Accelerating thermo-mechanical simulations using Neural Networks --- Applications to additive manufacturing and metal forming
In multiple industries, such as metal forming and additive manufacturing, the discrepancy between the desired shape and the shape really obtained is significant, which hinders the development of these manufacturing techniques. This is largely due to the complexity of the thermal and mechanical processes involved, resulting in a high computational simulation time.
The aim of this PhD is to significantly reduce this gap by accelerating thermo-mechanical finite element simulations, particularly through the design of a tailored neural network architecture, leveraging theoretical physical knowledge.
To achieve this, the thesis will benefit from a favorable ecosystem at both the LMS of École Polytechnique and CEA List: internally developed PlastiNN architecture (patent pending), existing mechanical databases, FactoryIA supercomputer, DGX systems, and 3D printing machines. The first step will be to extent the databases already generated from finite element simulations to the thermo-mechanical framework, then adapt the internally developed PlastiNN architecture to these simulations, and finally implement them.
The ultimate goal of the PhD is to demonstrate the acceleration of finite element simulations on real cases: firstly, through the implementation of feedback during metal printing via temperature field measurement to reduce the gap between the desired and manufactured geometry, and secondly, through the development of a forging control tool that achieves the desired geometry from an initial geometry. Both applications will rely on an optimization procedure made feasible by the acceleration of thermo-mechanical simulations.
Study and simulation of phase entrainment in mixer-settler batteries
As part of the development of new liquid-liquid extraction separation processes, experimental tests are implemented to demonstrate the recovery of valuable elements sufficiently decontaminated from impurities. These tests are commonly carried out in mixer-settler batteries. However, depending on the operating conditions, these finished products may be contaminated by impurities. This contamination results from the combination of several factors:
-Hydrodynamic: Entrainment in the solvent of non-decanted aqueous drops containing impurities
-Chemical: the impurity separation factor is low (less than 10-3)
-Process: the entrainment of drops is amplified with the increase in the rate (reduction of the residence time of the drops)
This thesis aims to increase the understanding of the different phenomena responsible for these phase entrainments in order to estimate optimal operating parameters and to guarantee a contamination of the finished products below a fixed threshold. The aim will be to develop a macroscopic model to predict the flow rate of non-decanted droplets as a function of the operating conditions in the mixer-settler batteries. It will have to be based on hydrodynamic simulations coupling the resolution of a droplet population balance to a continuous phase flow. A coupling will be carried out between this hydrodynamic model and the PAREX or PAREX+ code to size the process diagrams. The qualification of the proposed models will have to be done by comparisons with experimental measurements (based on previous or future test campaigns).
Monte Carlo methods for sensitivity to geometry parameters in reactor physics
The Monte Carlo method is considered to be the most accurate approach for simulating neutron transport in a reactor core, since it requires no or very few approximations and can easily handle complex geometric shapes (no discretisation is involved). A particular challenge for Monte Carlo simulation in reactor physics applications is to calculate the impact of a small model change: formally, this involves calculating the derivative of an observable with respect to a given parameter. In a Monte-Carlo code, the statistical uncertainty is considerably amplified when calculating a difference between similar values. Consequently, several Monte Carlo techniques have been developed to estimate perturbations directly. However, the question of calculating perturbations induced by a change in reactor geometry remains fundamentally an open problem. The aim of this thesis is to investigate the advantages and shortcomings of existing geometric perturbation methods and to propose new ways of calculating the derivatives of reactor parameters with respect to changes in its geometry. The challenge is twofold. Firstly, it will be necessary to design algorithms that can efficiently calculate the geometric perturbation itself. Secondly, the proposed approaches will have to be adapted to high-performance computing environments.
Influence of delayed neutron precursors losses resulting from fission gas evacuation on molten salt reactors dynamics
Over the past twenty years, molten salt reactors (MSRs) have been the focus of renewed interest in the international nuclear community (national programs, start-ups, including one from the CEA). Modern MSR concepts feature a system for evacuating fission gases, which accumulate in the expansion tank. Some of these gases will consist of radionuclides that are delayed neutron precursors, which will therefore be lost for the fission chain reaction. This should further reduce the effective fraction of delayed neutrons in these reactors, already reduced by the circulation of the fuel salt outside the critical zone. The aim of this thesis is to assess the extent of this reduction, and its influence on reactor dynamics.
Such an assessment may involve numerical simulations that take into account 1) a differentiation of delayed neutron precursor groups into “liquid phase groups” and “gas phase groups”, and 2) two-phase flow models (where each type of group joins its corresponding phase). In order to differentiate the groups, we need to evaluate the “liquid” and “gas” fractions for each of them, based for example on the branching ratios of the nuclear evaluations and knowledge of the chemical elements joining each of the phases. Once this has been done, simulations can be carried out with the CATHARE “system” code (already able to use two-phase models) and the TRUST-NK “core” code (whose two-phase calculation functions may require further development) to assess the influence of precursor loss on reactor dynamics.
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.
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.
Understanding and Modeling Laser Cutting Mechanisms for Dismantling
For over 30 years, the Assembly Technologies Laboratory (LTA) at CEA Saclay has been conducting research to develop innovative tools for the dismantling of nuclear facilities, by designing laser cutting processes to work in hostile environments. This technology is suitable to cut thick materials, either in air or underwater, and has proven particularly effective for dismantling operations due to its precision and ability to limit aerosol generation. Today, this technology is considered safe and reliable, thanks to the efforts achieved through the European project "LD-SAFE".
However, technical challenges remain, particularly the management of residual laser energy, which, by propagating beyond the cut piece, can damage surrounding structures.
Initial studies, including a PhD thesis, have made it possible to develop numerical models to predict and control this energy, yielding significant advancements. Nevertheless, technological challenges remain, such as handling thicker materials (>10 mm), cutting multi-plate configurations, and considering the addition of oxygen to improve cutting efficiency.
The objective of the PhD is to address these challenges and to gain a better understanding of the laser cutting process and the propagation of residual laser energy. The doctoral student will refine the numerical model to predict its impact on background structures, particularly for thick materials and multi-plate configurations. The work will include the development of a multiphysics model, validated by experiments, with a particular focus on the effect of oxygen, the creation of simplified models, and adaptation for use by operators.
The PhD will be conducted in collaboration between the Assembly Technologies Laboratory (LTA) at CEA Saclay and the Dupuy de Lôme Research Institute (IRDL - UMR CNRS 6027) at the University of South Brittany (Lorient).
Measurement and evaluation of the energy dependence of delayed neutron data from 239Pu
This PhD proposal aims to measure and characterize the delayed neutron emissions from the fission of 239Pu. This actinide is involved in various reactor concepts, and the nuclear data available remains insufficient, particularly with fast neutrons. The project has a strong experimental focus, with multiple measurement campaigns at the MONNET electrostatic accelerator from JRC Geel, in which the candidate will actively participate.
The first phase focuses on the intercomparison of the neutron flux measurement methods (dosimetry, fission chamber, long-counter detector and recoil proton scintillator) which will be confronted to Monte-Carlo simulations of neutron emission from charged particle interactions (D+T, D+D, p+T). This work will ensure proper neutron flux characterization, a crucial step for the project.
Next, the candidate will replicate the delayed neutron measurements for ²³8U using an existing target in order to verify the results from a 2023 experimental campaign.
Finally, the candidate will measure the delayed neutron yields and group abundances for ²³?Pu in a neutron energy range from 1 to 8 MeV. The objective is to produce an energy-dependent evaluation, integrated into an ENDF file, to be tested on reactor calculations (beta-eff, power transients, absorber efficiency calibration, etc.). These measurements will complement a thermal spectrum study conducted at ILL in 2022, forming a coherent model for ²³?Pu from 0 to 8 MeV.
This project will contribute to the OECD/NEA's JEFF-4 nuclear data file, addressing a strong demand from the nuclear industry (highlighted by the IAEA) to improve the precision of multiplicity measurements and delayed neutron kinetic parameters, thus enhancing reactor safety and reducing safety margins.