A macroscale approach to evaluate the long-term degradation of concrete structures under irradiation
In nuclear power plants, the concrete biological shield (CBS) is designed to be very close of the reactor vessel. It is expected to absorb radiation and acts as a load-bearing structure. It is thus exposed during the lifetime of the plant to high level of radiations that can have consequences on the long term. These radiations may result especially in a decrease of the material and structural mechanical properties. Given its key role, it is thus necessary to develop tools and models, to predict the behaviors of such structures at the macroscopic scale.
Based on the results obtained at a lower scale - mesoscopic simulations, from which a better understanding of the irradiation effect can be achieved and experimental results which are expected to feed the simulation (material properties especially), it is thus proposed to develop a macroscopic methodology to be applied to the concrete biological shield. This approach will include different phenomena, among which radiation-induced volumetric expansion, induced creep, thermal defromations and Mechanical loading.
These physical phenomena will be developed within the frame of continuum damage mechanics to evaluate the mechanical degradation at the macroscopic scale in terms of displacements and damage especially. The main challenges of the numerical developments will be the proposition of adapted evolution laws, and particularly the coupling between microstructural damage and damage at the structural level due to the stresses applied on the structure.
3D ultrasound imaging using orthogonal row and column addressing of the matrix array for ultrasonic NDT
This thesis is part of the activities of the Digital Instrumentation Department (DIN) in Non-Destructive Testing (NDT), and aims to design a new, fast and advanced 3D ultrasound imaging method using matrix arrays. The aim will be to produce three-dimensional ultrasound images of the internal volume of a structure that may contain defects (e.g. cracks), as realistically as possible, with improved performance in terms of data acquisition and 3D image computation time. The proposed method will be based on an approach developed in medical imaging based on Row and Column Addressed (RCA) arrays. The first part will focus on the development of new data acquisition strategies for matrix arrays and associated ultrafast 3D imaging using RCA approach in order to deal with conventional NDT inspection configurations. In the second part, developed methods will be validated on simulated data and evaluated on experimental data acquired with a conventional matrix array of 16x16 elements operating in RCA mode. Finally, a real-time proof of concept will be demonstrated by implementing the new 3D imaging methods in a laboratory acquisition system.
Understanding the mechanisms of oxidative dissolution of (U,Pu)O2 in the presence of platinum group metals
The treatment of MOx fuel, composed of a mixed uranium and plutonium oxide (U,Pu)O2, is aimed at recycling plutonium. Plutonium dioxide (PuO2) is notably difficult to dissolve in concentrated nitric acid. However, by introducing a highly oxidizing agent, such as Ag(II), into the nitric acid, plutonium can be solubilized with fast dissolution kinetics—a process known as oxidative dissolution. The fission products present in irradiated MOx, particularly platinum group metals, can potentially impair the effectiveness of plutonium’s oxidative dissolution through side reactions. For the industrial deployment of this method, it is therefore crucial to understand how platinum group metals influence the dissolution kinetics. Yet, there is currently very limited data on this subject.
This thesis aims to address this knowledge gap. The proposed research involves a parametric experimental study of increasing complexity: initially, the impact of platinum group metals on Ag(II) consumption will be investigated separately, followed by their effect during the dissolution of (U,Pu)O2. These findings will enable the development of a kinetic model for the dissolution process based on the studied parameters.
By the end of this thesis, the candidate, with a strong background in physical or inorganic chemistry, will have gained expertise in a wide range of experimental techniques and advanced modeling methods. This dual competence will open up numerous career opportunities in academic research or industrial R&D, both within and beyond the nuclear sector.
Experimental study of the two-phase natural convection and vaporization regimes in the cooling pool of a nuclear facility
Nuclear energy, with low CO2 emissions, is one of the major players in France's energy transition. In this context, the management of the cooling of irradiated fuel elements is a matter of utmost importance. This thesis focuses on two-phase natural convection flows and vaporization phenomena that can develop in the cooling pools of various nuclear facilities, particularly those having a significant vertical variation in the saturation temperature of the coolant due to their great depth. These pools are used to dissipate the residual heat from irradiated fuels in many types of nuclear reactors, both existing and planned. In an accident scenario with a significant heat release from the fuels, the water in these pools can vaporize, eventually limiting their cooling capability. Among the possible phase change mechanisms in deep pools is the gravity-driven flashing, a phenomenon found in various natural or industrial systems analogous to vertical channels heated from below. However, this phenomenon has been little studied in the specific configuration of a pool and was only recently observed in this context. Therefore, the objective of this thesis is to better understand the phenomenon, as well as the turbulence induced within the coolant by the bubbles it generates, in order to improve state-of-the-art thermal-hydraulic models for simulating such pools. The proposed research, of an experimental nature, will be conducted in collaboration with the Catholic University of Louvain (UCLouvain, Belgium) and the LEGI laboratory of CNRS Grenoble, with a significant portion of the research carried out at UCLouvain. The candidate will be affiliated to the Core and Circuit Thermal-hydraulics Laboratory (LTHC) of CEA IRESNE, specialized in the study of two-phase flows in nuclear facilities. During the thesis, finely resolved experimental data in both space and time will be acquired and interpreted, contributing to a better understanding of the phenomenon. To achieve this, advanced techniques such as stereo particle image velocimetry (3D PIV) in two-phase media, thermometry and shadowgraphy will be employed. During this thesis project, the PhD student will be able to develop skills in the field of experimental thermal-hydraulics through the definition, execution, and interpretation of tests, as well as the use of advanced two-phase flow measurement techniques.
Machine Learning-based Algorithms for the Futur Upstream Tracker Standalone Tracking Performance of LHCb at the LHC
This proposal focuses on enhancing tracking performance for the LHCb experiments during Run 5 at the Large Hadron Collider (LHC) through the exploration of various machine learning-based algorithms. The Upstream Tracker (UT) sub-detector, a crucial component of the LHCb tracking system, plays a vital role in reducing the fake track rate by filtering out incorrectly reconstructed tracks early in the reconstruction process. As the LHCb detector investigates rare particle decays, studies CP violation in the Standard Model, and study the Quark-Gluon plasma in PbPb collisions, precise tracking becomes increasingly important.
With upcoming upgrades planned for 2035 and the anticipated increase in data rates, traditional tracking methods may struggle to meet the computational demands, especially in nucleus-nucleus collisions where thousands of particles are produced. Our project will investigate a range of machine learning techniques, including those already demonstrated in the LHCb’s Vertex Locator (VELO), to enhance the tracking performance of the UT. By applying diverse methods, we aim to improve early-stage track reconstruction, increase efficiency, and decrease the fake track rate. Among these techniques, Graph Neural Networks (GNNs) are a particularly promising option, as they can exploit spatial and temporal correlations in detector hits to improve tracking accuracy and reduce computational burdens.
This exploration of new methods will involve development work tailored to the specific hardware selected for deployment, whether it be GPUs, CPUs, or FPGAs, all part of the futur LHCb’s data architecture. We will benchmark these algorithms against current tracking methods to quantify improvements in performance, scalability, and computational efficiency. Additionally, we plan to integrate the most effective algorithms into the LHCb software framework to ensure compatibility with existing data pipelines.
Multi-physical characterization of potassium hybrid supercapacitors for performance improvement
The PhD subject focuses on the optimization of potassium hybrid supercapacitors (KIC), which combine the properties of supercapacitors (power, cyclability) and batteries (energy). This system, developed at the CEA, represents a promising technology, low cost and without critical/strategic materials. However, performance optimization still requires overcoming various obstacles observed in previous work, in particular on the intercalation of potassium in graphite and the heating phenomena of cells during operation. In order to explore in depth the operating mechanisms of the KIC system, an essential part of the thesis project will include experiments conducted at the ESRF (European Synchrotron Radiation Facility), where advanced diffraction and imaging techniques will be used to analyze the structure of the materials and their behavior in real operating conditions. The processing of the data collected will also be crucial in order to establish correlations between the physicochemical properties of the materials and the overall performance of the system. This thesis will contribute to the fundamental understanding of the multi-physical mechanisms at stake in KIC to develop innovative design strategies and thus improve their capacity, energy efficiency and lifetime.
Learning Interpretable Models for Stress Corrosion of Stainless Steels Exposed in the Primary Environment of PWRs
Stress corrosion cracking (SCC) of austenitic alloys in water-cooled nuclear reactors is one of the most significant component degradation phenomena. SCC occurs due to the synergistic effects of tensile stresses, environment and material susceptibility. For reactor life extension, understanding this mechanism is essential. The methodology most frequently employed to investigate SCC cracking is an experimental one, requiring lengthy and costly tests of several thousand hours. Furthermore, the considerable number of critical parameters that influence susceptibility to SCC cracking and coupling effects have resulted in test grids increasing in length and complexity. This thesis proposes a novel approach based on the use of interpretable models that are driven by the artificial intelligence of fuzzy logic. The aim is to reduce the length and cost of research activities by focusing on relevant tests and parameters that can improve environmental performance. The key issues here will be to add the performance of artificial intelligence to the experimental approach, with the aim of defining susceptibility domains for the initiation of SCC cracks as a function of the critical parameters identified in the model, and providing data for the development of new materials by additive manufacturing. The thesis will develop a numerical model that can be used as guidance in decision-making regarding the stress corrosion mechanism. The future PhD student will also carry out experimental work to validate this new numerical approach.
SCO&FE ALD materials for FeFET transistors
Ferroelectric Field Effect Transistors FeFET is a valuable high-density memory component suitable for 3D DRAM. FeFET concept combines oxide semiconductors SCO as canal material and ferroelectric metal oxides FE as transistor gate [2, 3]. Atomic layer deposition ALD of SCO and FE materials at ultrathin thickness level (<10 nm) and low temperature (10 cm2.Vs); ultrathin (<5nm) and ultra-conformal (aspect ratio 1:10). The PhD student will beneficiate from the rich technical environment of the 300/200mm CEA-LETI clean-room and the nano-characterization platform (physico-chemical, structural and microscopy analysis, electrical measurements).
The developments will focus on the following items:
1-Comparison of SCO layers (IGZO Indium Gallium Zinc Oxide) fabricated using ALD and PVD techniques: implementation of adapted mesurements techniques and test vehicles
2-Intrinsec and electrical characterization of ALD-SCO (IWO, IGZO, InO) and ALD-EF (HZO) layers: stoichiometry, structure, resistivity, mobility….
3-Co-integration of ALD-SCO and ALD-FE layers for vertical and horizontal 3D FeFET structures
[1]10.35848/1347-4065/ac3d0e
[2]https://doi.org/10.1109/TED.2023.3242633
[3]https://doi.org/10.1021/acs.chemmater.3c02223
Laser Fault Injection Physical Modelling in FD-SOI technologies: toward security at standard cells level on FD-SOI 10 nm node
The cybersecurity of our infrastructures is at the very heart in the digital transition on-going, and security must be ensured throughout the entire chain. At the root of trust lies the hardware, integrated circuits providing essential functions for the integrity, confidentiality and availability of processed information.
But hardware is vulnerable to physical attacks, and defence has to be organised. Among these attacks, some are more tightly coupled to the physical characteristics of the silicon technologies. An attack using a pulsed laser in the near infrared is one of them and is the most powerful in terms of accuracy and repeatability. Components must therefore be protected against this threat.
As the FD-SOI is now widely deployed in embedded systems (health, automotive, connectivity, banking, smart industry, identity, etc.) where security is required. FD-SOI technologies have promising security properties as being studied as less sensitive to a laser fault attack. But while the effect of a laser fault attack in traditional bulk technologies is well handled, deeper studies on the sensitivity of FD-SOI technologies has to be done in order to reach a comprehensive model. Indeed, the path to security in hardware comes with the modelling of the vulnerabilities, at the transistor level and extend it up to the standard cells level (inverter, NAND, NOR, Flip-Flop) and SRAM. First a TCAD simulation will be used for a deeper investigation on the effect of a laser pulse on a FD-SOI transistor. A compact model of an FD-SOI transistor under laser pulse will be deduced from this physical modelling phase. This compact model will then be injected into various standard cell designs, for two different objectives: a/ to bring the modelling of the effect of a laser shot to the level of standard cell design (where the analog behaviour of a photocurrent becomes digital) b/ to propose standard cell designs in FD-SOI 10nm technology, intrinsically secure against laser pulse injection. Experimental data (existing and generated by the PhD student) will be used to validate the models at different stages (transistor, standard cells and more complex circuits on ASIC).
Ce sujet de thèse est interdisciplinaire, entre conception microélectronique, simulation TCAD et simulation SPICE, tests de sécurité des systèmes embarqués. Le candidat sera en contact/encadré avec deux équipes de recherche; conception microélectronique , simulation TCAD et sécurité des systèmes embarqués.
Contacts: romain.wacquez@cea.fr, jean-frederic.christmann@cea.fr, sebastien.martinie@cea.fr
Super-gain miniature antennas with circular polarization and electronic beam steering
Antenna radiation control in terms of shape and polarization is a key element for future communication systems. Directive compact antennas offer new opportunities for wireless applications in terms of spatial selectivity and filtering. This leads to a reduction in electromagnetic pollution by mitigating interferences with other communication systems and reducing battery consumption in compact smart devices (IoT), while enabling also new use modes. However, the conventional techniques for enhancing the directivity often lead to a significant increase of the antenna size. Consequently, the integration of directional antennas in small wireless devices is limited. This difficulty is particularly critical for the frequency bands below 3 GHz if object dimensions are limited to a few centimeters. Super directive/gain compact antennas with beam-steering capabilities and operating on a wideband or on multi-bands are an innovative and attractive solution for the development of new applications in the field of the connected objects. In fact, the possibility to control electronically the antenna radiation properties is an important characteristic for the development of the future generation and smart communication systems. CEA Leti has a very strong expertise in the domain of superdirective antennas demonstrating the potentials of the use of ultra-compact parasitic antenna arrays. This PhD project will take place at CEA Leti Grenoble in the antennas and propagation laboratory (LAPCI). The main objectives of this work are: i) contribution to development of numerical tools for the design and optimization of superdirective compact arrays with beam-steering capabilities; ii) the study of new elementary sources for compact antenna arrays; iii) the realization and experimental characterization of a supergain compact array with circular polarization and beam-steering capabilities. This work will combine theoretical studies and model developments, antenna design using 3D electromagnetic software, prototyping and experimentations.