Multiscale Modeling of the Degradation Mechanisms in Polymer Electrolyte Fuel Cells
In an attempt to provide a rigorous physical-based description of the physicochemical phenomena occurring in the PEFC environments, the Modeling Group at CEA-Grenoble/LCPEM has developed a novel physical multi-scale theory of the PEFC electrodes electro-catalysis,the MEMEPhys model, based on a combined non-equilibrium thermodynamics/electrodynamics approach. This postdoctoral research position will consist on actively contributing on the development of the model, including the implementation of a physical-based description of water transport phenomena and water condensation in the PEFC. Heterogeneities on the electrochemical and aging processes, induced by water transport, will be in particular addressed. The candidate will strongly combine theoretical and experimental data, obtained in our laboratory, in order to establish MEA microstructure-performance relationships and to elucidate the main MEA degradation and failure mechanisms. From a fundamental point of view, this work will provide a deeper understanding of the electrochemical mechanisms responsible of the PEFC active layers aging at different spatiotemporal scales.
Study and realization of thermal energy harvesting prototypes by thermal/fluidic coupling, and then electrical conversion. Application to electronic circuits.
The objective of this study is to explore possibilities of using systems with fluidic/thermal coupling to harvest the thermal energy released by an electronic device and then convert it into electricity that can be stored or used again. In those systems, the fluidic can be also used for a cooling purpose.
The two main steps will be the design of devices allowing controlling the operating regimes of the fluidic system submitted to a constant heat source (thermo-fluidic coupling) and the characterization of the best coupling conditions with the electrical conversion devices, in particular piezo-electrical. The studies will also explore new mechanisms taking place in the small scale fluidic systems compared to models known macroscopically. The work will be mostly experimental but will also include a simulation part.
The study should also provide an estimation of the harvesting efficiency as well as the power densities taking place in this kind of new devices.
Process evaluation of 3rd generation biofuel production from micro-algae
CEA contributes to R&D activities in 3rd generation biofuel production from micro-algae by its fundamental research in biology (understanding of biological mechanism and improvement of microorganism performances) led by DSV at CEA Cadarache. LITEN Institute, belonging to CEA/DRT, investigates 2nd biofuel generation, from studies on resources (biomass, waste) up to industrial, economical and environmental integration.
This post doc fellow will use the different approaches developed at LITEN/DTBH to :
- perform a prospective study on process integration, for biofuel production from micro-algae,
- realize a technico-economical study of the more promising process solutions in the 2rd generation domain and industrial use of micro-algae,
- estimate the environmental impact (especially CO2) of these processes.
This work will take place in in frame of a collaboration of both labs (DSV/IBEB and DRT/LITEN/DTBH), the first one bringing its very fundamental knowledge on technical ability and performance of the micro-organism, the second one giving the knowledge on process and technico-economical evaluation of industrial reactor systems.
The post doc fellow, located in Grenoble, will go as needed in Cadarache to discuss with biology experts.
Synthesis and characterization of amino-phosphorous ligands for extraction of uranium in a sulfuric medium with a “liquid / liquid” process
The development of new and more effective extractants than those currently used is therefore an important issue for the mining of uranium. In particular, access to specific chelating systems with high affinity for uranium with selective properties in regards to competitor’s ions and less susceptible to hydrolysis remains a challenge.
Recently, new bifunctional molecules amio-phosphine oxide type has shown their potential for the extraction of uranyl in sulfuric media with excellent properties in terms of affinity and selectivity for the metal.
The objective of this postdoctoral fellowship will be to optimize this family of ligands, with the development of concise and efficient routes for their chemical and suitable for the preparation of large quantities of extractant for further study the optimization of the process.
Post-doc: CNN neural network – managing data uncertainty in the learning database.
The aim is to develop algorithms able to take into account the uncertainty in the learning database of neural networks. The project fits into the context of the dynamic state estimation of liquid-liquid extraction and benefits of its knowledge-based simulator as well as industrial data. Indeed, the status of an industrial chemical process is accessible through operating parameters and available monitoring measures. However, the measures being inherently associated with uncertainty, it is necessary to make the data consistent with process knowledge. Therefore, the goal is to find the best data set of operational parameters (input of the knowledge-based simulator) to provide the model to estimate the real process state known through monitoring measures (output of the knowledge-based simulator). A convolutional neural network (CNN) is being developed in another postdoctoral project to solve the inverse problem to find the best input thanks to the measured output. A consistent set of operating parameters is going to be obtained and state of the process is going to be known during the dynamic regime of the liquid-liquid extraction process. This first step is to evaluate the impact of the uncertainty of operational parameters on the outputs of the knowledge-based model. This step will need to connect the knowledge-based model to URANIE, internal platform developed by CEA ISAS. This knowledge must be taken into account in the second part of the project. The uncertainty observed on the outputs should be taken into account in the learning loop to improve the estimation of the operational parameters by the CNN. The impact of these uncertainties on the CNN computed results must be assesed in order to trust the ability of the CNN to estimate the state of the process.
Through this project, we are at the heart of the thematic of digital simulation for the best control of complex systems.
Optimal Multi Agent System management of smart heat grid using thermal storage
The aim of this work is a major contribution to a software framework based on coupling of Modelica/Jade environments that will allow to model, to simulate and to optimise the control of smart heat grid through dedicated thermal storage models development: interface specification to control the storages in the grid, simplified models design of heat grid’s most crucial components to be integrated in Agents (production, distribution/storage, consumption) and design of consumption and production forecast models in order to manage anticipation and improve the overall efficiency. The evaluation of performance is based on the test case build in Modelica simulation environment.
Multiscale approach of f elements aqueous solutions modeling
A post-doctoral position is available for one year at CEA-Marcoule
The study will be the modeling of concentrated aqueous phases of heavy metal salts using both microscopic and mesoscopic modeling.
Separation processes for heavy metals recycling usually use liquid-liquid extraction with the transfer of ionic species from a concentrated aqueous phase to an organized organic phase.
This post-doctoral research subject relates to the chemical properties of these processes, and especially to the characterization of the aqueous phase using as accurate as possible models. The goal is to understand the various effects (solvation, electrostatic and van der waals forces, entropy…) influencing the structural and energetic properties of these solutions. A multi-scale approach will be used to study some systems of interest for both fundamental and industrial point of view, the aim being the characterization of these solutions from their molecular structure to their thermodynamic properties. The tools and the approach used here have to be be valid for separative chemistry overall.
Modelling of interstitial cluster evolution in body-centered cubic metals after helium implantation
Under irradiation, structural materials inside nuclear reactors undergo changes in mechanical properties, which result from the formation of point defect clusters, such as cavities (clusters of vacancies) and interstitial dislocation loops (clusters of self-interstitial atoms). Understanding the formation processes of such clusters is thus of prime importance. Recently, three-dimensional interstitial clusters, known as C15 clusters, have been shown theoretically to be highly stable in iron. In order to detect such clusters experimentally, an idea is to make them grow, as shown for dislocation loops after helium implantation. This approach will be carried out experimentally in various bcc metals in the framework of the ANR project EPigRAPH, in collaboration with Chimie ParisTech, GEMaC and LPS.
In this project, the following modelling tasks will be performed by the postdoc:
- Electronic structure calculations will be done to obtain the energetic properties of point defects and point defect clusters in the bcc metals envisaged in the project.
- These data will then be used to parameterize a kinetic model based on cluster dynamics. This formalism is particularly well adapted to simulate the evolution of point defect clusters over long physical times.
Fabrication and characterization of high thermal conductivity SiCf/SiC composites
SiCf/SiC ceramic matrix composites are foreseen candidates for structure materials and claddings in fast neutron reactor of 4th generation. However, their use may be limited because of their too low thermal conductivity in the operating conditions (< 10 W/mK).
SiCf/SiC ceramic matrix composites are now elaborated by chemical vapour infiltration (CVI). In order to improve their thermal conductivity (reduced porosity), it is planned to develop a hybrid elaboration process combining CVI and liquid routes.
The objective of this study is to determine the conditions of elaboration of a SiC matrix by liquid routes and then to characterize the thermo-mechanical behaviour of the hybrid composites, particularly in relation to CVI references.
Large-area processing and design of functional piezoelectric nanomaterials for flexible sensors and systems
CEA LETI develops innovative highly flexible strain sensors which exploit the piezoelectric properties of self-organized gallium nitride nanowires. The fabrication steps are basically: i) nanowire growth, ii) nanowire assembly, iii) encapsulation, iv) contacting. First demonstrators with small active area (1.5 cm²) have already been achieved using the Langmuir Blodgett (LB) technique for the assembly of nanowires. The present project is concerned with the scaling-up of the assembly process over large surface areas, as well as controlled patterning of nanowire assemblies in 1D and 2D by using an innovative CEA LITEN roll-to-roll technology called Boostream® which has the same functionalities as LB in its basic function.
The aim of the post doc is to develop a new building block for the Boostream® equipment enabling a controlled assembly of wires with a pre-defined design. The candidate will carry out studies to optimize the wire assembly, develop the process of film patterning and fabricate, integrate and characterize GaN nanowire piezoelectric transducers with dimensions of 15x15 cm².
More generally, this post doc will also provide the opportunity to develop a generic knowledge to manipulate micro or nano wires or fibers giving new solutions in various fields such as surface structuration, electronic skin, energy...