Simulation of surfaces and interfaces between halogenated perovskites and carrier transport layers.
Context: Halide perovskites are a very active field of research for their applications in photovoltaics (PV), light emitting devices, X-ray detectors, and more. The role of interfaces in the devices, especially in enhancing carrier extraction in solar cells, is crucial. Atomic scale modelling of bulk and surfaces of these
materials is challenging, because of their softness, associated with anharmonic behaviour and local atomic disorder (polymorphism), spin-orbit coupling and polar distortions. In spite of recent advances in describing polymorphism and anharmonic lattice dynamics in the bulk, studies of surface and interface properties are recent and still rare.
Description and duties: The work will focus on building predictive models of interfaces between pérovskites light absorbers and electron/holes transport layers (ETL/HTL) based on Density Functional Theory. Existing and new HTL/ETL materials will be investigated with a special focus on understanding passivation mechanisms, defects at the interface, their stability and kinetics. Development of machine learning potential is envisaged.
Digital correction of the health status of an electrical network
Cable faults are generally detected when communication is interrupted, resulting in significant repair costs and downtime. Additionally, data integrity becomes a major concern due to the increased threats of attacks and intrusions on electrical networks, which can disrupt communication. Being able to distinguish between disruptions caused by the degradation of the physical layer of an electrical network and an ongoing attack on the energy network will help guide decision-making regarding corrective operations, particularly network reconfiguration and predictive maintenance, to ensure network resilience. This study proposes to investigate the relationship between incipient faults in cables and their impact on data integrity in the context of Power Line Communication (PLC). The work will be based on deploying instrumentation using electrical reflectometry, combining distributed sensors and AI algorithms for online diagnosis of incipient faults in electrical networks. In the presence of certain faults, advanced AI methods will be applied to correct the state of the health of the electrical network's physical layer, thereby ensuring its reliability.
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.
Attack detection in the electrical grid distributed control
To enable the emergence of flexible and resilient energy networks, we need to find solutions to the challenges facing these networks, in particular digitization and the protection of data flows that this will entail, and cybersecurity issues.
In the Tasting project, and in collaboration with RTE, the French electricity transmission network operator, your role will be to analyze data protection for all parties involved. The aim is to verify security properties on data in distributed systems, taking into account that those induce a number of uncertainties.
To this end, you will develop a tool-based methodology for protecting the data of power grid stakeholders. The approach will be based on formal methods, in particular runtime verification, applied to a distributed control system.
This postdoc position is part of the TASTING project, which aims to meet the key challenges of modernizing and securing power systems. This 4-year project, which started in 2023, addresses axis 3 of the PEPR TASE call “Technological solutions for the digitization of intelligent energy systems”, co-piloted by CEA and CNRS, which aims to generate innovations in the fields of solar energy, photovoltaics, floating wind power and for the emergence of flexible and resilient energy networks. The targeted scientific challenges concern the ICT infrastructure, considered as a key element and solution provider for the profound transformations that our energy infrastructures will undergo in the decades to come.
The project involves two national research organizations, INRIA and CEA through its technological research institute CEA-List. Also involved are 7 academic laboratories: G2Elab, GeePs, IRIT, L2EP, L2S and SATIE, as well as an industrial partner, RTE, which is supplying various use cases.
Preparation and characterization of an oxide/oxide composite
Fiber-reinforced ceramic matrix composites (CMCs) are a class of materials that combine good specific mechanical properties (properties relative to their density) with resistance to high temperatures (> 1000 °C), even in oxidizing atmospheres. They are typically composed of a carbon or ceramic fiber reinforcement and a ceramic matrix (carbide or oxide.
The proposed study focuses on the development of a low-matrix oxide/oxide CMC with suitable dielectric, thermal, and mechanical properties.
This study will be conducted in collaboration with several laboratories at CEA Le Ripault.
Development of an innovative method for ultrasound imaging of velocity fields in flows behind opaque walls
Today, the only solutions on the market for measuring 2D velocity fields are laser-based optical methods (such as particle imaging velocimetry: PIV).
These are limited by the need for optical access to the flow and are therefore inapplicable on opaque fluids (such as liquid metals) or through opaque pipes (such as metal pipes, the majority in industry).
To overcome this limitation and meet new challenges (in research and industry) it is possible to rely on acoustic imaging methods.
The LISM (CEA Cadarache Instrumentation Laboratory) has been working for several years on the development of an industrial acoustic PIV (or echo-PIV) method.
An initial thesis has led to significant progress, and the CEA is now planning to market echo-PIV scanners through a start-up project.
However, there are still a number of hurdles to overcome, in particular that of imaging through walls with high acoustic impedance differences.
Your main objective will be to remove these obstacles. This mission will be structured as follows:
- Bibliographical study and familiarisation with the echo-PIV method
- Numerical study and development of a solution to resolve the problems of energy transmission through the metal wall
- Experimental validation of the detection of microscopic reflectors through a metal wall
- Numerical study and development of a solution to the problem of multiple reflection within the metal wall, leading to poor reconstruction of the final image
- Experimental validation of the solution to the reflection problem
- Adaptation of the acoustic imaging method to simultaneously resolve the transmission and reflection problems
- Publication in scientific journals (and/or patents)
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.
Simulation of reactive gas-liquid multi-phase flows
The objective of this postdoctoral position is to develop and implement a simulation method for the simulation of a
sodium spray fire. Two key points need to be adressed. First, one needs to propose a proper representation of the sodium
droplets (dispersed phase) from their generation by a jet (separate phase) fragmentation to their behavior (motion,
oxidation, combustion) in the air atmosphere. This requires to derive a flow model that accounts for multiple components
with multiple interface topology regimes (dispersed and separate). Second, one needs to develop a robust discretization
strategy for this complex flow model.
The numerical work will be implemented in a new numerical tool to perform simulations of sodium spray fires developed at CEA. This tool is based on the canoP. Canop is a library designed for solving computational fluid dynamics problems using a cell-based
Adaptive Mesh Refinement (AMR) approach and parallel calculation.
Optomechanical resonators in chaotic regime for cryptography in optical datacoms
The aim of the post doc is to explore the use of optomechanical resonators placed in a chaotic regime to secure optical communications. It is part of a project from the CEA's research-at-risk program, selected in July 2024. A key point is to obtain a highly non-linear regime, favored by specific geometries, necessary for the richness of chaos. Exploiting the unique properties of chaos for secure data transfer will be explored by the postdoc as part of a working group.
With the advent of the quantum computer, current techniques for securing information exchange become largely compromised, necessitating the development of post-quantum cryptography techniques. Beyond software approaches, new hardware concepts have emerged, such as chaotic cryptography. In this context, it is becoming essential to develop chaos sources that are high-quality (richness of parameter space), compatible with existing communication systems and compact. While lasers are a well-known source of chaos, optomechanical systems seem particularly well suited to this application, as the mechanical domain provides an enriched parameter space, while retaining high data throughput and a direct connection with optical communications systems. The postdoc will explore the suitability of chaotic optomechanical devices for implementing hardware cryptography.
Advanced modeling of thermal turbulent flows
For several decades, numerical simulations in fluid mechanics have significantly contributed to the design and maintenance of industrial installations. Turbulence modeling, a key area at the intersection of research and industry, has seen substantial advancements in both LES and RANS approaches. Since the early 2010s, hybrid methods that combine RANS and LES techniques have emerged to leverage the advantages of each, necessitating proficiency in both modeling types. The TrioCFD code developed at STMF, although capable of handling these models, has not seen adequate investment in modern approaches. To incorporate hybrid models, it is essential to update and enhance the current models. The proposed task is to identify the most relevant models for industrial applications, restructure the software to accommodate these models, and validate their performance.