Modeling of faults on low voltage DC networks in buildings, towards fault detection algorithms
The development of the use of renewable energies and energy storage as well as the progress made by power electronic components are gradually leading to a rethinking of the architectures of low voltage electrical distribution networks in buildings. These developments will allow the development of direct current or mixed alternating-direct current networks supplied by static converters. On this type of network, faults become more difficult to manage due to the power sources used. Indeed, the usual signatures of the short-circuit or the overload are no longer the same and will vary according to the converters used and the architecture of the network. For this, it is necessary to identify, by simulation, the most suitable protection topologies (by neutral systems for example) and to identify the typical fault signatures. Ultimately, these signatures will provide optimum detection devices.
HPC simulations for PEM fuel cells
The goal is to improve TRUST-FC software -a joint development between LITEN and DES institutes at CEA- for detailed full 3D simulation of hydrogene PEM fuel cells and to run simulations on whole real bipolar plate geometries. Funded by AIDAS virtual lab (CEA/Forshungs Zentrum Juelich), a fully coupled electro-chemical, fluidic and thermal model has been built, based on CEA software TRUST. The model has been benchmarked against its FZJ counterpart (Open fuelcell, based on OpenFoam). The candidate will adapt the software and toolchain to larger and larger meshes up to billion cells meshes required to model a full bipolar plate. Besides, he will introduce two phase flow models in order to address the current technological challenges (local flooding or dryout). This ambitious project is actively supported by close collaboration with CEA/DES and FZJ.
High efficiency silicon cell irradiations for space
Historically, photovoltaics was developed in conjunction with the growth of space exploration. During the 90's, III-V multi-junction solar cells were progressively replaced silicon, for their superior performance & radiation hardness. Today, the context is favorable to a revival of space Si: increasing PV power needs, missions with moderate durations & constraints (LEO), very low cost & high performance terrestrial Si cells (p-type > 26% AM1.5g). However, for Si cells, conventional irradiation ageing methods & sequences (ECSS) are less appropriate. As the literature mainly comes from 80s - 90s, it is necessary to revisit the topic for the latest generation of passivated contacts Si cells (developed at CEA INES) and the unique double beam irradiation facilities of JANNuS platform - CEA Saclay.
This work is part of the SiNRJs project, at the interface between two CEA departments, dealing with space photovoltaics & materials irradiation. The scientific & technological approach adopted: 1. fabrication of passivated contact Si cells (HeT and/or Poly-Si) 2. Si cells optoelectronic characterizations before irradiation (IV AM1.5/AM0, EQE, etc.) 3. Cells & samples proton irradiations, in situ characterizations (Raman & El) 4. Ex situ characterizations after irradiations (IV AM1.5/AM0, EQE, etc) 5. Results analysis and synthesis. From a scientific point of view, the key issues to be addressed concern the understanding of the mechanisms/dynamics of defect creation/healing under this double electronic and ballistic excitation.
Development of artificial intelligence algorithms for narrow-band localization
Narrowband (NB) radio signals are widely used in the context of low power, wide area (LPWA) networks, which are one of the key components of the Internet-of-Things (NB-IoT). However, because of their limited bandwidth, such signals are not well suited for accurate localization, especially when used in a complex environment like high buildings areas or urban canyons, which create signals reflections and obstructions. One approach to overcome these difficulties is to use a 3D model of the city and its buildings in order to better predict the signal propagation. Because this modelling is very complex, state-of-the art localization algorithms cannot handle it efficiently and new techniques based on machine learning and artificial intelligence should be considered to solve this very hard problem. The LCOI laboratory has deployed a NB-IoT network in the city of Grenoble and is currently building a very large database to support these studies.
Based on an analysis of the existing literature and using the knowledge acquired in the LCOI laboratory, the researcher will
- Contribute and supervise the current data collection.
- Exploit existing database to perform statistical analysis and modelling of NB-IoT signal propagation in various environments.
- Develop a toolchain to simulate signal propagation using 3D topology.
- Refine existing performance bounds through a more accurate signal modelling.
- Develop and implement real-time as well as off line AI-based localization algorithms using 3D topology.
- Evaluate and compare developed algorithms with respect to SoTA algorithms.
- Contribute to collaborative or industrial projects through this research work.
- Publish research papers in high quality journals and conference proceedings.
Design and fabrication of the magnetic control of 1.000 qubits arrays
Quantum computing is nowadays a strong field of research at CEA-LETI and in numerous institutes and companies around the world. In particular, RF magnetic fields allow to control the spin of silicon qubits, and pathway for large scale control is a real technological challenge.
The bibliographic analysis and the studies already carried out will able to draw out the pros and cons of the various existing solutions. In collaboration with integration, simulation and design staff, a proof of concept will be develloped and fabricated.
Auto-adaptive neural decoder for clinical brain-spine interfacing
CEA/LETI/CLINATEC invite applications for postdoctoral position to work on the HORIZON-EIC project. The project goal is to explore novel solutions for functional rehabilitation and/or compensation for people with sever motor disabilities using auto-adaptive Brain-Machine Interface (BMI) / neuroprosthetics. Neuroprosthetics record, and decode brain neuronal signal for activating effectors (exoskeleton, implantable spinal cord stimulator etc.) directly without physiological neural control command pass way interrupted by spinal cord injury. A set of algorithms to decode neuronal activity recorded at the level of the cerebral cortex (Electrocorticogram) using chronic WIMAGINE implants were developed at CLINATEC and tested in the frame of 2 clinical research protocols in tetraplegics in Grenoble and in paraplegics in Lausanne. The postdoctoral fellow will contribute to the next highly ambitious scientific breakthroughs addressing the medical needs of patients. The crucial improvement of usability may be achieved by alleviating the need of constant BMI decoder recalibration introducing an auto-adaptive framework to train the decoder in an adaptive manner during the neuroprosthetics self-directed use. Auto-adaptive BMI (A-BMI) adds a supplementary loop evaluating from neuronal data the level of coherence between user’s intended motions and effector actions. It may provide BMI task information (labels) to the data registered during the neuroprosthetics self-directed use to be employed for BMI decoder real-time update. Innovative A-BMI neural decoder will be explored and tested offline and in real-time in ongoing clinical trials.
Development of a digital twin of complex processes
The current emergence of new digital technologies is opening up new opportunities for industry, making production more efficient, safer, more flexible and more reliable than ever. The application of these technologies to the vitrification processes could improve the knowledge of the processes, optimise their operation, train operators, help with predictive maintenance and assist in the management of the process.
The SOSIE project aims at providing a first proof of concept for the implementation of digital technologies in the field of vitrification processes, by integrating virtual reality, augmented reality, IoT (Internet of Things) and Artificial Intelligence.
This project, carried out in collaboration between the CEA and the SME GAMBI-M, is a READYNOV project. GAMBI-M is a company specialised in the reconstruction of complex environments and in digital engineering. The work will be carried out in close collaboration with the CEA teams developing the vitrification processes for nuclear waste.
The project consists of developing a digital twin of 2 vitrification processes, and will be implemented on 2 platforms in parallel, one in a conventional zone, the other in a high activity zone. The first step will be to develop a visual digital twin, the virtual 3D model of each cell, which will allow the user to visit the cells and access any point virtually. Based on this reconstructed model, an "augmented" twin will be developed and connected to the supervisory controller. Finally, the last step will be to develop the "intelligent twin" by exploiting existing databases on the operation of the process. By training machine learning algorithms on these data, a predictive model of nominal operation will be generated.
Publications are expected on the implementation of virtual reality and augmented reality tools on shielded chain operations, as well as on the development of deep learning methods for the assistance to the control of such complex processes.
Decentralized Solar Charging System for Sustainable Mobility in rural Africa
A novel stand-alone solar charging station (SASCS) will be deployed of in Ethiopia. Seeing as 45% of Sub-Saharian Africa’s population lacks direct access to electricity grids and seeing as the the infrastructure necessary to reliably harness other energy sources is largely non-existent for many such populations in Ethiopia, introducing the SASCS among some of the country’s rural communities is a necessary effort. It could ostensibly invigorate communities’ agricultural sector and support those whose employment is rooted in farming. A SASCS could also serve to integrate renewable energy within the country’s existing electricity mix. CEA INES will act as a consulting Partner for the design and implementation of the solution (second life batteries, solar will be investigated). In addition, because of CEA INES’s established expertise in the installation of solar tools within various communities, the initiative will also provide know-how for the installation of the SolChargE in Ethiopia as well as cooperate on workshops for students and technicians employed by the project.
Development of large area substrates for power electronics
Improving the performance of power electronics components is a major challenge for reducing our energy consumption. Diamond appears as the ultimate candidate for power electronics. However, the small dimensions and the price of the substrates are obstacles to the use of this material. The main objective of the work is to overcome these two difficulties by slicing the samples into thin layers by SmartCut™ and by tiling these thin layers to obtain substrates compatible with microelectronics.
For this, various experiments will be carried out in a clean room. Firstly, the SmartCut™ process must be made more reliable. Characterizations such as optical microscopy, AFM, SEM, Raman, XPS, electrical, etc. will be carried out in order to better understand the mechanisms involved in this process.
The candidate might be required to work on other wide-gap materials studied in the laboratory such as GaN and SiC, which will allow him to have a broader view of substrates for power electronics.