The stabilized boiling in sodium is a subject that has been studied for many years at CEA in order to improve the validation of scientific calculation tools such as CATHARE3. Being able to reproduce properly this phenomena is a key safety related question for liquid metal liquid 4th generation reactors. When an unprotected loss of flow (ULOF) happens in the reactor and the safety measures are not deployed, the coolant can reach saturation, which can ultimately lead to a degradation of the subassembly. In order to avoid this situation, new fuel assembly designs provide negative neutronic feedback as the void fraction is generated. To understand how this void fraction evolves in the sub-assembly (within the rod bundle or the top plenum), the code requires a state of the art sodium modeling in terms of momentum, heat and mass transfer.
To improve the qualification of the CATHARE3 code for such situations, the doctoral student will implement CFD models allowing a better understanding of the boiling mechanisms in sodium-cooled subassemblies. New CFD models, such as large interface modelling, wall boiling, heat and mass exchange at the interface will be applied, yielding detailed information on local variables. Subsequently, this detailed information will be transferred to the 1D system code during an upscaling operation. Once this information is properly gathered and transferred, new models will be developed and implemented into the system code. Finally, these new models will be confronted to experimental data in a validation exercise over the CATHARE code validation database. Ultimately, the aim is to increase the confidence in the CATHARE3 1-D simulation tool for predicting the specific physics of sodium boiling during an unprotected loss of flow transient.
The doctoral student will be based in a research unit on innovative nuclear systems at CEA/IRESNE Cadarache, in a dynamic and international environment. Travel to CEA-Saclay and EDF-Chatou is planned during the thesis, as well as participation in international conferences.
Sans objet.
Steam generators are essential components of nuclear reactors whose main function is heat exchange. The chemical species present in steam generators are the cause of many parasitic phenomena (clogging, fouling, sludge deposition, etc.). Numerical simulation of species transport, taking into account the migration of chemical species and exchanges between species, both intra- and inter-phase, will allow a better understanding and better management of these problems. Numerical resolution of species transport systems presents real difficulties, in particular the management of the appearance and total disappearance of certain species, high void rates, as well as rapidly excessive calculation times.
While relying on the new code for nuclear components developed at STMF, the thesis will address the following three main scientific issues:
• Upstream, the analysis of numerical methods allowing in particular the management of evanescence, as mentioned above, and thermo-hydraulic modeling at high void rates. For this, we will rely on the PolyMAC and PolyVEF numerical schemes, already implemented in the component code.
• The physical modeling of a steam generator in the new component code, via the addition (in C++) of correlations specific to steam generators, the completion of the state laws already available, etc..
• The determination of the major chemical species to be transported, in order to be able to take into account both thermo-hydraulics and chemistry. The algorithmic coupling between thermo-hydraulics and chemistry, taking into account feedback, being the long-term objective.
While benefiting from the existing parallelization of the component code, the thermo-hydraulic and chemical modeling will be done taking into account the constraints on computation times.
This thesis aims to explore the application of machine learning techniques to improve turbulence modeling and numerical simulations in fluid mechanics. More specifically, we are interested in the application of artificial neural networks (ANNs) for large eddy simulation. The latter is a modeling approach that focuses on the direct resolution of large turbulent structures, while modeling small scales by a subgrid-scale model. It requires a certain ratio of total kinetic energy to be resolved. However, this ratio may be difficult to achieve for industrial simulations due to the high computational cost, leading to under-resolved simulations. We aim to improve the latter by focusing work along two main axes: 1) Using ANNs to build generic sub-mesh models that outperform analytical models and compensate for coarse spatial discretization; 2) Training ANNs to learn wall models. One of the main challenges is the ability of the new models to generalize correctly in configurations different from those used during training. Thus, taking into account the different sources and quantification of uncertainties plays a vital role in improving the reliability and robustness of machine-learned models.
The performance of micro-LEDs (µLEDs) is crucial for micro-displays, a field of expertise at the LITE laboratory within CEA-LETI. However, simulating these components is complex and computationally expensive due to the incoherent nature of light sources and the involved geometries. This limits the ability to effectively explore multi-parameter design spaces.
This thesis proposes to develop an innovative finite element method to accelerate simulations and enable the use of topological optimization. The goal is to produce non-intuitive designs that maximize performance while respecting industrial constraints.
The work is divided into two phases:
Develop a fast and reliable simulation method by incorporating appropriate physical approximations for incoherent sources and significantly reducing computation times.
Design a robust topological optimization framework that includes fabrication constraints to generate immediately realizable designs.
The expected results include optimized designs for micro-displays with enhanced performance and a methodology that can be applied to other photonic devices.
This thesis will explore the use of implanted brain-computer interfaces (BCIs) for motor rehabilitation in stroke patients. The project leverages Clinatec's WIMAGINE technology, which measures brain activity through electrocorticography (ECoG) to accurately decode patients' motor intentions. Integrating this technology into a rehabilitation protocol is expected to enhance residual motor abilities through neural plasticity. In this context, the thesis aims to implement the rehabilitation protocol with Clinatec's technical and clinical teams and to characterize motor recovery in patients during training sessions, both behaviorally and neurally. The study will include a review of current BCI-assisted rehabilitation approaches, the development of tools to monitor motor progress and measure neural plasticity indicators, as well as the optimization of rehabilitation sessions based on patients' motor performance. Motor and neural progression will be studied longitudinally over nine months, with an extended two-year follow-up to assess the durability of the benefits. Supported by the French Ministry of Research and the EU, this project offers a unique opportunity to establish the foundations of a new post-stroke rehabilitation paradigm using implanted BCIs.
Semantic communications is an emerging and transformative research area, where the focus shifts from transmitting raw data to conveying meaningful information. While initial models and design solutions have laid foundational principles, they often rest on strong assumptions regarding the extraction, representation, and interpretation of semantic content. The advent of 6G networks introduces new challenges, particularly with the growing need for multi-agent systems where multiple AI-driven agents interact seamlessly.
In this context, the challenge of semantic alignment becomes critical. Existing literature on multi-agent semantic communications frequently assumes that all agents share a common understanding and interpretation framework, a condition rarely met in practical scenarios. Misaligned representations can lead to communication inefficiencies, loss of critical information, and misinterpretations.
This PhD research aims to advance the state-of-the-art by investigating the principles of semantic representation, alignment, and reasoning in multi-AI agent environments within 6G communication networks. The study will explore how agents can dynamically align their semantic models, ensuring consistent interpretation of messages while accounting for differences in context, objectives, and prior knowledge. By leveraging techniques from artificial intelligence, such as machine learning, ontology alignment, and multi-agent reasoning, the goal is to propose novel frameworks that enhance communication efficiency and effectiveness in multi-agent settings. This work will contribute to more adaptive, intelligent, and context-aware communication systems that are key to the evolution of 6G networks.
In light of the growing demand for transmission capacity in communication networks, it is essential to explore innovative techniques that enhance spectral efficiency while maintaining the reliability and security of transmission links. This project proposes a comprehensive theoretical modeling of Faster-Than-Nyquist (FTN) systems, accompanied by simulations and numerical analyses to evaluate their performance in various communication scenarios. The study will aim to identify the necessary trade-offs to maximize transmission rates while considering the constraints related to implementation complexity and transmission security, a crucial issue in an increasingly vulnerable environment to cyber threats. This work will help identify opportunities for capacity enhancement while highlighting the technological challenges and adjustments necessary for the widespread adoption of these systems for critical and secure links.