Extension of the massively parallel code AMITEX: non-periodic, GPU, coupling
The AMITEX code developed by CEA is a massively parallel code (distributed memory), using Discrete Fourier Transforms, for the numerical simulation of the mechanical behavior of heterogeneous materials. It overcomes the limitations (in size and computation time) encountered by standard finite-element codes used in the same context. A stabilized version of the code, available to the public at https://amitexfftp.github.io/AMITEX/, is used by various national (Mines Paris, ONERA, ENSTA Bretagne, I2M-ENSAM Bordeaux, Météo-France, etc.) and international (USA, UK, China, Canada, Germany, Finland, Poland, etc.) teams.
The aim of the post-doc, proposed over two years, is threefold: to extend the field of application of the AMITEX code to non-periodic boundary conditions, to explore a possible adaptation to hybrid CPU/GPU architectures, and to use these extensions to develop FFT-based multi-scale couplings. This objective is built around the 2decomp open source library, on which AMITEX is based, and whose development was taken over in 2022 (https://github.com/2decomp-fft/2decomp-fft)[1] by a French-English team.
The various tasks of the post-doc consist of :
- extend the functionalities of the AMITEX code (non-periodic BC) by introducing different types of discrete transforms (sine, cosine, Fourier) within the 2decomp library,
- explore 2decomp's current development towards the use of hybrid CPU/GPU architectures, with the aim of setting up a first AMITEX-GPU code,
- use this new functionality for multi-scale coupling, where a local (refined) simulation interacts with a global (unrefined) simulation [3] via non-periodic boundary conditions.
Predicting corrosion properties using artificial intelligence and thermodynamic calculations
Project CORRTHIA aims to demonstrate the use of thermodynamic calculation for predicting corrosion properties using artificial intelligence (AI) models. Given the complexity of corrosion and the difficulty in describing it with current physical models,
understanding a material's behavior requires long-term experiments. The use of AI for predicting corrosion properties is a promising method for accelerated materials design, as it can limit the required experiments by selecting relevant materials. This research
theme aligns with the CEA's strategy for numerical material design and the objectives of the PEPR DIADEME.
Leveraging expertise in corrosion and thermodynamic calculation from the DES team and AI experience from DRT partners, we will focus on high-temperature oxidation (> 500°C) of metallic alloys to limit the range of compositions and possible environments. It is
expected that thermodynamic aspects play an essential role under these conditions. The scarcity of corrosion data necessitates work on constructing a dataset (Lot 1), based on published literature or internal S2CM data, which will be enriched with thermodynamic
calculation results. This augmented dataset will be used to train AI models.
Modeling the corrosion behavior of stainless steels in a nitric acid media with temperature
Controlling the aging of equipment materials (mainly stainless steel) of the spent nuclear fuel reprocessing plant is the subject of constant attention. This control requires a better understanding of the corrosion phenomena of steels by nitric acid (oxidizing agent used during the recycling stages), and ultimately through their modeling.
The materials of interest are Cr-Ni austenitic stainless steels, with very low carbon content. A recent study on Si-rich stainless steel, which was developed with the aim of improving the corrosion resistance of these steels with respect to highly oxidizing environments [1 , 2 ]; showed that the corrosion of this steel was thermally activated between 40 °C and 142 °C with different behavior below and above the boiling temperature (107 °C) of the solution [3]. Indeed, between 40°C and 107°C, the activation energy is 77 kJ/mol and above boiling point, it is much lower and is worth 20 kJ/mol. This difference may be due to a lower energy barrier or a different kinetically limited step.
The challenge of this post-doctoral subject is to have a predictive corrosion model depending on the temperature (below and beyond boiling). With this objective, it will be important to analyze and identify the species involved in the corrosion process (liquid and gas phase) as a function of temperature but also to characterize the boiling regimes. This model will be able to explain the difference in activation energies of this Si-rich steel below and above the boiling temperature of a concentrated nitric acid solution but will also make it possible to optimize the processes of the factory where temperature and/or heat transfer play an important role.
Design and accelerated testing of corrosion FOSs for reinforced concrete structures
Corrosion of steel reinforcement is the main pathology threatening the durability of civil engineering structures. Today, structures are mainly monitored by means of periodic visual inspections or even auscultation (corrosion potentials, ultrasonic measurements, core sampling, etc…), which are not very satisfactory. There is therefore a need for instrumentation capable of detecting the initiation and location of corrosion of reinforcement in concrete and ensuring long-term monitoring (several decades or more). In the context of Civil Engineering (CE) structures, Optical Frequency-Domain Reflectometry (OFDR) appears to be a suitable metrological solution because of its centimetre resolution and measurement range (70 metres in the standard version, i.e. several thousand measurement points along an optical fibre).
Content of work: The aim will be to adapt the design of this fibre optic sensor (FOS) to increase its durability and then to verify its applicability in the laboratory. Initially, the person recruited on a fixed-term research contract will be asked to work on the durability of the connexion between the optical fibre and the armature. Two different methods are envisaged: plasma torch spraying of ceramic powders and sol-gel. Both of these processes prevent the galvanic coupling because they involve insulating materials (ceramics) and are already deployed in industry in various civil and military fields. Secondly, test specimens equipped with the FOS will be tested in the laboratory according to classic civil engineering situations, i.e. localised corrosion (pitting induced by exposure to chloride ions) and uniform corrosion (generalised corrosion induced by carbonation of the embedding concrete). OFDR acquisitions will be carried out periodically over time in parallel with conventional metrology (potential, etc.).
Thermodynamic study of the Nb-O-Zr system for the nuclear fuel elements recycling
The first step of nuclear material recycling consists in a section-cutting process of the fuel assemblies leading to shells.
Nuclear materials in the cut sections are dissolved in acid solutions whilst structural as well as cladding materials are rinsed and then compacted in CSD-C containers for a final storage in CIGEO.
The REGAIN project aims at studying the feasibility of an alternative solution: the objective is to investigate the possibility to optimize the nuclear and cladding materials management by reducing the radiological source term. The idea is to proceed to a sequence of decontamination steps in order to minimize the waste volume: The first step consists in removing minor actinides and fission products and the second one in the separation of zirconium from structural activation products.
In order to feed the industrial process study, a part of the REGAIN project aims at collecting raw data, which will be used by the other work packages of the project.
In this framework, CEA proposes a post-doctoral position with the purpose of developing a thermodynamic database for the Nb-O-Zr system starting from literature data as well as using experimental informations obtained within the first stages of the project. It will be also possible to include a selection of key fission products into the existing database. The candidate may also be asked to complete the existing data by an experimental campaign to obtain a complete set of data for the modelling. The scientific approach will be based on the CALPHAD method: this method allows developing a thermodynamic database by the definition of an analytical formulation of the thermodynamic potential, which will be used to calculate phase diagrams as well as thermodynamic properties of multi-components systems.
Development of a simulation tool for the pitting process of a stainless steel used for the storage of nuclear waste
Structural nuclear waste is compacted in patties, stacked in a stainless steel container. In these compacting boxes are placed various metal-type materials with the addition of organic matter, including chlorinated waste. By radiolytic degradation, these can lead to the formation of hydrogen chloride HCl. During the storage phase, relative humidity may be present within the container, which, added to the HCl, may lead to a phenomenon of condensation, resulting, on the surface of the materials, of acid and concentrated into chloride ions condensates. In contact with this acid and chloride electrolyte, a pitting phenomenon is likely to begin on the surface of a stainless steel. This is a local phenomenon that can lead to the piercing of the material in extreme cases. The initiation of this phenomenon depends on several factors: the morphology of the electrolyte, its composition and its evolution over time.
If nowadays this phenomenon is well known, modeling it remains a major challenge because it is a coupled multi-physics and multi-parameter problem. Many questions remain open, particularly at the level of the physical and chemical laws to be used or how to represent the corrosion process?
The objective of the post-doctorate is to develop a tool under COMSOL capable of simulating the initiation and the evolution over time of a pit on the surface of a stainless steel. The approach will be based on a mechanistic modeling of the processes (material transport process and all the chemical and electrochemical reactions).
The post-doctorate will take place in several actions:
1- make a state of the art of the bibliography in order to understand the pitting phenomenon and to identify the laws necessary for modeling.
2-simulate the spread of the pit in a chloride environment in order to establish a propagation criterion.
3-the pitting initiation will be implemented in order to obtain a complete tool capable of simulating the pitting process
Separation microsystem coupled to mass spectrometry for on-line purification and characterisation of nuclear samples
The miniaturisation of analytical steps commonly carried out in laboratories offers many advantages and particularly in the nuclear sector, where the reduction of material consumption and waste production is of major interest. In this context, one of our laboratory’s focus area is the miniaturisation of analytical tools, particularly chromatographic separation techniques. The aim of this project is to reduce the scale of the purification steps of nuclear samples by solid phase extraction chromatography, prior to the analytical processes. Obtaining these miniaturised extraction devices is based on the in situ synthesis and anchoring of monoliths, in the channels of cyclic olefin copolymer (COC) microsystems. Since this material is chemically inert, COC functionalisation strategies are currently under development to covalently graft reactive sites on its surface, before locally anchoring actinide-specific monoliths in the micro-channels. The aim is to design and fabricate chromatographic extraction microsystems in COC, and to implement them for chemical purification and mass spectrometry measurements, both off-line and on-line.
Assessment of bimetallic bonds for molten salt nuclear reactors: understanding the microstructure-mechanical behavior relationships
Nickel-based alloys are the natural candidates for corrosion-resistant metallic materials, but their mechanical behavior and resistance to irradiation may not be satisfactory. To overcome this difficulty, the CEA with its partners in the ISAC project is developing generic solutions for bimetallic components that allow the surface of a reference material known for its good behavior under irradiation to be functionalized by a thick deposit of a nickel-based grade.
Two reference materials were selected for this study, the austenitic steel 316L(N) and the martensitic steel with phase transformation Fe-9Cr T91. The behavior of these two grades under irradiation is well known and controlled. The process used for deposition is thick TIG welding which is close to additive manufacturing methods. It has the advantage of being generic and simple to implement on very different parts (ferrules, plates, inside tubes ...). The purpose of this post doc is to evaluate the relevance of this bimetallic concept for MSR.
Thermodynamic investigation of Metal-Insulator-Transition materials – The case of doped VO2 for smart windows applications
The present post-doc proposal aims to develop a specific thermodynamic database on the V-O-TM (TM=Fe,Cr) system by using the CALPHAD approach. The candidate will conduct experimental campaigns to obtain relevant data to feed the thermodynamic models. The candidate will mostly use the experimental equipment available at the lab (DTA, annealing furnaces, high temperature mass spectrometry, laser heating, SEM-EDS). In addition, the post-doc may participate to combinatorial high-throughput activities led by other laboratory of the Hiway-2-Mat consortium (e.g., ICMCB in Bordeaux), allowing a better connection between the CALPHAD simulation output and the accelerated characterization platform. The thermodynamic database will be then included in the autonomous research routine implemented in the material exploration path.
Automatic machine learning identification of nanoscale features in transmission electron microscopy images
Imaging nanoscale features using transmission electron microscopy (TEM) is key to predicting and assessing the mechanical behaviour of structural materials in nuclear reactors or in the fields of nanotechnology. These features, visible by phase contrast (nanobubbles) or diffraction contrast (dislocation loops or coherent precipitates), are prime candidates for automation. Analysing these micrographs manually is often tedious, time-consuming, non-universal and somehow subjective.
In this project, the objective is to develop a Python-based framework for data treatment of transmission electron microscopy (TEM) images.
Machine Learning approaches will be implemented in order to tackle the following tasks:
- Data collection: The success of any machine learning approach is linked to the database quality. In this project, a huge database is available. Four microscopists are involved in the project and will continuously enrich the database with images containing easily recognizable features.
- Denoising and finding the defect contour both through existing open-access software and in-house developed descriptors. Representative ROI (region-of-Interest) will be generated on images.
- Design of the Convolutional Neural Network (CNN) Architecture and model training: A collective feature map will be generated for the entire images in order to identify some representatives ROI. Each ROI is then overlaid to the original feature map and is passed to the CNN for individual region classifications. Secondly, recent advances in image segmentation will be placed in the core engine of the workflow.
- Model performance metrics: The aim is to reach a compromise between the training time and the detector performance.
The process will be applied to nanometer-sized features formed under irradiation in nuclear oriented materials (Co-free high entropy alloys (HEA), UO2) and precipitates in materials with a technological interest (coherent Cr precipitates in Cu).