of industrial digitalization and real-time monitoring, accessing 3D fields (velocity, viscosity, turbulence, concentration, etc.) in real time can be crucial, as local sensor networks are sometimes insufficient to provide a comprehensive view of the system's dynamics. This PhD project aims to investigate a methodology for the real-time reconstruction of fields within an instrumented industrial tank equipped with a mixing system. The proposed approach relies on finite element modeling of the relevant physics within the tank (e.g., fluid dynamics, thermal processes) and model reduction techniques such as physics-based Machine Learning (virtual sensor approach). A key focus of this thesis will also be the development of the tank instrumentation and its associated acquisition chain, both to validate the models and to generate a database for applying the proposed methodology.
SIMULATION-BASED PREDICTION OF VIBRATION IN CENTRIFUGES
Rotating machinery is a critical piece of equipment in many industrial plants, and its operation is regularly accompanied by balancing problems that result in potentially dangerous vibrations for operators and equipment. The centrifugal decanter, for example, is sometimes subject to vibrations that force the operator to slow down the production rate. The nuclear environment in which this equipment operates makes it impossible to carry out the measurements and observations required for a purely experimental study. The aim is therefore to carry out modelling with limited data in order to gain a detailed understanding of the phenomena involved. The aim of this work is to combine Euler-Euler type CFD simulations of the mass distribution in the rotating bowl with mass-spring modelling of the mechanical connections in order to get closer to the vibration signals measured industrially. Such a numerical tool would be a valuable aid in investigating the various potential sources of mass imbalance without the need for experimental replication. Combined with deep learning methods, this type of model would also make it possible to build an unbalance predictor from short vibration signals, opening the door to active control of the decanter
Design and optimization of an innovative breeding blanket concept for a compact high heat flux nuclear fusion reactor
Skills:
Technical: heat transfer, structural mechanics, hydraulics, materials, numerical simulation
Non-technical: writing, interpersonal skills, English
Prerequisites: this thesis will be preceded by a 6-month internship. Contact the supervisor for more details about the topic.
Context:
This PhD focuses on the design and optimization of an innovative breeding blanket for compact nuclear fusion reactors. Nuclear fusion offers a promising solution to produce clean and sustainable energy. However, it requires the continuous production of tritium, a rare isotope, through breeding blankets surrounding the plasma. These blankets must also extract the generated heat. In compact reactors, technical constraints are increased due to extremely high heat fluxes and severe thermal and neutron conditions.
The PhD will take place within the Design, Calculations, and Realizations Office at CEA Saclay, a recognized player in the development of breeding blankets at the European level. This office has designed several concepts, such as HCLL (Helium Cooled Lithium Lead) and BCMS (Breeder and Coolant Molten Salt), two types of blankets based on helium or molten salt cooling systems.
PhD description:
The research program will take place over three years. The first year will focus on studying existing blankets, identifying the constraints of compact reactors, selecting appropriate materials and heat transfer fluids, and developing a preliminary design of the blanket. The following years will be dedicated to multiphysics modelling (thermal, mechanical, neutron), followed by iterative optimization of the concept to improve its performance.
Perspectives:
The results of this PhD will have a significant impact on the development of compact fusion reactors by ensuring tritium production and structural integrity. This work could also open new avenues for future research on even more advanced breeding blankets, contributing to the growth of sustainable and commercially viable fusion energy.