Deterministic neutron calculation of soluble-boron-free PWR-SMR reactors based on Artificial Intelligence
In response to climate challenges, the quest for clean and reliable energy focuses on the development of small modular reactors using pressurized water (PW-SMR), with a power range of 50 to 1000 MWth. These reactors aimed at decarbonizing electricity and heat production in the coming decade. Compared to currently operating reactors, their smaller size can simplify design by eliminating the need for soluble boron in the primary circuit water. Consequently, control primarily relies on the level of insertion of control rods, which disturb the spatial power distribution when control rods are inserted, implying that power peaks and reactivity are more difficult to manage than in a standard PWR piloted with soluble boron. Accurately estimating these parameters poses significant challenges in neutron modeling, particularly regarding the effects of the history of control rod insertion on the isotopic evolution of the fuel. A thesis completed in 2022 explored these effects using an analytical neutron model, but limitations persist as neutron absorbers movements are not the only phenomena influencing the neutron spectrum. The proposed thesis seeks to develop an alternative method that enhances robustness and further reduces the calculation biases. A sensitivity analysis will be conducted to identify key parameters, enabling the creation of a meta-model using artificial intelligence to correct biases in existing models. This work, conducted in collaboration with IRSN and CEA, will provide expertise in reactor physics, numerical simulations, and machine learning.
AI based prediction of solubilities for hydrometallurgy applications
Finding a selective and efficient extractant is one of the main challenges of hydrometallurgy. A comprehensive screening is impossible by the synthesis/test method due to the high number of possible molecules. Instead, more and more studies use quantum calculations to evaluate the complexes stabilities. Still, some important parameters such as solubility are lacking in this model.
This project thus aims to develop an AI based tool that provides solubility values from the molecular structure of any ligand. The study will first focus on 3 solvants: water, used as a reference as AI tools already exist, 3 M nitric acid to mimic nuclear industry applications and n-octanol, organic solvent used to measure the partition coefficient logP. The methodology follows 4 steps:
1) Bibliography on existing AI tools for solubility prediction yielding the choice of the most promising method(s)
2) Bibliography on existing databases to be complemented by the student's in-lab solubility experiments
3) Code generation and training of the neural network on the step 2 databases
4) Checking the accuracy of the predictions on molecules not included in the databases by comparing the calculated results with in-lab experiments
Hydrogen and ammonia combustion within porous media: experiments and modelling
- Context
Current energy prospects suggest the use of hydrogen (H2) and ammonia (NH3) as carbon-free energy carriers to achieve neutrality by 2050. NH3 offers advantages like high energy density and safe storage but faces combustion challenges such as narrow flammability and high NOx emissions. Interestingly, some H2 can be obtained by partial cracking of NH3 to create blends of more favourable combustion properties, with open questions regarding pollutant emissions and unburnt NH3 content.
- Challenges
Porous burners show promise for safe and low-pollutant combustion of NH3/H2 blends. However, material durability issues and the complexity of flame stabilization pose significant hurdles. Fortunately, recent advances in additive manufacturing enable the precise tailoring of porous matrices, but the experimental characterization remains difficult due to the opacity of the solid matrix.
- Research objectives
The PhD candidate will operate an experimental bench at CEA Saclay to conduct combustion experiments with NH3/H2/N2+air mixtures in various porous burners. Key tasks will include designing new burner geometries, comparing experimental results with numerical simulations, and advancing the modelling of porous burners using 1D Volume-Averaged Models and asymptotic theory. Experimental measurements will include hotwire anemometry, infrared thermometry, output gas composition analysis, chemiluminescence, and laser diagnostics. The porous burners will be manufactured using 3D printing techniques with materials such as stainless steel, inconel, alumina, zirconia, and silicon carbide.
The research aims to develop more robust and efficient porous burners for NH3/H2 combustion, enhancing their practical application in achieving carbon neutrality. The candidate will contribute to advancing the field through experimental data, innovative designs, and improved modelling techniques.
Study of of the thermodynamic of K2CO3-CO2-H2O for the development of NET and SAF technologies
.Bioenergy with Carbon Capture and Storage (BECCS) uses biomass energy while capturing the carbon dioxide released by the process, resulting in negative emissions into the atmosphere. The reference process in Europe uses potassium carbonate but at atmospheric pressure [1], whereas its sequestration or hydrogenation into sustainable molecules requires high pressures.
The thesis consists in acquiring new thermodynamic and thermo-chemical data at high temperature/pressure [2] required for the energy optimization of such a process, and integrating them into a thermodynamic model.
The overall process will then be reassembled in order to quantify the expected energy gain.
The thesis will be carried out at the Thermodynamic Modeling and Thermochemistry Laboratory (LM2T), in collaboration with LC2R (DRMP/SPC) for the experimental part.
References :
[1]K. Gustafsson, R. Sadegh-Vaziri, S. Grönkvist, F. Levihn et C. Sundberg, «BECCS with combined heat and power: assessing the energy penalty,» Int. J. Greenhouse Gas Control, vol. 110, p. 103434, 2021.
[2] S. Zhang, X. Ye et Y. Lu, «Development of a Potassium Carbonate-based Absorption Process with Crystallization-enabled High-pressure Stripping for CO2 Capture: Vapor–liquid Equilibrium Behavior and CO2 Stripping Performance of Carbonate/Bicarbonate,» Energy Procedia, 2014
Power and data transmission via an acoustic link for closed metallic environments
This thesis focuses on the transmission of power and data through metal walls using acoustic waves. Ultimately, this technology will be used to power, read and control systems located in areas enclosed in metal, such as pressure vessels, ship hulls and submarines.
Because electromagnetic waves are absorbed by metal, acoustic waves are needed to communicate data or power through metal walls. These are generated by piezoelectric transducers bonded to either side of the wall. The acoustic waves are poorly attenuated by the metal, resulting in numerous reflections and multiple paths.
The aim of the thesis will be to develop a robust demonstrator of this technology, enabling the remote powering and communication of acoustic data through metal walls. This work will be based on advanced modelling of the acoustic channel in order to optimise the performance of the power and data transmission device. It will also involve developing innovative electronic building blocks to determine and maintain an optimum power transmission frequency, impacted by environmental conditions and typically by temperature.
The goal of this thesis will be the development and implementation of a communication system embedded in an FPGA and/or microcontroller in order to send sensor data through a metal wall of variable thickness. The limitations due to the imperfections of the channel and the electronics will lead to the invention of a large number of compensation methods and systems in the digital and/or analogue domain. Work will also have to be carried out on the choice of piezoelectric transducers and the characterisation of the channel, in conjunction with the acoustic wave activities of the laboratory working on the transmission of acoustic power.
Contact : nicolas.garraud@cea.fr and esteban.cabanillas@cea.fr
Multiphysics modeling of fission gas behavior and microstructure evolution of nuclear fuels
The climate crisis demands urgent action and a rapid shift towards carbon-free technologies. This requires the development of advanced materials for more efficient electricity production and storage, including innovation in nuclear reactor fuels. To enhance the safety and efficiency of both current and future nuclear power plants, it is crucial to understand and predict fuel behavior under operating and accidental conditions.
A critical issue is related to fission gases produced upon nuclear fissions. These gases have low solubility and form small bubbles that grow from nanoscale to microscale during fuel operation, significantly impacting the fuel's overall properties. While experimental characterization is essential, numerical simulations complement this work by modeling bubble formation and growth, as well as the consequences in terms of changes in fuel properties. This approach is key to the design of next-generation, high-performance nuclear fuels.
This PhD project aims to advance simulation models for fission gas behavior within the polycrystalline structure of nuclear fuels, with a particular focus on uranium oxides. The PhD student will develop a physical model using the phase-field method, compute necessary input parameters, and conduct numerical simulations that replicate irradiation experiments performed in our department. Direct comparison between simulation results and experimental data will enable deeper insights into the underlying physics of gas behavior, including bubble formation, gas release, and fuel swelling. Additionally, this project will serve as validation for the INFERNO scientific code that will be used for these simulations on national supercomputers.
The research will be conducted at the Nuclear Fuel Department (DEC) of the IRESNE Institute(CEA-Cadarache), in collaboration with CEA fuel modeling and experimental characterization experts. The PhD student will have opportunities to share their findings through scientific publications and presentations at international conferences. Throughout the project, they will develop expertise in multiphysics modeling, numerical simulations, and scientific computing. These highly transferable skills will prepare them for a successful career in academic research, industrial R&D, or materials engineering.
References :
https://doi.org/10.1063/5.0105072
https://doi.org/10.1016/j.commatsci.2019.01.019
Modeling condensation and solidification of air gases on a cold wall: application to the simulation of the Loss of Vacuum of a liquid hydrogen tank
The increasingly widespread use of liquid hydrogen (LH2), particularly for low-carbon mobility, raises safety issues given its highly flammable nature. One of the major accidents involving cryogenic systems is the air ingress following a rupture of the outer shell of a vacuum-insulated tank. In such an event, the gases in the air liquefy and solidify on the cold walls, resulting in a high heat deposit and sudden system overpressure. The discharge line and the safety devices must be sized to evacuate the cryogenic fluid safely and avoid any risk of explosion. The aim of this thesis is to develop a model to simulate this type of scenario using the CATHARE code. A particular effort will be made to model heat exchange by liquefaction and solidification through the tank wall. This work will benefit from the loss of vacuum experimental campaign to be carried out in LH2 by CEA as part of the ESKHYMO ANR project. In addition, the use of a CFD local-scale simulation tool such as neptune_cfd could help in the construction of models in CATHARE by up-scaling. Finally, the methodology developed will be applied to simulate a system representative of an industrial facility.
Multiphysical modeling of a dual-frequency induction-heated metallothermic reactor
The recycling of uranium extracted from spent fuel (reprocessed uranium or URT) is of major strategic interest as regards both closure and economics of the cycle as well as for national sovereignty. France has initiated the development of a reprocessing route for this URT, involving an entire production chain relying on SILVA laser enrichment technology.
In this context, the CEA is in charge of developing all the processes in this chain, in particular the steps involved in the conversion of uranium oxide into uranium metal required for laser enrichment. For this purpose, the “Laboratoire d'étude des technologies Numériques et des Procédés Avancés” (LNPA) is studying the transposition of the historical metallothermy process to a cold crucible type reactor. This dual-frequency inductive furnace is designed to melt a two-phase charge consisting of a fluorinated slag and a metal produced in situ by the metallothermic reaction.
Alongside a multi-year technology development program on reduced-scale inactive pilot plants, numerical modeling studies of the reactor are undertaken in order to consolidate the change in working scale and enable system parameters to be optimized before deployment of the technology in active operation on depleted uranium for validation tests. The aim of the proposed thesis work is to develop the magneto-thermo-hydraulic (MTH) multiphysical model of the cold crucible metallothermic furnace.
Integrity, availability and confidentiality of embedded AI in post-training stages
With a strong context of regulation of AI at the European scale, several requirements have been proposed for the "cybersecurity of AI" and more particularly to increase the security of complex modern AI systems. Indeed, we are experience an impressive development of large models (so-called “Foundation” models) that are deployed at large-scale to be adapted to specific tasks in a wide variety of platforms and devices. Today, models are optimized to be deployed and even fine-tuned in constrained platforms (memory, energy, latency) such as smartphones and many connected devices (home, health, industry…).
However, considering the security of such AI systems is a complex process with multiple attack vectors against their integrity (fool predictions), availability (crash performance, add latency) and confidentiality (reverse engineering, privacy leakage).
In the past decade, the Adversarial Machine Learning and privacy-preserving machine learning communities have reached important milestones by characterizing attacks and proposing defense schemes. Essentially, these threats are focused on the training and the inference stages. However, new threats surface related to the use of pre-trained models, their unsecure deployment as well as their adaptation (fine-tuning).
Moreover, additional security issues concern the fact that the deployment and adaptation stages could be “on-device” processes, for instance with cross-device federated learning. In that context, models are compressed and optimized with state-of-the-art techniques (e.g., quantization, pruning, Low Rank Adaptation) for which their influence on the security needs to be assessed.
The objectives are:
(1) Propose threat models and risk analysis related to critical steps, typically model deployment and continuous training for the deployment and adaptation of large foundation models on embedded systems (e.g., advanced microcontroller with HW accelerator, SoC).
(2) Demonstrate and characterize attacks, with a focus on model-based poisoning.
(3) Propose and develop protection schemes and sound evaluation protocols.
Impact of the Pulse Width Modulation strategy on the semiconductor ageing
The Pulse Witdh Modulation strategy (PWM) is a fundamental technique in power electronics. It is used to control the Energy transfer by modifying the pulse width of the control signals in a power converter. In an automotive traction inverter, this PWM strategy applied to a transistor phase leg allows to convert the DC current from the battery to an AC current adapted to the motor windings. The impact of the PWM on the performances and the reliability of the engine have been widely studied in the litterature. However, the impact of the PWM strategy on the reliability and the ageing of the semiconductor devices inside the power modules has not been adressed. This is particularly true for the power modules intagrating wide bandgap semiconductors (eg: SiC) which are widely used for 10 years. The main objective of this thesis is to understand and model the impact of several PWM strategies on the ageing of SiC power semiconductor devices.
The thesis targets to define a link between the stress on the semicondcutor devices and the shift of its key parameters offering the possibility to define a PWM strategy able to maximize the long term performances and the lifetime of the power electronics system. By combining experimental and theroretical approaches, this thesis will contribute to improve the PWM strategies in power electronics systems.