Modeling and upscaling of sodium boiling flow within a 4th generation nuclear reactor core
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.
Scalability of the Network Digital Twin in Complex Communication Networks
Communication networks are experiencing an exponential growth both in terms of deployment of network infrastructures (particularly observed in the gradual and sustained evolution towards 6G networks), but also in terms of machines, covering a wide range of devices ranging from Cloud servers to lightweight embedded IoT components (e.g. System on Chip: SoC), and including mobile terminals such as smartphones.
This ecosystem also encompasses a variety of software components ranging from applications (e.g. A/V streaming) to the protocols from different communication network layers. Furthermore, such an ecosystem is intrinsically dynamic because of the following features:
- Change in network topology: due, for example, to hardware/software failures, user mobility, operator network resource management policies, etc.
- Change in the usage/consumption ratio of network resources (bandwidth, memory, CPU, battery, etc.). This is due to user needs and operator network resource management policies, etc.
To ensure effective supervision or management, whether fine-grained or with an abstract view, of communication networks, various network management services/platforms, such as SNMP, CMIP, LWM2M, CoMI, SDN, have been proposed and documented in the networking literature and standard bodies. Furthermore, the adoption of such management platforms has seen broad acceptance and utilization within the network operators, service providers, and the industry, where the said management platforms often incorporate advanced features, including automated control loops (e.g. rule-based, expert-system-based, ML-based), further enhancing their capability to optimize the performance of the network management operations.
Despite the extensive exploration and exploitation of these network management platforms, they do not guarantee an effective (re)configuration without intrinsic risks/errors, which can cause serious outage to network applications and services. This is particularly true when the objective of the network (re)configuration is to ensure real-time optimization of the network, analysis/ tests in operational mode (what- if analysis), planning updates/modernizations/extensions of the communication network, etc. For such (re)configuration objectives, a new network management paradigm has to be designed.
In the recent years, the communication network research community started exploring the adoption of the digital twin concept for the networking context (Network Digital Twin: NDT). The objective behind this adoption is to help for the management of the communication network for various purposes, including those mentioned in the previous paragraph.
The NDT is a digital twin of the real/physical communication network (Physical Twin Network: PTN), making it possible to manipulate a digital copy of the real communication network, without risk. This allow in particular for visualizing/predicting the evolution (or the behavior, the state) of the real network, if this or that network configuration is to be applied. Beyond this aspect, the NDT and the PTN network exchange information via one or more communication interfaces with the aim of maintaining synchronized states between the NDT and the PTN.
Nonetheless, setting up a network digital twin (NDT) is not a simple task. Indeed, frequent and real-time PTN-NDT synchronization poses a scalability problem when dealing with complex networks, where each network information is likely to be reported at the NDT level (e.g. a very large number of network entities, very dynamic topologies, large volume of information per node/per network link).
Various scientific contributions have attempted to address the question of the network digital twin (NDT). The state-of-the-art contributions focus on establishing scenarios, requirements, and architecture for the NDT. Nevertheless, the literature does not tackle the scalability problem of the NDT.
The objective of this PhD thesis is to address the scalability problem of network digital twins by exploring new machine learning models for network information selection and prediction.
Automatization of quantum computing kernel writing for quantum applications
The framework of Hamiltonian simulation opens up a new range of computational approaches for quantum computing. These approaches can be developed across all relevant fields of quantum computing applications, including, among others, partial differential equations (electromagnetism, fluid mechanics, etc.), quantum machine learning, finance, and various methods for solving optimization problems (both heuristic and exact).
The goal of this thesis is to identify a framework where these approaches—based on Hamiltonian simulation or block-encoding techniques—are feasible and can be written in an automated way.
This work could extend to the prototyping of a code generator, which would be tested on practical cases in collaboration with European partners (including a few months of internship within their teams).
Increasing the electrothermal robustness of new SiC devices
Silicon Carbide (SiC) is a semiconductor with superior intrinsic properties than Silicon for high temperature and high power electronics applications. SiC devices are expected to be extensively used in the electrification transition and novel energy management applications. To fully exploit the SiC superior properties, the future semiconductor devices will be used under extreme biasing and temperature conditions. These devices must operate safely at higher current densities, higher dV/dt and higher junction temperatures than Si devices does.
The objective of this thesis is to study the SiC devices fabricated at LETI under these extreme operating conditions, and to optimize their design to fully use the theoretical potential of SiC. The thesis work will include several phases that will be strongly coupled:
- Advanced electro-thermal characterisation (50%), by proposing new approaches to testing components in a box or on a suitable support, using artificial intelligence (AI) tools for data extraction and processing. The work will include adapting standard measurement methodologies to the specific switching characteristics of SiC.
- An assessment (15%) of the design and technological parameters responsible for the operating limits of the components.
- A physico-chemical characterisation component (15%) to analyse failures under these extreme conditions.
- The inclusion of predictive models (20%) for the sensitivity of architectures to extreme conditions and faults, based on modelling.
Design and optimization of color routers for image sensors
Color routers represent a promising technology that could revolutionize the field of image sensors. Composed of nanometricstructures called metasurfaces, these devices allow the modification of light propagation to improve the quantum efficiency of pixels. Thanks to recent technical advances, it is now possible to design and manufacture these structures, paving the way for more efficient image sensors.
The thesis topic focuses on the design and optimization of color routers for image sensors. Several research avenues will be explored, such as the implementation of new metasurfacegeometries (`freeform`) or innovative configurations to reduce pixel pitch (0.5µm or 0.6µm). Various optimization methods can be used, such as the adjointmethod, machine learning, or the use of auto-differentiable solvers. The designs must be resilient to the angle of light incidence and expected variations during manufacturing. After this simulation phase, the proposed structures will be manufactured, and the student will have the mission to characterize the chips and analyze the obtained results (quantum efficiency, modulation transfer function...).
This thesis will be co-supervised by STMicroelectronics and CEA LETI in Grenoble. The student will be integrated into the teams of engineer-researchers working on this project. He/she will be led to collaborate with various specialists in various fields such as lithography and optical characterization.
The student's main activities:
- Optical simulation using numerical methods (FDTD, RCWA)
- Development of optimization methodologies for metasurfacedesign (adjointmethod, topological optimization...)
- Electro-optical characterization and analysis of experimental data
CTC electrolyte pour LiS system
Lithium-Sulfur (Li-S) Batteries are among the most promising energy storage technologies for the fifth generation of batteries, often referred to as post-Li-ion. With a theoretical energy density five times higher than that of conventional Li-ion batteries and an abundant availability of sulfur, the Li-S system offers a unique potential to meet the growing demand for sustainable energy storage. However, current technology is limited by major challenges related to the dissolution of polysulfides in the electrolyte, leading to active sulfur loss, poor cycle life, and insufficient electrochemical performance. These limitations currently hinder the market deployment of this technology.
This thesis aims to explore an alternative approach based on an all-solid electrochemical sulfur conversion mechanism. To achieve this, a next-generation organic solid electrolyte developed in the laboratory will be implemented. This electrolyte features a unique lithium-ion conduction mechanism within a crystalline lattice, preventing polysulfide solubilization. The main objectives are:
1. To understand and control the ionic conduction mechanisms in these electrolytes.
2. To integrate this solid electrolyte into an innovative Li-S system.
3. To optimize the cathode structure for the solid-state mechanism and evaluate the electrochemical performance on a representative prototype scale.
The PhD candidate will use a wide range of characterization and analysis techniques to carry out this project:
• Formulation and characterization of the organic solid electrolyte: Techniques such as FT-IR and NMR to analyze chemical structures and identify the properties of synthesized materials (DSC, TGA, XRD, etc.).
• Electrochemical characterization: Analyses using electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and symmetric cell cycling tests to study ionic conduction properties and electrolyte stability.
• Formulation and performance study of the cathode: Formulation of carbon/sulfur composites and sulfur cathodes integrating the solid electrolyte; galvanostatic cycling tests and advanced interface analyses to understand and optimize solid-state sulfur conversion.
The research will progress in three main phases:
1. Development and characterization of the solid electrolyte: Material development, analysis of conduction mechanisms, and optimization of ionic and mechanical properties.
2. Design and optimization of the cathode structure: Improving electrolyte/cathode interfaces for solid-state sulfur conversion.
3. Electrochemical performance evaluation: Experimental validation of prototypes through in-depth tests, including cyclability and power performance.
Sub-THz programmable electromagnetic surfaces based on phase change material switches
Spatiotemporal manipulation of the near- and far-electromagnetic (EM)-field distribution and its interaction with matter in the THz spectrum (0.1-0.6 THz) is of prime importance in the development of future communication, spectroscopy, imaging, holography, and sensing systems. Reconfigurable Intelligent (Meta)Surface (RIS) is a cutting-edge hybrid analogue/digital architecture capable of shaping and controlling the THz waves at the subwavelength scale. To democratize the RIS technology, it will be crucial to reduce its energy consumption by two orders of magnitude. However, the state-of-the-art does not address the integration, scalability, wideband and high-efficiency requirements.
Based on our recent research results, the main objective of this project will be to demonstrate novel silicon-based RIS architectures s at 140 GHz and 300 GHz. The enhancement of the THz RIS performance will derive from a careful choice of the silicon technology and, from novel wideband meta-atom designs (also called unit cell or element) with integrated switches based on PCM (phase change material). The possibility of dynamically controlling the amplitude of the transmission coefficients of the meta-atoms, besides their phase, will be also investigated. Near-field illumination will be introduced to obtain an ultra-low profile. To the best of our knowledge, this constitutes a new approach for the design of high-gain antennas in the sub-THz range.
RF Circuit Design for Zero Energy Communication
Our ambition for 6G communication is to drastically reduce the Energy in IoT. For that purpose we aim at developing an integrated circuit enabling zero Energy communication.
The objective of this PhD is to design this circuit in FD-SOI and operating in the 2.4 GHz. In this PhD, we propose to use a new design technique which is currently revolutionizing the radio-frequency design. We expect that many innovations can be carried out during this PhD by combining those two innovations.
The candidate will integrate a large design team and he will participate in collaborative project at european level. As a first step, he will analyze the system constraints to choose the best architecture and derive the specifications. Then, he will formalize mathematically the performances of the backscattering technique in order to setup a design methodology. Then he will be working full time on circuit design, sending to fabrication two circuits in 22 um technology. He will be also involve in the test of the circuit as well as in the preparation of a demonstrator of the backscattering techniques. We expect to publish several papers in high level conferences.
Learning world models for advanced autonomous agent
World models are internal representations of the external environment that an agent can use to interact with the real world. They are essential for understanding the physics that govern real-world dynamics, making predictions, and planning long-horizon actions. World models can be used to simulate real-world interactions and enhance the interpretability and explainability of an agent's behavior within this environment, making them key components for advanced autonomous agent models.
Nevertheless, building an accurate world model remains challenging. The goal of this PhD is to develop methodology to learn world models and study their use in the context of autonomous driving, particularly for motion forecasting and developing autonomous agents for navigation.
Development of a predictive power model for a photovoltaic device under spatial constraints
CEA is developing new cell and module architectures and simulation tools to assess the electrical performance of photovoltaic (PV) systems in their operating environment. One of these models, called CTMod (Cell To Module), takes into account not only the different materials making up the module, but also the different cell architectures. For space applications, the community wants to use terrestrial silicon-based technologies that can be integrated on flexible PVAs (Photovoltaic Assembly). The space environment imposes severe constraints. A relevant evaluation of performance at the start and end of a mission is therefore essential for their dimensioning.
The aim of this thesis is to correlate physical models of radiation-matter degradation in space with electrical models of photovoltaic cells. Performance degradations linked to the various electron, proton and ultraviolet (UV) irradiations of the space environment will be evaluated and validated experimentally. Linked to the CTMod Model, this new approach, never seen in the literature, will able to get a more accurate understanding of interactions between radiations and PVAs. These degradations result from non-ionizing energy deposition phenomena, quantified by the defect dose per displacement, and ionizing ones quantified by the total ionizing dose for protons and electrons. In the case of UV, the excitation of electrons in matter generates chain breaks in organic materials and colored centers in inorganic materials. Initially, the solar cell used in the model will be a Silicon cell, but the model can be extended to include other types of solar cell under development, such as perovskite-based cells.