Mass transfers and hydrodynamic coupling: experimental investigation and models validation and calibration
With the energy transition and the paramount importance of the nuclear energy in this context, it is pivotal to understand the consequences of potential accident with core meltdown, as well as thinking about mitigation strategy.
During a nuclear severe accident with core meltdown a magma called corium can form a pool in the reactor lower head. The pool is not homogeneous and can stratify into multiple immiscible layers. The composition of the pool may evolve in time, due to progressive material assimilation. With the evolution of the global composition of the corium, the properties of the layers evolve. The vertical position of these layer may then change. This change comes with the creation of droplets from a layer which then cross the other one. The vertical order of the different layers as well as their movements have a significant impact on the heat fluxes imposed on the reactor vessel. A better understanding of these phenomena improves safety of both nowadays and future nuclear reactors.
Modelling work has been done, but it lacks validation and need calibration. Prototypical experiments (with actual materials present inside a reactor) are difficult to carry and are not foreseen in the near future. This PhD aims at experimentally studying the mass transfer between a droplet and its surrounding as well as the droplet creation. The planned experimental setup will use a water-based system which allow for local measurement. The goal is to validate, calibrate the existing model, and potentially create new ones. The final goal being to capitalize the work into PROCOR software platform. The experimental setup will be constructed and operated in LEMTA laboratory in University of Lorraine, where the student will work.
The PhD work will be mainly experimental but will also require software use for calibration, validation and for the design of the experimental setup. This work will be conducted in close collaboration between the laboratories LMAG in CEA/IRESNE (Cadarache) and LEMTA in University of Lorraine (Nancy). The student will work in LEMTA, where the experiments will be conducted, while being part of the CEA. The student will benefit from LEMTA’s expertise in building of experimental setup, transport phenomena in fluids and metrology, and from LMAG’s expertise in mass transfer, physical modeling and simulation in the scope of nuclear severe accidents. The student will regularly interact with CEA team which will follow the work closely. The student will therefore have to regularly go to CEA Cadarache.
The PhD student will be integrated to a dynamic environment comprised of researchers and other PhD students. The PhD candidate needs to be knowledgeable in transport phenomena, and needs to have a taste for experimental sciences.
New concepts for cold neutron reflectors
The CEA and the CNRS have launched an initiative to design a new neutron source using low-energy proton accelerators, the ICONE project [1]. The goal is to build a facility that will provide an instrumental suite of about ten spectrometers available to the French and European scientific community. Alongside ICONE, the LLB is also participating in HiCANS R&D work on the construction of a platform in Bilbao to facilitate European collaborations.Neutron scattering experiments require thermal and cold neutrons. The design of the moderator is therefore a crucial component of the project to maximize the source's performance.
One avenue for improving the moderator performances is to enhance the efficiency of the reflector, and more specifically, the cold neutron reflector. In this study, we propose to investigate the specific scattering properties of cold neutrons on nanostructured materials. Indeed, cold neutrons have long wavelengths (> 0.4 nm) and can therefore be coherently scattered by nanostructured materials. Scattering efficiency is not only amplified by coherent scattering effects, but it is potentially possible to direct this scattering if the reflecting material is anisotropic. This control over the scattering direction can further increase the moderator's brightness.
The first part of the work will consist of identifying the most promising nanostructured materials and modeling their cold neutron reflectivity performance. In a second step, these materials will be shaped and their properties characterized using neutron scattering devices at neutron scattering facilities such as the ILL in Grenoble or the PSI in Switzerland.
CONTEXT: strain - texture neutron instrumentation for ICONE
The CEA and the CNRS have launched an initiative to design a new neutron source using low-energy proton accelerators, the ICONE project. The objective is to build a facility that will offer an instrumental suite of about ten spectrometers available to the French and European scientific community. The project is currently in the Preliminary Design phase, with the aim of refining as much as possible all technical aspects.
We are proposing a PhD thesis on the modeling and development of a new neutron scattering spectrometer for measuring textures and stresses in materials. This technique makes it possible to probe residual stresses in materials after machining, heat treatment, and/or use, and to measure the crystallographic anisotropy of alloys to exploit the induced mechanical properties.
Part of the work will take advantage of the start-up of the DREAM and MAGIC spectrometers at ESS in Sweden, in which the LLB participated in the construction, so that the candidate can become familiar with time-of-flight neutron scattering techniques (measurements and data analysis).
In the second part of this work, we propose to implement statistical modulation techniques for the construction of an instrument, CONTEXT, on ICONE, which will allow to best exploit the potential of ICONE's long pulses. The objective will be to create a digital twin of the future instrument using various Monte Carlo simulation tools.
Assisted generation of complex computational kernels in solid mechanics
The behavior laws used in numerical simulations describe the physical characteristics of simulated materials. As our understanding of these materials evolves, the complexity of these laws increases. Integrating these laws is a critical step for the performance and robustness of scientific computations. Therefore, this step can lead to intrusive and complex developments in the code.
Many digital platforms, such as FEniCS, FireDrake, FreeFEM, and Comsol, offer Just-In-Time (JIT) code generation techniques to handle various physics. This JIT approach significantly reduces the time required to implement new simulations, providing great versatility to the user. Additionally, it allows for optimization specific to the cases being treated and facilitates porting to various architectures (CPU or GPU). Finally, this approach hides implementation details; any changes in these details are invisible to the user and absorbed by the code generation layer.
However, these techniques are generally limited to the assembly steps of the linear systems to be solved and do not include the crucial step of integrating behavior laws.
Inspired by the successful experience of the open-source project mgis.fenics [1], this thesis aims to develop a Just-In-Time code generation solution dedicated to the next-generation structural mechanics code Manta [2], developed by CEA. The objective is to enable strong coupling with behavior laws generated by MFront [3], thereby improving the flexibility, performance, and robustness of numerical simulations.
The selected PhD candidate should have a solid background in computational science and a strong interest in numerical simulation and C++ programming. They should be capable of working independently and demonstrate initiative. The doctoral student will benefit from guidance from the developers of MFront and Manta (CEA), as well as the developers of the A-Set code (a collaboration between Mines-Paris Tech, Onera, and Safran). This collaboration within a multidisciplinary team will provide a stimulating and enriching environment for the candidate.
Furthermore, the thesis work will be enhanced by the opportunity to participate in conferences and publish articles in peer-reviewed scientific journals, offering national and international visibility to the thesis results.
The PhD will take place at CEA Cadarache, in south-eastern France, in the Nuclear Fuel Studies Department of the Institute for Research on Nuclear Systems for Low-Carbon Energy Production (IRESNE)[4]. The host laboratory is the LMPC, whose role is to contribute to the development of the physical components of the PLEIADES digital platform [5], co-developed by CEA and EDF.
[1] https://thelfer.github.io/mgis/web/mgis_fenics.html
[2] MANTA : un code HPC généraliste pour la simulation de problèmes complexes en mécanique. https://hal.science/hal-03688160
[3] https://thelfer.github.io/tfel/web/index.html
[4] https://www.cea.fr/energies/iresne/Pages/Accueil.aspx
[5] PLEIADES: A numerical framework dedicated to the multiphysics and multiscale nuclear fuel behavior simulation https://www.sciencedirect.com/science/article/pii/S0306454924002408
Multiphysic modeling of sintering of nuclear fuel pellet: effect of atmosphere on shrinkage kinetics
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. Grain growth induce material densification and macroscopic shrinkage of the pellet. If the green pellet (powder obtained by pressing, manufacturing step before sintering) admit a highly heterogeneous density, this gradient leading to differential shrinkage and the appearance of defects. Furthermore, the sintering atmosphere, i.e., the gas composition in the furnace, impacts grain growth kinetics and thus the shrinkage of the pellet. Advanced simulation is the key to improving understanding of the mechanisms observed as well as optimizing manufacturing cycles.
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.
Understanding the signals emitted by moving liquids
Elasticity is one of the oldest physical properties of condensed matter. It is expressed by a constant of proportionality G between the applied stress (s) and the deformation (?): s = G.? (Hooke's law). The absence of resistance to shear deformation (G' = 0) indicates liquid-like behavior (Maxwell model). Long considered specific to solids, shear elasticity has recently been identified in liquids at the submillimeter scale [1].
The identification of liquid shear elasticity (non-zero G') is a promise of discoveries of new liquid properties. For example, do we know that a confined liquid changes temperature under flow? Yet no classical model (Poiseuille, Navier-Stokes, Maxwell) predicts the effect because without long-range correlation between molecules (i.e. without elasticity), the flow is dissipative, therefore athermal. For a change in temperature to be flow induced (without a heat source), the liquid must have elasticity and this elasticity must be stressed [1,2].
The PhD thesis will explore how the mechanical energy of the flow is converted in a thermal response [2]. We will exploit the capacity of conversion to develop a new generation of microfluidic devices (patent FR2206312).
We will also explore the impact of the wetting on the liquid flow, and reciprocally, we will examine how the liquid flow modifies the solid dynamics (THz) of the substrate [3]. Powerful methods only available in Very Large Research Facilities such as the ILL will be used to probe the non-equilibrium state of solid phonons. Finally, we will strengthen our existing collaborations with theoreticians.
The PhD topic is related to wetting, macroscopic thermal effects, phonon dynamics and liquid transport.
1. A. Zaccone, K. Trachenko, “Explaining the low-frequency shear elasticity of confined liquids" PNAS, 117 (2020) 19653–19655. Doi:10.1073/pnas.2010787117
2. E. Kume, P. Baroni, L. Noirez, “Strain-induced violation of temperature uniformity in mesoscale liquids” Sci. Rep. 10 13340 (2020). Doi : 10.1038/s41598-020-69404-1.
3. M. Warburton, J. Ablett, P. Baroni, JP Rueff, L. Paolasini, L. Noirez, “Identification by Inelastic X-Ray scattering of bulk alteration of solid dynamics due to Liquid Wetting”, J. of Molecular Liquids 391 (2023) 123342202
Machine Learning-Based Algorithms for Real-Time Standalone Tracking in the Upstream Pixel Detector at LHCb
This PhD aims to develop and optimize next-generation track reconstruction capabilities for the LHCb experiment at the Large Hadron Collider (LHC) through the exploration of advanced machine learning (ML) algorithms. The newly installed Upstream Pixel (UP) detector, located upstream of the LHCb magnet, will play a crucial role from Run 5 onward by rapidly identifying track candidates and reducing fake tracks at the earliest stages of reconstruction, particularly in high-occupancy environments.
Achieving fast and highly efficient tracking is essential to fulfill LHCb’s rich physics program, which spans rare decays, CP-violation studies in the Standard Model, and the characterization of the quark–gluon plasma in nucleus–nucleus collisions. However, the increasing event rates and data complexity expected for future data-taking phases will impose major constraints on current tracking algorithms, especially in heavy-ion collisions where thousands of charged particles may be produced per event.
In this context, we will investigate modern ML-based approaches for standalone tracking in the UP detector. Successful applications in the LHCb VELO tracking system already demonstrate the potential of such methods. In particular, Graph Neural Networks (GNNs) are a promising solution for exploiting the geometric correlations between detector hits, allowing for improved tracking efficiency and fake-rate suppression, while maintaining scalability at high multiplicity.
The PhD program will first focus on the development of a realistic GEANT4 simulation of the UP detector to generate ML-suitable datasets and study detector performance. The next phase will consist in designing, training, and benchmarking advanced ML algorithms for standalone tracking, followed by their optimization for real-time GPU-based execution within the Allen trigger and reconstruction framework. The most efficient solutions will be integrated and validated inside the official LHCb software stack, ensuring compatibility with existing data pipelines and direct applicability to Run-5 operation.
Overall, the thesis will provide a major contribution to the real-time reconstruction performance of LHCb, preparing the experiment for the challenges of future high-luminosity and heavy-ion running.
Surface technologies for enhanced superconducting Qubits lifetimes
Materials imperfections in superconducting quantum circuits—in particular, two-level-system (TLS) defects—are a major source of decoherence, ultimately limiting the performance of qubits. Thus, identifying the microscopic origin of possible TLS defects in these devices and developing strategies to eliminate them is key to superconducting qubit performance improvement. This project proposes an original approach that combines the passivation of the superconductor’s surface with films deposited by Atomic Layer Deposition (ALD), which inherently have lower densities of TLS defects, and thermal treatments designed to dissolve the initially present native oxides. These passivating layers will be tested on 3D Nb resonators than implemented in 2D resonators and Qubits and tested to measure their coherence time. The project will also perform systematic material studies with complementary characterization techniques in order to correlate improvements in qubit performances with the chemical and crystalline alteration of the surface.
Euclid Weak Lensing Cluster Cosmology inference
Galaxy clusters, which form at the intersection of matter filaments, are excellent tracers of the large-scale matter distribution in the Universe and are a valuable source of information for cosmology.
The sensitivity of the Euclid space mission (launch in 2023) allow blind detection of galaxy clusters through gravitational lensing (i.e. directly linked to the projected total mass). Combined with its wide survey area (14,000 deg²), Euclid should allow the construction of a galaxy cluster catalogue that is unique in both its size and selection properties.
In contrast to existing cluster catalogues, which are typically based on baryonic content (e.g., X-ray emission from intra-cluster gas, the Sunyaev-Zel’dovich effect in the millimeter regime, or optical emission from galaxies), a catalogue derived from gravitational lensing is directly sensitive to the total mass of the clusters. This makes it truly representative of the underlying cluster population, a significant advantage for both galaxy cluster studies and cosmology.
In this context, we have developed a multi-scale detection method specifically designed to identify galaxy clusters based only on their gravitational lensing signal, which has been pre-selected to produce the Euclid cluster catalogue.
The goal of this PhD project is to build and characterize the galaxy cluster catalogue identified via weak lensing in the data collected during the first year of Euclid observations (DR1), based on this detection method. The candidate will derive cosmological constraints from the modelling of the cluster abundance, using the classical Bayesian framework, and will also investigate the potential of Simulation-Based Inference (SBI) methods for cosmological inference.
Bayesian Neural Inference Using Ferroelectric Memory Transistors
An increasing number of safety-critical systems now rely on artificial intelligence functions that must operate under strict energy constraints and in environments characterized by data scarcity and high uncertainty. However, conventional deterministic AI approaches provide only point estimates and lack principled uncertainty quantification, which can lead to unreliable or unsafe decisions in real-world deployment.
This PhD is positioned within the emerging field of Bayesian electronics, which aims to implement probabilistic inference directly in hardware by leveraging the intrinsic stochasticity of nanoscale devices to represent and manipulate probability distributions. While memristive devices have previously been explored for Bayesian inference, their limited endurance and high programming energy remain critical bottlenecks for on-chip learning.
The objective of this thesis is to investigate ferroelectric field-effect memory transistors (FeMFETs) as building blocks for hardware Bayesian neural networks. The work will involve characterizing and modeling the exploitable ferroelectric randomness for sampling and probabilistic weight updates, designing Bayesian neuron and synapse architectures based on FeMFETs, and evaluating their robustness, energy efficiency, and system-level performance for safety-critical inference under uncertainty.