Chemo-mechanical modeling of the coupling between carbonation, rebar corrosion and cracking in cementitious materials
Rebar corrosion is one of the main causes of premature degradation of concrete infrastructures, including in the nuclear sector, where concrete is extensively used in containment structures and waste storage facilities. Carbonation, caused by the penetration of CO2 into the concrete, lowers the pH of the pore solution, promoting rebar corrosion. This corrosion leads to the formation of expansive products that can cause cracking in the material. The proposed thesis work, developed as part of a European collaborative project between CEA Saclay, École des Mines de Paris - PSL, and IRSN, aims to develop a numerical model to simulate these phenomena. The model combines a reactive transport code (Hytec) and a finite element code (Cast3M) to study the local effects of carbonation-induced corrosion on concrete cracking. This project will benefit from parallel experimental work to gather data for parameter identification and model validation. The first part of the research will focus on modeling the carbonation of cementitious materials under unsaturated conditions, while the second part will address the corrosion of rebar caused by the pH drop induced by carbonation. The model will describe the growth of corrosion products and their expansion, inducing stress within the concrete and potential microcracking.
This research project is aimed at a PhD student wishing to develop their skills in materials science, with a strong focus on multi-physical and multi-scale modeling and numerical simulations. The thesis will be carried out principally at CEA Saclay and at École des Mines de Paris – PSL (Fontainebleau).
Improving the predictivity of large eddy simulations using machine learning guided by high-fidelity simulations
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.
HW/SW Contracts for Security Analysis Against Fault Injection Attacks on Open-source Processors
This thesis focuses on the cybersecurity of embedded systems, particularly the vulnerability of processors and programs to fault injection attacks. These attacks disrupt the normal functioning of systems, allowing attackers to exploit weaknesses to access sensitive information. Although formal methods have been developed to analyze the robustness of systems, they often limit their analyses to hardware or software separately, overlooking the interaction between the two.
The proposed work aims to formalize hardware/software (HW/SW) contracts specifically for security analysis against fault injection. Building on a hardware partitioning approach, this research seeks to mitigate scalability issues related to the complexity of microarchitecture models. Expected outcomes include the development of techniques and tools for effective security verification of embedded systems, as well as the creation of contracts that facilitate the assessment of compliance for both hardware and software implementations. This approach could also reduce the time-to-market for secure systems.
Cryptographic security of RISC-V processor enclaves with CHERI
CHERI (Capability Hardware Enhanced RISC Instructions) is a solution for securing the processor against spatial and temporal memory leaks by transforming any pointer into a capability that clearly defines the access limits to the data or instructions addressed.
In this thesis, we propose to enrich CHERI and its control-flow integrity capabilities on a RISC-V application processor, by protecting instructions right up to their execution against any type of modification. Secondly, based on authenticated memory encryption, we will study the possibility of using CHERI to define secure enclaves enabling cryptographic isolation between processes. The processor will be modified so that each process is encrypted with its own key and can have a secure life cycle. All keys must be efficiently protected in hardware.
Contact : olivier.savry@cea.fr
Topologic optimization of µLED's optical performance
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.
Combining over and underapproximation of memory abstractions for low-level code analysis
Rice's theorem stating that no method can automatically tell whether a property of a program is true or not has led to the separation of verification tools into two groups: sound tools operating by over-approximation, such as abstract interpretation, are able to automatically prove that certain properties are true, but are sometimes unable to conclude and produce alarms; conversely, complete tools operating by under-approximation, such as symbolic execution, are able to produce counter-examples, but are unable to demonstrate whether a property is true.
*The general aim of the thesis is to study the combination of sound and complete methods of programanalysis, and in particular static analysis by abstract interpretation and the generation of underapproximated formulae by symbolic execution*.
We are particularly interested in the combination of over- and sub-approximating abstractions, especially for memory. The priority applications envisaged concern the analysis of code at the binary level, as achieved by the combination of the BINSEC and CODEX analysis platforms, so as to automatically discover new security vulnerabilities, or prove their absence.
Study and evaluation of silicon technology capacities for applications in infrared bolometry
Microbolometers currently represent the dominant technology for the realization of uncooled infrared thermal detectors. These detectors are commonly used in the fields of thermography and surveillance. However, the microbolometer market is expected to grow explosively in the coming years, particularly with their integration into automobiles and the proliferation of connected devices. The CEA Leti LI2T, a recognized player in the field of infrared thermal detectors, has been transferring successive microbolometer technologies to the industrial partner Lynred for over 20 years. To remain competitive in this growing market for microbolometers, the laboratory is working on breakthrough microbolometers incorporating CMOS components as the sensitive element. In this context, the laboratory has initiated studies focusing on temperature-dependent silicon technology capabilities, with promising initial results not reported in the literature. The thesis topic fits into this context and aims to demonstrate the interest of these components for microbolometric applications. It will therefore cover the analytical modeling of these components and their associated physical effects, as well as the reading of such a component in a microbolometer imager approach. A reflection on technological integration will also be conducted. The student will benefit from several already realized technological lots to experimentally characterize the physical effects and familiarize themselves with the subject. To understand the encountered phenomena, the student will have access to the laboratory's entire test set-ups (semiconductor parameter tester, noise analyzer, optical bench, etc.) as well as the numerical analysis Tools (Matlab/Python, TCAD simulations, SPICE simulations, Comsol, etc.). By the end of the thesis, the student will be able to address the question of the interest of these components for microbolometric applications.
Characterization of motor recovery in stroke patients during a BCI-guided rehabilitation
Brain-computer interfaces (BCIs) make it possible to restore lost functions by allowing individuals to control external devices through the modulation of their brain activity. The CEA has developed a BCI technology based on the WIMAGINE implant, which records brain activity using electrocorticography (ECoG), along with algorithms for decoding motor intentions. This technology was initially tested for controlling robotic effectors such as exoskeletons and spinal cord stimulation devices to compensate for severe motor impairments. While this initial paradigm of substitution and compensation is promising, a different application potential is now emerging: functional recovery through BCI-guided rehabilitation. Current literature suggests that BCIs, when used intensively and in a targeted manner, can promote neural plasticity and, in turn, improve residual motor abilities. In particular, ECoG-based implanted BCIs could offer significant therapeutic outcomes. The objective of this thesis is therefore to assess the potential of CEA's BCI technology to enhance patients' residual motor functions through neural plasticity.
This work will be approached through a rigorous and multidisciplinary scientific methodology, including a comprehensive review of the scientific literature, the setup and execution of experimentations with patients, the algorithmic development of tools for monitoring and analyzing patient progress, and the publication of significant results in high-level scientific journals.
This PhD is intended for a student specializing in biomedical engineering, with expertise in signal processing and the analysis of complex physiological data, as well as experience in Python or Matlab. A strong interest in clinical experimentation and neuroscience will also be required. The student will work within a multidisciplinary team at CLINATEC, contributing to cutting-edge research in the field of BCIs.
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.