Development of a 2D kinetic model for the high-temperature oxidation of chromia-forming alloys.
For many industrial applications, the high temperature oxidation phenomena of components need to be assessed in order to optimise the design of the component. This is the case, for example, for aircraft engine turbines in the aerospace industry (ambient temperatures of 1000°C), heat exchanger tubes in power plants (temperatures of 300 to 600°C), vitrification pots for long-lived radioactive waste (temperatures in excess of 1000°C), etc. All these applications use Fe-Ni-Cr alloys, the oxidation of which leads to the formation of a layer of chromium oxide, Cr2O3. The development of reliable models and simulation tools for the oxidation of Fe-Ni-Cr alloys at high temperatures (from 350°C) is therefore a major challenge for limiting the costs associated with high-temperature applications.
The post-doc will be divided into two parts: the first will involve using a simulation tool created at the CEA (EKINOX-FeNiCr) and the second will be based on the transition from the 1D model to the 2D model in order to take into account the finite size of components or geometric singularities.
The generality of this subject, which can be applied to many industrial cases, and the detailed understanding of oxidation phenomena will enable the student to move into both academic and industrial research at the end of the post-doctorate.
Application of a filtering method for the estimation of effective transmission condition parameters from ultrasonic data
In a recently completed thesis work, a filtering strategy combining both iterations of a Levemberg-Marquardt descent method with a gradient-free Kalman filtering approach has been developed. First evaluations of the algorithm have been carried out in order to reconstruct the pre-deformation of a plate geometry from guided wave ultrasound data. In this context, the main objective of the proposed work is on the one hand to consolidate the knowledge and implementation of the proposed approach, and to confirm its efficiency and interest in other ultrasonic NDT configurations. A particular application case of interest in the framework of this work will be the reconstruction of the Effective Transmission Conditions (ETCs) parameters that can typically represent: a delamination defect between two layers of a composite material, an imperfect bonding between an ultrasonic sensor and the inspected part, or an interface presenting a roughness of characteristic dimensions lower than the minimum wavelength used for the control. In practical industrial cases, the parameters of these ETCs are difficult to obtain. Thus, the interest of setting up a filtering process is to offer, in complex cases, an automatic calibration of the effective parameters of these models.
Advanced reconstruction methods for cryo-electron tomography of biological samples
Cryo-electron tomography (CET) is a powerful technique for the 3D structural analysis of biological samples in their near-native state. CET has seen remarkable advances in instrumentation in the last decade but the classical weighted back-projection (WBP) remains by far the standard CET reconstruction method. Due to radiation damage and the limited tilt range within the microscope, WBP reconstructions suffer from low contrast and elongation artifacts, known as ‘missing wedge’ (MW) artifacts. Recently, there has been a revival of interest in iterative approaches to improve the quality and hence the interpretability of the CET data.
In this project, we propose to go beyond the state-of-the-art in CET by (1) applying curvelet- and shearlet-based compressed sensing (CS) algorithms, and (2) exploring deep learning (DL) strategies with the aim to denoise et correct for the MW artifacts. These approaches have the potential to improve the resolution of the CET reconstructions and facilitate the segmentation and sub-tomogram averaging tasks.
The candidate will conduct a comparative study of iterative algorithms used in life science, and CS and DL approaches optimized in this project for thin curved structures.
Generative deep-learning modeling and machine-learning potentials for the calculation of atomic-scale transport properties in disordered uranium-plutonium mixed oxides
Machine learning (ML) is now commonly used in materials science to enhance the predictive capabilities of physical models. ML interatomic potentials (MLIP) trained on electronic-structure calculations have become standard tools for conducting efficient yet physically accurate molecular dynamics simulations. More recently, unsupervised generative ML models are being explored to learn hidden property distributions, and generate new atomic structures according to these distributions. This is useful for chemically disordered solid solutions, whose properties depend on the distribution of chemical species in the crystal lattice. In such cases, the number of possible configurations is so large that exhaustive sampling is beyond the capability of conventional methods. An example is U-Pu mixed oxides (MOX), a type of nuclear fuel that can significantly reduce the volume and radiotoxicity of spent-fuel waste. High-entropy alloys are another class of disordered materials that are promising candidates for radiation-resistant nuclear materials.
The goal is to combine MLIPs and generative methods to address atomic transport properties in MOX. The candidate will use in-house ML generative tools to generate representative atomic configurations and build an ab initio database. They will use this database to train a new MLIP for MOX, leveraging the experience gained from developing analogous MLIPs for the pure UO2 and PuO2 oxides. Finally, they will apply the MLIP to calculate atomic diffusion coefficients, crucial for predicting irradiation-induced microstructure evolution and in-reactor behavior.
The work will be conducted at the Nuclear Fuel Department (IRESNE, CEA Cadarache), within a scientific environment characterized by a high level of expertise in materials modelling, and in close collaboration with other CEA teams in the Paris region specialized in ML methods. Results will be disseminated through scientific publications and participation in international conferences
Modeling of charge noise in spin qubits
Thanks to strong partnerships between several research institutes, Grenoble is a pioneer in the development of future technologies based on spin qubits using manufacturing processes identical to those used in the silicon microelectronics industry. The spin of a qubit is often manipulated with alternating electrical (AC) signals through various spin-orbit coupling (SOC) mechanisms that couple it to electric fields. This also makes it sensitive to fluctuations in the qubit's electrical environment, which can lead to large qubit-to-qubit variability and charge noise. The charge noise in the spin qubit devices potentially comes from charging/discharging events within amorphous and defective materials (SiO2, Si3N4, etc.) and device interfaces. The objective of this postdoc is to improve the understanding of charge noise in spin qubit devices through simulations at different scales. This research work will be carried out using an ab initio type method and also through the use of the TB_Sim code, developed within the CEA-IRIG institute. This last one is able of describing very realistic qubit structures using strong atomic and multi-band k.p binding models.
detection of multiplets and application to turkey-Syria seismic crisis of february 2023
The correlation technique, or template matching, applied to the detection and analysis of seismic events has demonstrated its performance and usefulness in the processing chain of the CEA/DAM National Data Center. Unfortunately, this method suffers from limitations which limit its effectiveness and its use in the operational environment, linked on the one hand to the computational cost of massive data processing, and on the other hand to the rate of false detections that could generate low-level processing. The use of denoising methods upstream of processing (example: deepDenoiser, by Zhu et al., 2020), could also increase the number of erroneous detections. The first part of the research project consists of providing a methodology aimed at improving the processing time performance of the multiplets detector, in particular by using information indexing techniques developed in collaboration with LIPADE (L-MESSI method , Botao Peng, Panagiota Fatourou, Themis Palpanas. Fast Data Series Indexing for In-Memory Data. International Journal on Very Large Data Bases (VLDBJ) 2021). The second part of the project concerns the development of an auto-encoder type “filtering” tool for false detections built using machine learning. The Syria-Turkey seismic crisis of February 2023, dominated by two earthquakes of magnitude greater than 7.0, will serve as a learning database for this study.
Optimization of a metrological approach to radionuclide identification based on spectral unmixing
The Laboratoire national Henri Becquerel (LNE-LNHB) at CEA/Saclay is the laboratory responsible for French references in the field of ionizing radiations. For several years now, it has been involved in the development of an automatic analysis tool for low-statistics gamma spectra, based on the spectral unmixing technique. This approach makes it possible to respond to metrological constraints such as robust decision-making and unbiased estimation of counts associated with identified radionuclides. To extend this technique to field measurements, and in particular to the deformation of spectra due to interactions in the environment of a radioactive source, a hybrid spectral unmixing model combining statistical and automatic learning methods is currently being developed. The aim of this mathematical solution is to implement a joint estimation of the spectra measured and the counts associated with the radionuclides identified. The next step will be to quantify the uncertainties of the quantities estimated from the hybrid model. The aim is also to investigate the technique of spectral unmixing in the case of neutron detection with a NaIL detector. The future candidate will contribute to these various studies in collaboration with the Laboratoire d'ingénierie logicielle pour les applications scientifiques (CEA/DRF).
Development of Algorithms for the Detection and Quantification of Biomarkers from Voltammograms
The objective of the post-doctoral research is to develop a high-performance algorithmic and software solution for the detection and quantification of biomarkers of interest from voltammograms. These voltammograms are one-dimensional signals obtained from innovative electrochemical sensors. The study will be carried out in close collaboration with another laboratory at CEA-LIST, the LIST/DIN/SIMRI/LCIM, which will provide dedicated and innovative electrochemical sensors, as well as with the start-up USENSE, which is developing a medical device for measuring multiple biomarkers in urine.
Earthquake effect on underground facilities
The Industrial Centre for Geological Disposal (Cigeo) is a project for a deep geological disposal facility for radioactive waste to be built in France. These wastes will be put in sealed packages in tunnels designed at 500 meters depth. The seals are made of a bentonite/sand mixture which has a high swelling capacity and a low water permeability. As a part of the long-term safety demonstration of the repository, it must be demonstrated that the sealing structures can fulfill their functions under seismic loads over their entire lifetime. In order to guarantee this future nuclear waste repository, CEA and Andra are collaborating to work on the potential scientific and engineering challenges involved.
The responses of underground repository to earthquake events are complex due to the spatially and temporally evolving hydro-mechanical properties of the surrounding media and the structure itself. Accurate modeling of the behavior, therefore, requires a coupled multiphysics numerical code to efficiently model the seismic responses for these underground repositories within their estimated lifespan of 100 thousand years.
The research will therefore, propose a performance assessment for sequential and parallel finite element numerical modeling for earthquake analysis of deep underground facilities. Then perform a synthetic data sampling to account for material uncertainties and based on the obtained results in the previous assessment, run a sensitivity analysis using a FEM or a metamodeling process. Finally, the results and knowledge gained within the span of this project will be processed and interpreted to provide responses for industrial needs.