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.
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
Dislocation glide in body-centered-cubic high-entropy alloys
High entropy alloys are single-phase multi-component solid solutions, all elements being present in high concentrations. This class of materials has significant improvements in mechanical properties over "conventional" alloys, particularly their high strength at high temperature. It is commonly accepted that good mechanical performance comes from the interactions of dislocations with the alloying elements and that at high temperature interstitial impurities or interstitial doping, such as oxygen, carbon or nitrogen, play a preponderant role. The study of plasticity in concentrated alloys with a body-centered cubic crystal structure in the high temperature range therefore constitutes the objective of this PhD thesis. The associated technological challenges are important, these alloys being promising structural materials, notably for nuclear applications where operating temperatures above room temperature are targeted.
This work aims to understand and model the physical mechanisms controlling the mechanical strength of these alloys at high temperature, by considering different concentrated alloys of increasing complexity and by using atomistic simulations, in particular ab initio electronic structure calculations. We will first focus on the binary alloy MoNb before extending to the ternary alloys MoNbTi and MoNbTa and studying the impact of oxygen impurities on plastic behavior of these alloys. We will model the dislocation cores and analyze their interaction with interstitial and substitutional elements in order to determine the energy barriers controlling their mobility. Based on these ab initio results, we will develop strengthening models notably allowing us to predict the yield strength as a function of temperature and alloy composition.
This work will be carried out within the framework of the DisMecHTRA project funded by the French National Research Agency, allowing in particular to compare our strengthening models with the data from the experiments which are planned in the project (mechanical tests and transmission electron microscopy), and which will be carried out by the other partners (CNRS Toulouse and Thiais). The PhD thesis, hosted at CEA Saclay, will be co-supervised by a team from CEA Saclay and MatéIS (CNRS Lyon).
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.
Alteration mechanisms study of MOX spent fuel in the presence of cimentious bentonitic material (MREA). Experimental and modeling approaches
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.
The Pd-Rh-Ru-Te-O system in nuclear glasses and its impact on the glass melt conductivity
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.
Design and Optimisation of an innovative process for CO2 capture
A 2023 survey found that two-thirds of the young French adults take into account the climate impact of companies’ emissions when looking for a job. But why stop there when you could actually pick a job whose goal is to reduce such impacts? The Laboratory for Process Simulation and System analysis invites you to pursue a PhD aiming at designing and optimizing a process for CO2 capture from industrial waste gas. One of the key novelties of this project consists in using a set of operating conditions for the process that is different from those commonly used by industries. We believe that under such conditions the process requires less energy to operate. Further, another innovation aspect is the possibility of thermal coupling with an industrial facility.
The research will be carried out in collaboration with CEA Saclay and the Laboratory of Chemical Engineering (LGC) in Toulouse. First, a numerical study via simulations will be conducted, using a process simulation software (ProSIM). Afterwards, the student will explore and propose different options to minimize process energy consumption. Simulation results will be validated experimentally at the LGC, where he will be responsible for devising and running experiments to gather data for the absorption and desorption steps.
If you are passionate about Process Engineering and want to pursue a scientifically stimulating PhD, do apply and join our team!
Building a new effective nuclear interaction model and propagating statistical errors
At the very heart of any « many-body » method used to describe the fundamental properties of an atomic nucleus, we find the effective nucleon-nucleon interaction. Such an interaction should be capable of taking into account the nuclear medium effects. In order to obtain it, one has to use a specific fitting protocol that takes into account a variety of nuclear observables such as radii, masses, the centroids of the giant resonances or the properties of the nuclear equation of state around the saturation density.
A well-known model of the strong interaction is the Gogny model. It is a linear combination of coupling constants and operators, plus a radial form factor of the Gaussian type [1]. The coupling constants are determined via a fitting protocol that typically uses the properties of spherical nuclei such as 40-48Ca, 56Ni, 120Sn and 208Pb.
The primary goal of this thesis is to develop a consistent fitting protocol for a generic Gogny interaction in order to access some basic statistical information, such as the covariance matrix and the uncertainties on the coupling constants, in order to be able to perform a full statistical error propagation on some selected nuclear observables calculated with such an interaction [2].
After having analysed the relations between the model parameters and identified their relative importance on how well observables are reproduced, the PhD candidate will explore the possibility of modifying some terms of the interaction itself such as the inclusion of a real three-body term or beyond mean-field effects.
The PhD candidate will work within a nuclear physics group at CEA/IRESNE Cadarache. The work will be done in close collaboration with CEA/DIF. Employment perspectives are in academic research and nuclear R&D labs.
[1] D. Davesne et al. "Infinite matter properties and zero-range limit of non-relativistic finite-range interactions." Annals of Physics 375 (2016): 288-312.
[2] T. Haverinen and M. Kortelainen. "Uncertainty propagation within the UNEDF models." Journal of Physics G: Nuclear and Particle Physics 44.4 (2017): 044008.
Online analysis of actinides surrogates in solution by LIBS and AI for nuclear fuel reprocessing processes
The construction of new nuclear reactors in the coming years will require an increase in fuel reprocessing capacity. This evolution requires scientific and technological developments to update process monitoring equipment. One of the parameters to be continuously monitored is the actinide content in solution, which is essential for process control and is currently measured using obsolete technologies. We therefore propose to develop LIBS (laser-induced breakdown spectroscopy) for this application, a technique well suited for quantitative online elemental analysis. As actinide spectra are particularly complex, we shall use multivariate data processing approaches, such as several artificial intelligence (AI) techniques, to extract quantitative information from LIBS data and characterize measurement uncertainty.
The aim of this thesis is therefore to evaluate the performance of online analysis of actinides in solution using LIBS and AI. In particular, we aim to improve the characterisation of uncertainties using machine learning techniques, in order to strongly reduce them and to meet the monitoring needs of the future reprocessing plant.
Experimental work will be carried out on non-radioactive actinide simulants, using a commercial LIBS equipment. The spectroscopic data will drive the data processing part of the thesis, and the determination of the uncertainty obtained by different quantification models.
The results obtained will enable publishing at least 2-3 articles in peer-reviewed journals, and even to file patents. The prospects of the thesis are to increase the maturity level of the method and instrumentation, and gradually move towards implementation on a pilot line representative of a reprocessing process.