Automatic driving of a finite element software based upon a domain decomposition strategy. Application to ultrasonic non-destructive testing.
One the most important field of activity at the DISC (Department of Imaging and Simulation for Control) of CEA - LIST is to provide a comprehensive set of tools for modeling and simulation for Non-Destructive Testing (NDT). These tools are gathered within the computational platform CIVA. Most of the ultrasound models -- elaborated by the LSMA (research laboratory for Simulation and Modeling in Acoustics) -- are based upon semi-analytical methods. Although very efficient, these methods suffer from a loss of precision as soon as some critical phenomena (e.g. head waves or caustics) or some particular features of the material (e.g. flaws or heterogeneities ) appear in the control experiment. To circumvent these limitations, one of the field of research in the LSMA is to build coupling schemes between semi-analytical and numerical methods. Following this strategy, a computational software based upon high-order finite elements combined with domain decomposition strategies is developped in order to address 3D configurations. The work proposed here focuses on increasing the complexity of the configurations reachable within this coupling strategy. A typical example being the fluid-structure interaction in the case of flaws reaching the bottom of the material to control.
Simulation of silicon solar cells based on n-type material : modelling and architecture optimisation.
INES is actually developping new fabrication technologies for n-type silicon solar cells. Working on simulation of photovoltaic solar cells enables the speed-up of the developement of new technologies: physical interpretation of characterisation results, support to device design, optimisation of processing steps and evaluation of original designs.
This subject open for post-doc position is focused on the study of semi-empirical models for materials and process steps for n-type solar cells. These basic road-blocks will be assembled in a complete model by using a multi-scale simulation tool. In the end, this global model will allow optimising of the p-type emitter geometrical structure, the efficiency of carrier collection on the back side or the geometry of metallisation for electrical contacts.
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.
Global offshore wind turbines monitoring using low cost devices and simplified deployment methods
This project follows previous work focused on on-shore wind turbine instrumentation with inertial sensors networks whose dataflows allows the detection of vibration modes specific to the wind turbine components, in particular the mast and the real-time monitoring of these signals.
The objectives of this project are manyfolds: to bring this work to offshore wind turbines; search for signatures in wider frequency bands; study the behavior of offshore platforms and their anchorages.
One of the challenges is to find the signatures of rotating elements (blades) without direct instrumentation. Instrumentation of these elements is indeed more expensive and more impacting on the structure.
In addition, the sensor technology will be suitable for monitoring the fatigue life cycle of moving wire structures (dynamic electrical connection cable and anchoring) in the case of an off-shore wind turbine. The ultimate goal is to propose a global method for offshore wind turbine health monitoring.
Sizing and control optimisation of a hydrogen production system coupled with an offshore wind farm
Coupling MRE (Marine Renewable Energy) and hydrogen sectors reveal an important potential long-term assets. The MHyWind project suggests to estimate the energetic and economic potential of a hydrogen production system integrated into a substation of an offshore wind farm. The hydrogen produced and stored locally will be distributed by boat for harbour uses, as a replacement of fossil fuels. For that purpose, it will be organized a simulation which will integrate all the energy chain towards the harbour uses of hydrogen. It will allow to estimate various configurations and sizing according to the local uses, valuation leverages, control modes and behavior of the system. The criteria will be the producible (kg of H2 producted and used) and complet costs (CAPEX and OPEX). The objective of the postdoctoral student will be to develop the simulation tool on this applicative being fully integrated with the teams of concerned laboratories.
Numerical Meta-modelization based study of the propagation of ultrasonic waves in piping system with corroded area
The aim of the ANR project PYRAMID (http://www.agence-nationale-recherche.fr/Projet-ANR-17-CE08-0046) is to develop some technics of detection and quantification of the wall thinning due to flow accelerated corrosion in piping system. In the framework of this project involving French and Japanese laboratories, CEA LIST develops new numerical tools based on finite elements dedicated to the modelling of an ultrasonic guided wave diffracted by the corrosion in an elbow pipe. These solutions support the design of an inspection process based on electromagnetic-acoustic transduction (EMAT). To this end, the ability of CEA LIST to adapt meta-modeling tools of its physical models will be the key asset to allow intensive use of the simulation.
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.
Deployment of distributed consensus protocols on blockchains with Smart Contract
The aim is to implement various distributed consensus protocols on both public and private blockchain platforms supporting Smart Contracts technology. The techniques based on Proof-of-Stake and token management will be analyzed and their level of security will be evaluated in terms of energy consumption and quality of the distribution of the trust in the system. The techniques to verify the transactions of the blockchain Ethereum will be implemented, as well as other algorithms, lighter and that consume less energy, dedicated to "private" blockchains where users are authenticated. The platform Hyperledger will be used to test the various distributed consensus protocols. New algorithms will be proposed and the solutions will be deployed for applications in the field of the Internet of Things.
Machine learning technics and knowledge-based simulator combined for dynamic process state estimation
This project aims to estimate the real state of a dynamic process for liquid-liquid extraction through the real data record. Data of this kind are uncertain due to exogenous variables. They are not included inside the simulator PAREX+ dedicated to the dynamic process. So, the first part of the project is to collect data from simulator. By this way the operational domain should be well covered and the dynamic response recorded. Then, the project focuses to solve the inverse problem by using convolutionnal neural networks on times series. Maybe a data enrichment could be necessary to perfect zones and improve estimations. Finally, the CNN will be tested on real data and integrate the uncertainty inside its estimations.
At the end, the model built needs to be used in operational conditions to help diagnosis and improve the real-time control to ensure that the dynamic observed is the one needed.
Correlative X-ray and ToF-SIMS tomography Data fusion of 3-D data sets from X-ray and ToF-SIMS tomography
The nanocaracterisation platform of the CEA Grenoble has recently installed 2 state-of-the-art tools for 3-D imaging with 100 nm resolution: X-ray tomography in a SEM and time of flight secondary ion mass spectrometry (ToF-SIMS) assisted by focused ion milling (FIB). X-ray tomography delivers non-invasive 3-D images of the internal morphology of an object whilst ToF-SIMS is able to map the local composition in 3-D. We aim to combine the two techniques to perform quantitative 3-D investigations of objects such as copper pillars for microelectronics or silicon electrodes for Li battery applications.
The proposed research subject is data analysis orientated. Some simulation work may be performed to implement and test existing 3-D data fusion methods with a view to adapting and improving them. The candidate will assist with the experimental measurements and be responsible for treating the data with the chosen protocols. The candidate should be pragmatic, at ease with applied mathematics and have good programming skills. These will be essential in understanding and manipulating the fusion and reconstruction algorithms, from the simplest, to the increasingly advanced (prior information, superiorisation, Bayesian fusion)
The candidate will have completed a PhD in physics and have good computer (Python, Matlab, C) and image treatment skills, or a PhD in mathematics/computational science with an interest in applications. The candiate will need to interface with a multidisciplinary team, and be receptive to new ideas. The candidate will be proficient in both written and spoken English in order to communicate with the team and to disseminate their results in articles or at conferences.