Preparation and optimization of HiPIMS PVD coatings for corrosion protection of nickel alloys in molten chloride environments
The aim of this post-doctoral project is to demonstrate the effectiveness and performance of various materials - metals, oxides and ceramics - that can be used as coatings on nickel alloys intended for the construction of a molten-salt reactor. Coatings deposited by HiPIMS PVD will be subjected to microscopic and structural characterization to optimize deposition parameters. Corrosion experiments in a molten-salt environment will then be used to verify the performance of these coatings, and to identify degradation mechanisms to remedy them. The most promising compositions can be optimized by the addition of minor elements, a development involving multi-target PVD devices.
In situ analysis of dislocations with Molecular Dynamics
Thanks to new supercomputer architectures, classical molecular dynamics (MD) simulations will soon reach the scale of a trillion atoms. These unprecedentedly large simulation systems will thus be capable of representing metal plasticity at the micron scale. Such simulations generate an enormous amount of data, and the challenge now lies in processing them to extract statistically relevant features for the mesoscale plasticity models (continuous-scale models).
The evolution of a material is complex as it depends on extended crystal defect lines (dislocations), whose dynamics are governed by numerous mechanisms. To feed higher-scale models, the key quantities to extract are the velocities and lengths of dislocations, as well as their evolution over time. These data can be extracted using specific post-processing techniques based on local environment characterization ('distortion score' [Goryaeva_2020], 'local deformation' [Lafourcade_2018], ‘DXA’ [Stukowski_2012]). However, these methods remain computationally expensive and do not allow for in situ processing.
We have recently developed a robust method for real-time identification of crystalline structures [Lafourcade_2023], which will soon be extended to dislocation classification. The objective of this postdoctoral project is to develop a complete analysis pipeline leading to the in situ identification of dislocations in atomic-scale simulations and their extraction in a nodal representation.
Distributed fiber optic sensor for hydrogen leak detection
Hydrogen technologies are among the most promising low-carbon energies, and it fits perfectly the context of ecological transition. Carbon-free hydrogen represents a greener and more sustainable alternative to the batteries currently used for energy storage. There is a huge interest in optimizing the procedure for hydrogen production, use, and storage. This subject represents a particular interest for the CEA, EDF, and ORANO through several projects such as PEPR-H2 and udd@Orano. However, only a few works are carried out within the safety improvement framework of this energy production, transport, and use structures of this energy. Hydrogen leaks can represent a very high risk of a serious accident. In this project, we bring together several CEA departments expertise to develop a new hydrogen leak detection technology that can meet these major challenges. The combination of a simple chemical reaction (exothermic reaction) with distributed fiber optic sensors allows the creation of a new generation of hybrid sensors. These sensors use a reagent that heats up in the presence of hydrogen, leading to a temperature rise, which can be detected easily using an optical fiber distributed sensor. This measurement is characterized by high precision (can measure temperature variations of about 0.5 °C) with spatial resolutions that can reach the millimeter. These sensors will allow the monitoring of production lines, transport circuits, storage containers, etc., and provide real-time information on any containment system failure. Which will allow the localization of leaks with greater precision than the existing sensors. The low energy input (a few mW) and the absence of electronics reduce the risk of sparking, which makes the sensor functional and safe, even in the presence of high concentrations of hydrogen.
Adapting the Delayed Hydride Cracking (DHC) experience to irradiated materials
The objective of this study is to nuclearize the Delayed Hydride Cracking (DHC) experiment developed as part of Pierrick FRANCOIS PhD research (2020-2023). This experiment enables the reproduction of the DHC phenomenon in Zircaloy cladding under laboratory conditions to determine the material's fracture toughness in case of DHC: KI_DHC.
The term "nuclearize" refers to the adaptation of the experiment to test irradiated materials within dedicated shielded enclosures (called hot cells), where materials are handled using remote manipulators. The experimental protocols described in Pierrick FRANCOIS' thesis must therefore be modified, and ideally simplified, to allow for their implementation in hot cells. This will require close collaboration with the personnel responsible for the tests and the use of numerical simulation tools developed during the same PhD research.
The development of this hot cell procedure will be used by the postdoctoral researcher to assess the risk of HC during dry storage of spent fuel assemblies by quantifying the fracture toughness of irradiated claddings.
Ageing battery analysis with internal multi-sensors analysis: development of sensors and operando measurements
CEA and CNRS collège de France lead the PEPR Batterie , a French National project with the objective to achieve the European Roadmap from Battery2030+ for the development of “smart battery”. The Sensiga project is a part of PEPR Battery. This project aims to develop new sensors technologies for monitoring the critical parameters of the Lithium ion cells during cycling to improve performances, safety and ageing. This new sensors technology will increase the knowledge of the internal physical, chemical and electrochemical process occurs in the cell. The large amount of data measuring operando will be used to developing new algorithm and strategies to improve the battery management systems. In the context of the Sensiga project, doctoral and postdoctoral position was open at CEA for working on this topic with a multidisciplinary and laboratory team. The aims of the work is to developing new sensing technology for in-situ and operando monitoring of the cell. The candidate will integrate a team specialized in the development of specific sensors for Lithium ion battery in the Laboratory of Postmortem Analysis and Security at CEA Grenoble.
The scope of the position will focus on the development of optical fibre sensors, the integration of theses sensors inside pouch cell and performing electrochemistry test for ageing study. This kind of sensors will be used to monitor the internal temperature, strain, and pressure and lithium concentration. The second type of sensors using in the project is the reference electrode using for electrode potential measurement. These data’s are crucial to access to the degradation mechanisms of each materials in operando. The candidate will be participate to the ageing test campaign and to the post-mortem analysis. This analysis compare to the ageing test will be used to identify the degradation mechanisms and correlate it to the sensors signal. The candidate will integrate a multidisciplinary team.
Aerosol generation and transformation mechanisms during the fuel debris cutting at Fukushima Daiichi future dismantling
During Fukushima Daiichi nuclear reactor accident, several hundred tons of fuel debris (the mixture generated by the reactor core melting and its interaction with structural materials) have been formed. Japanese government plans to dismantle with 30 to 40 years Fukushima Daiichi nuclear power station, which implies recovering these fuel debris that are there. CEA is part to several projects aiming at mastering the risks due to aerosols generated during fuel debris cutting.
The post-doctoral work objective is to exploit the large experimental database created thanks to these projects in order to study the generation and transformation mechanisms of these cutting aerosols for both thermal and mechanical cutting. An important source of aerosol seems to be partial evaporation/condensation, close to fractional distillation. A thermodynamic modelling shall be proposed, coupled with some kinetic effects. For mechanical cutting, aerosol analyses shall be compared to fuel debris block microstructure to quantify a preferential release of some phases.
After a bibliographic study, a synthesis of the experimental results will be carried out and completed, where necessary, by chemical or crystallographic analyses. The aim will be to propose a modelling of these aerosol generation and transformation mechanisms.
The postdoctoral researcher will work within an experimental laboratory of about 20 staff within CEA IRESNE institute (Cadarache site, Southern France).
Postdoc in Advanced Fault-Tolerant Control for Fuel Cell Durability Enhancement
Fuel cells are a key technology for clean and sustainable energy systems, particularly in hybrid configurations for transport and stationary applications. However, their durability under real-world conditions remains a critical challenge. This project aims to address these challenges by exploring advanced control strategies that leverage state-of-the-art prognostic algorithms for fuel cell health assessment.
This postdoc offer focalizes on Advanced Control and Optimization, specifically the design of Fault-Tolerant Control (FTC).
Building on prior work in machine learning-based prognostics for fuel cell health, the focus of this project is to develop methods to utilize this prognostic information to optimize the operation of the fuel cell system.
By incorporating both model-based, data-driven approaches and testing on real test-bench platforms, the project aims to create robust, deployable solutions that enhance fuel cell durability while reducing the complexity and cost of implementation.
Development of an innovative way of end-of-life plastics recycling by hydrothermal depolymerization
End of life plastics are scarcely recycled due to technical, health or structural constraints. To address this issue, a solvolysis route may be considered in order to recover monomers or other valuable molecules. Although good results are obtained after polymers sorting, this method remains sensitive to the composition of incoming flows, as well as the presence of contaminants. The Supercritical and Decontamination Processes Laboratory has developed an original depolymerization method in hydrothermal conditions (150 to 300°C and autogenous pressure) allowing to consider treatment of a mixture of end of life polymers (PET, PU, PC, PE, PVC). A parametric study will be carried out on a mixture of polymers of known composition by studying the influence of process parameters on the composition of the aqueous and organic phases, to define performance criteria such as conversion and depolymerization yields. Several end-of-life plastic wastes, alone or in a mixture, will be considered, to highlight a possible synergistic effect on the recovery of all or part of the recoverable monomers or products. Finally, an energy and mass balance will be implemented to study the complete life cycle of the process and to evaluate the relevance of the depolymerization process in hydrothermal conditions.
Numerical performance and sensitivity of the thermo-hygro-corrosive model of the French underground radioactive waste storage tunnels
In recent years, a multiphysics model that represents the complex physical phenomena that influence the accumulation of rust in the storage tunnels (alveoli) has been numerically implemented in the finite element method (FEM) software Cast3M. Seeking to estimate the thermo-hygro-corrosive properties of the alveoli at long time scales, recent (and ongoing) works on improving the execution time of the resolution algorithm were undertaken. However, for a FEM numerical model to be considered as a rigorous engineering/scientific tool, error bars must be associated to all computational results; therefore, the careful quantification of the plethora of modeling uncertainties is primordial. To undertake such an endeavor, multiple issues must first be tackled, begininning with an improvement of the physical representativity of the multiphysics model, following with an improvement of the computational performance of the numerical model, and ending with rigorous sensitivity studies of the implemented model. Work on computational performance is necessary, so as to render program execution fast enough to ensure that the large sets of numerical experiments required run in reasonable times.
Conception and deployment of innovative optimal control strategies for smart energy grids
District heating networks (DHNs) play a vital role in energy transition strategies due to their ability to integrate renewable and waste heat effectively. In France, the national low-carbon strategy emphasizes expanding and optimizing DHNs, including smaller networks with multiple heat sources like solar thermal and storage. Smart control systems, such as model-predictive control (MPC), aim to replace manual, expert-based practices to enhance efficiency. However, deploying advanced control systems on small DHNs remains challenging due to the cost and complexity of hardware and maintenance requirements.
Current industrial solutions for large DHNs leverage mixed-integer linear programming (MILP) for real-time optimization, while smaller networks often rely on rule-based systems. Research efforts focus on simplifying MPC models, utilizing offline pre-calculations, or incorporating machine learning to reduce complexity. Comparative studies assess various control strategies for adaptability, interpretability, and operational performance.
This postdoctoral project aims to advance DHN control strategies by developing, testing, and deploying innovative approaches on a real DHN experimental site. It involves creating and comparing control models, implementing them in a physical simulator, and deploying the most promising solutions. Objectives include optimizing operational costs, improving system robustness, and simplifying deployment while disseminating findings through conferences, publications, and potential patents. The researcher will have access to cutting-edge tools, computational resources, and experimental facilities.