Scaling Up Dislocation Dynamics Simulations for the Study of Nuclear Material Aging
Materials used in nuclear energy production systems are subjected to mechanical, thermal, and irradiation condition, leading to a progressive evolution of their mechanical properties. Understanding and modeling the underlying physical mechanisms involved is a significant challenge.
Dislocation Dynamics simulation aims to understand the behavior of the material at the crystal scale by explicitly simulating the interactions between dislocations, microstructure, and crystal defects induced by irradiation. The CEA, CNRS, and INRIA have been developing the NUMODIS calculation code for this purpose since 2007 (Etcheverry 2015, Blanchard 2017, Durocher 2018).
More specific work on zirconium alloys (Drouet 2014, Gaumé 2017, Noirot 2025) has allowed the validation and enhancement of NUMODIS's ability to handle these individual physical mechanisms by directly comparing them with experiments, through in situ tensile tests under a transmission electron microscope. However, these studies are limited by the current inability of the NUMODIS code to handle a sufficiently high and representative number of defects, and thus to obtain the mechanical behavior of the grain (~10 microns).
The objective of the proposed work is to implement new algorithms to extend the functionalities of the code, propose and test new numerical algorithms, parallelize certain parts still processed sequentially, and ultimately demonstrate the code's ability to simulate the deformation channeling mechanism in an irradiated zirconium grain.
The work will focus primarily on algorithms for calculating velocities, junction formation, and time integration, requiring both mastery of dislocation physics and the corresponding numerical methods. Algorithms for integration recently proposed by Stanford University and LLNL will be implemented and tested for this purpose.
Significant work will also be devoted to adapting the current code (hybrid MPI-OpenMP parallelism) to new computing machines that favor GPU processors, through the adoption of the Kokkos programming model.
Building on both previous experimental and numerical work, this study will conclude with the demonstration of NUMODIS's ability to simulate the channeling mechanism in an irradiated zirconium grain and to identify or even model the main physical and mechanical parameters involved.
At the interface between several fields, the candidate must have a good foundation in physics and/or mechanics, while being comfortable with programming and numerical analysis.
References:
1. Etcheverry Arnaud, Simulation de la dynamique des dislocations à très grande échelle, Université Bordeaux I (2015).
2. Blanchard, Pierre, Algorithmes hiérarchiques rapides pour l’approximation de rang faible des matrices, applications à la physique des matériaux, la géostatistique et l’analyse de données, Université Bordeaux I (2017).
3. Durocher, Arnaud, Simulations massives de dynamique des dislocations : fiabilité et performances sur architectures parallèles et distribuées (2018).
4. Drouet, Julie, Étude expérimentale et modélisation numérique du comportement plastique
des alliages de zirconium sous et après irradiation (2014).
5. Gaumé, Marine, Étude des mécanismes de déformation des alliages de zirconium
après et sous irradiation (2017).
6. Noirot, Pascal, Etude expérimentale et simulation numérique, à l'échelle nanométrique et en temps réel, des mécanismes de déformation des alliages de zirconium après irradiation (2025).
Detailed Numerical investigations on highly-concentrated bubbly flows
To assess the safety of industrial facilities, the CEA develops, validates, and uses thermohydraulic simulation tools. Its research focuses on modelling two-phase flows using various approaches, from the most detailed to the largest system-scale. In order to better understand two-phase flows, Service of Thermal-hydraulic and Fluid Mechanics (STMF) is working on implementing a multi-scale approach in which high-fidelity simulations (DNS, Direct Numerical Simulation of two-phase flows) are used as “numerical experiments” to produce reference data. This data is then averaged to be compared with models used on a larger scale. This approach is applied to high-pressure flows where the bubbly flow regime is maintained even at very high void fractions. The Laboratory of Development at Local Scales (LDEL) belonging to STMF has developed a DNS method (Front-Tracking) implemented in its open-source thermo-hydraulics code: TRUST/TrioCFD [1] (object-oriented code, C++). In several PhDs, it has been used to perform massively parallel simulations to describe interfaces in detail without resorting to models, for example in groups of bubbles (called swarms) [2][3][4].
Currently applied to low-concentration two-phase bubbly flows (volume fraction less than 12%), the objective of this thesis will be to evaluate and use the method at higher void fractions. Reference HPC simulations of bubble swarms will be conducted on national supercomputers up to gas fractions of 40%. The quality of the results will be evaluated before extracting physical models of bubble interactions under these conditions. The objective of these models is to recover the overall dynamics of the bubble swarm at much lower resolutions, thereby enabling the study of larger systems in disequilibrium (external forcing of imposed turbulence generation, imposed average velocity gradient, etc.).
This work is funded by the French ANR, in collaboration with IMFT and LMFL, in parallel with two other theses with which there will be strong interactions. It will be performed at CEA-Saclay, in the STMF/LDEL laboratory. It includes numerical aspects (validation), computer developments (C++), and a physical analysis of the flows obtained.
GPU-ACCELERATED CHARACTERISTICS METHOD FOR 3D NEUTRON TRANSPORT COMBINING THE LINEAR-SURFACE METHOD AND THE AXIAL POLYNOMIAL EXPANSION
This thesis falls within the framework of advancing numerical computation techniques for reactor physics. Specifically, it focuses on the implementation of methods that incorporate higher-order spatial expansions for neutron flux and cross-sections. The primary objective is to accelerate both existing algorithms and those that will be developed through GPU programming. By harnessing the computational power of GPUs, this research aims to enhance the efficiency and accuracy of simulations in reactor physics, thereby contributing to the broader field of nuclear engineering and safety.
Simulation of nuclear glass gels at the mesoscopic scale using a quaternary system.
This research work is part of studies conducted on the long-term behavior of nuclear glass used to immobilize radioactive waste and potentially intended for geological disposal. The challenge lies in understanding the mechanisms of alteration and gel formation (a passivating layer that can slow down the rate of glass alteration) by water and in predicting the kinetics of radionuclide release over the long term.
The proposed simulation approach aims to predict, at a mesoscopic scale, the maturation process of the gel formed during the alteration of glass by water using a ternary “phase field model” composed of silicon, boron, and water (leachate), to which aluminum will be added.
The underlying quaternary mathematical model will consists of a set of coupled nonlinear partial differential equations. These are based on Allen-Cahn and transport equations. The numerical solution of the associated equations is performed using the Lattice Boltzmann Method (LBM) programmed in C++ in the massively parallel LBM_saclay calculation code, which runs on several HPC architectures, both multi-CPUs and multi-GPUs.
The proposed research requires a solid foundation in applied mathematics and programming in order to develop the algorithms necessary for the correct resolution of the new system of strongly coupled equations.
HPC two-phase simulations with lattice Boltzmann methods and adaptative mesh refinement
CEA/STMF develops computational fluid dynamics (CFD) codes in thermohydraulics that aim to quantify mass and energy transfers in nuclear cycle systems such as reactors and management devices of radioactive wastes. This thesis focuses on Lattice Boltzmann Methods (LBM) adapted to Adaptive Mesh Refinement (AMR) inside a generic computing environment based on Kokkos and executable on multi-GPU supercomputers. The proposed work consists in developing LB methods in the Kalypsso-lbm code to simulate coupled partial differential equations (PDEs) modelling incompressible two-phase and multi-component flows such as those encountered in downstream cycle devices. Once the developments have been completed, they will be validated with reference solutions. They will allow a comparison of various interpolation methods between blocks of different sizes in the AMR mesh. A discussion will be held on the refinement and de-refinement criteria that will be generalized for these new PDEs. Finally, benchamrks of performance will quantify the contribution of AMR for 3D simulations when the reference simulation is performed on a static and uniform mesh. This work will use supercomputers which are already operational (e.g., Topaze-A100 from CEA-CCRT), as well as the future exascale supercomputer Alice Recoque depending on the progress of its installation.
Novel architecture and signal processing for mobile optical telecommunications
Free-Space Optical Communications (FSO) rely on transmitting data via light between two distant points, eliminating the need for fibers or cables. This approach is particularly valuable when wired connections are impractical or prohibitively expensive.
However, these links are highly susceptible to atmospheric conditions—fog, rain, dust, and thermal turbulence—which attenuate or distort the light beam, significantly degrading communication quality. Current solutions remain costly and limited, both in terms of optical compensation hardware and signal processing algorithms.
Within this framework, the thesis aims to design high-performance, robust mobile optical links capable of adapting to dynamic and disturbed environments. The study will focus on leveraging Silicon-based Optical Phased Arrays (OPAs)—a technology derived from low-cost LiDAR systems—offering a promising path toward compact, integrated, and cost-effective architectures.
The primary focus of the research will be developing advanced algorithmic approaches for signal processing and compensation. The PhD candidate will be tasked with designing a dedicated simulation environment to evaluate and validate architectural choices and algorithmic strategies before practical experimentation.
The overarching goal is to propose an integrated, flexible, and reliable architecture that ensures uninterrupted optical communication in motion, with potential applications in aerospace, space, and terrestrial domains.
Modeling the impact of defects in Steel–Concrete Structures. Identification of critical defects through metamodeling and optimization algorithms
To meet growing constructability challenges, steel–concrete (SC) structures are emerging as a promising alternative to conventional reinforced concrete structures. These elements are composed of infill concrete, two external steel plates, and steel shear studs that ensure composite action. While such structures present a clear interest due to their overall mechanical behavior, the presence of the steel plates prevents visual inspection of the concrete casting quality. It is therefore essential to characterize the impact of possible defects. This is the context of the proposed PhD research. Building upon recent results obtained in the laboratory, the goal is to develop a numerical framework to account for defects in steel–concrete structures. The thesis will be structured in several stages: validation of a modeling strategy for the mechanical behavior of defect-free SC structures, introduction of defects in the simulations and assessment of the applicability of the numerical approach, development of a metamodel and sensitivity analysis, and identification of critical defect configurations through optimization algorithms. One of the operational objectives of this doctoral work is to provide a tool capable of identifying critical defect configurations (size, position, and number) with respect to a given target quantity of interest (such as loss of strength or reduction in average stiffness). The research will therefore rely on the use and further development of state-of-the-art numerical tools in the fields of finite element modeling, optimization techniques, sensitivity analysis, and metamodeling. The thesis will be carried out within a rich collaborative environment, notably in partnership with EDF.
Artificial Intelligence for the Modeling and Topographic Analysis of Electronic Chips
The inspection of wafer surfaces is critical in microelectronics to detect defects affecting chip quality. Traditional methods, based on physical models, are limited in accuracy and computational efficiency. This thesis proposes using artificial intelligence (AI) to characterize and model wafer topography, leveraging optical interferometry techniques and advanced AI models.
The goal is to develop AI algorithms capable of predicting topographical defects (erosion, dishing) with high precision, using architectures such as convolutional neural networks (CNN), generative models, or hybrid approaches. The work will include optimizing models for fast inference and robust generalization while reducing manufacturing costs.
This project aligns with efforts to improve microfabrication processes, with potential applications in the semiconductor industry. The expected results will contribute to a better understanding of surface defects and the optimization of production processes.
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.
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.