Optimal Design of Hybrid Solar Heat and Power Systems for Industrial Processes
Industrial processes use heat in the 50-1500°C temperature range, and heat accounts for around 70% of industrial energy consumption. Heat consumption in industry is generally classified into three temperature ranges: low (400°C), which can be addressed by different solar collector technologies. Concentrating solar technologies are needed to produce solar heat at T>150°C. The central issue of integrating solar heat into industrial processes is addressed in the SHIP4D project (PEPR SPLEEN programme). As part of this postdoc, the work will focus on the optimal design of hybrid solar systems for industrial processes. To this end, the PERSEE internal code will be developed to address the problems of optimally integrating solar thermal and photovoltaic technologies for the production of heat and electricity on industrial sites or parks.. The work will also serve as a basis for the European INDHEAP project (Optimal Solar Systems for Industrial Heat and Power), coordinated by the CEA, and started in January 2024.
Development of energy optimization algorithms with low environmental impact
The increasing demand for energy, coupled with the urgency to reduce environmental impacts, requires innovative solutions in energy management. This postdoctoral research project fits into this framework with the objective of evaluating how the intelligent management of an energy system can reduce its environmental impact. The project aims technically to model a complex system and develop advanced energy management algorithms that take into account all environmental criteria. This project must therefore use an innovative and multidisciplinary approach by integrating the Life Cycle Analysis (LCA) of technologies into an Energy Management System (EMS).
The project will rely on the TOTEM platform, a smart grid connecting photovoltaic production, a tertiary building, electric/hydrogen charging stations, as well as energy storage in the form of batteries and gaseous hydrogen. The activities will focus on the development of advanced algorithms for TOTEM's EMS, which must not only improve energy efficiency based on usage but also consider LCA criteria. The goal is to achieve intelligent management of a complete energy system and ultimately minimize carbon footprints and other environmental consequences.
The deployment and testing of algorithms within the TOTEM platform will provide a realistic solution that can be improved by testing it on other applications.
Improvement and extension of a phase-field model for the 3D simulation of important phenomena in the behavior of lithium-ion batteries
In order to optimize the charging time of current-generation batteries, or increasing the power density for future generations, the study of the behavior of materials is crucial to master the lithiation mechanisms of intercalations materials. (e.g. graphite) or “stripping/plating” of lithium metal. In this context, the use of phase field numerical simulations is booming; these methods lend themselves to the modeling of dynamic phenomena for multiphase and multiconstituent systems.
Recently, a 2D phase field module from TrioCFD (open-source software developed at CEA and based on the TRUST platform) was generalized to an arbitrary number of constituents or phases. This post-doctoral project aims to improve and extend this TrioCFD module to high-performance 3D simulations in a distributed parallel computing environment. The objective is to use this module to simulate the 3D physical behaviors of interest of the aforementioned lithium-ion battery materials. We will rely on recent 2D phase field work which has provided a certain number of original and relevant answers to these issues. The move to 3D simulations will provide essential scientific perspectives for these applications.
This work will be carried out as part of a collaboration between several CEA teams from the Cadarache, Grenoble and Saclay centers, bringing together varied expertise (behavior of lithium-ion batteries, phase field method, TrioCFD software environment and numerical methods).
Modeling of charge noise in spin qubits
Thanks to strong partnerships between several research institutes, Grenoble is a pioneer in the development of future technologies based on spin qubits using manufacturing processes identical to those used in the silicon microelectronics industry. The spin of a qubit is often manipulated with alternating electrical (AC) signals through various spin-orbit coupling (SOC) mechanisms that couple it to electric fields. This also makes it sensitive to fluctuations in the qubit's electrical environment, which can lead to large qubit-to-qubit variability and charge noise. The charge noise in the spin qubit devices potentially comes from charging/discharging events within amorphous and defective materials (SiO2, Si3N4, etc.) and device interfaces. The objective of this postdoc is to improve the understanding of charge noise in spin qubit devices through simulations at different scales. This research work will be carried out using an ab initio type method and also through the use of the TB_Sim code, developed within the CEA-IRIG institute. This last one is able of describing very realistic qubit structures using strong atomic and multi-band k.p binding models.
detection of multiplets and application to turkey-Syria seismic crisis of february 2023
The correlation technique, or template matching, applied to the detection and analysis of seismic events has demonstrated its performance and usefulness in the processing chain of the CEA/DAM National Data Center. Unfortunately, this method suffers from limitations which limit its effectiveness and its use in the operational environment, linked on the one hand to the computational cost of massive data processing, and on the other hand to the rate of false detections that could generate low-level processing. The use of denoising methods upstream of processing (example: deepDenoiser, by Zhu et al., 2020), could also increase the number of erroneous detections. The first part of the research project consists of providing a methodology aimed at improving the processing time performance of the multiplets detector, in particular by using information indexing techniques developed in collaboration with LIPADE (L-MESSI method , Botao Peng, Panagiota Fatourou, Themis Palpanas. Fast Data Series Indexing for In-Memory Data. International Journal on Very Large Data Bases (VLDBJ) 2021). The second part of the project concerns the development of an auto-encoder type “filtering” tool for false detections built using machine learning. The Syria-Turkey seismic crisis of February 2023, dominated by two earthquakes of magnitude greater than 7.0, will serve as a learning database for this study.
Optimization of a metrological approach to radionuclide identification based on spectral unmixing
The Laboratoire national Henri Becquerel (LNE-LNHB) at CEA/Saclay is the laboratory responsible for French references in the field of ionizing radiations. For several years now, it has been involved in the development of an automatic analysis tool for low-statistics gamma spectra, based on the spectral unmixing technique. This approach makes it possible to respond to metrological constraints such as robust decision-making and unbiased estimation of counts associated with identified radionuclides. To extend this technique to field measurements, and in particular to the deformation of spectra due to interactions in the environment of a radioactive source, a hybrid spectral unmixing model combining statistical and automatic learning methods is currently being developed. The aim of this mathematical solution is to implement a joint estimation of the spectra measured and the counts associated with the radionuclides identified. The next step will be to quantify the uncertainties of the quantities estimated from the hybrid model. The aim is also to investigate the technique of spectral unmixing in the case of neutron detection with a NaIL detector. The future candidate will contribute to these various studies in collaboration with the Laboratoire d'ingénierie logicielle pour les applications scientifiques (CEA/DRF).
Development of Algorithms for the Detection and Quantification of Biomarkers from Voltammograms
The objective of the post-doctoral research is to develop a high-performance algorithmic and software solution for the detection and quantification of biomarkers of interest from voltammograms. These voltammograms are one-dimensional signals obtained from innovative electrochemical sensors. The study will be carried out in close collaboration with another laboratory at CEA-LIST, the LIST/DIN/SIMRI/LCIM, which will provide dedicated and innovative electrochemical sensors, as well as with the start-up USENSE, which is developing a medical device for measuring multiple biomarkers in urine.
Earthquake effect on underground facilities
The Industrial Centre for Geological Disposal (Cigeo) is a project for a deep geological disposal facility for radioactive waste to be built in France. These wastes will be put in sealed packages in tunnels designed at 500 meters depth. The seals are made of a bentonite/sand mixture which has a high swelling capacity and a low water permeability. As a part of the long-term safety demonstration of the repository, it must be demonstrated that the sealing structures can fulfill their functions under seismic loads over their entire lifetime. In order to guarantee this future nuclear waste repository, CEA and Andra are collaborating to work on the potential scientific and engineering challenges involved.
The responses of underground repository to earthquake events are complex due to the spatially and temporally evolving hydro-mechanical properties of the surrounding media and the structure itself. Accurate modeling of the behavior, therefore, requires a coupled multiphysics numerical code to efficiently model the seismic responses for these underground repositories within their estimated lifespan of 100 thousand years.
The research will therefore, propose a performance assessment for sequential and parallel finite element numerical modeling for earthquake analysis of deep underground facilities. Then perform a synthetic data sampling to account for material uncertainties and based on the obtained results in the previous assessment, run a sensitivity analysis using a FEM or a metamodeling process. Finally, the results and knowledge gained within the span of this project will be processed and interpreted to provide responses for industrial needs.
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.