Offre en lien avec l’Action/le Réseau : BigData4Astro/– — –
Laboratoire/Entreprise : DAVID Lab – UVSQ – Versailles
Durée : 36
Contact : mustapha.lebbah@uvsq.fr
Date limite de publication : 2024-01-31
Contexte :
An electric arc is a high-current disruptive discharge capable of self-sustaining at low voltage. Its study belongs to the field of plasma physics.
While legacy networks have learned to live with the non-zero probability of this fault occurring, since the causes and consequences are known, the same cannot be said for networks dedicated to propulsion, whether all-electric or hybrid. The criticality of the damage is often equated with the energy deployed by the fault compared with the susceptibility to damage of the materials in direct or indirect contact with the arc. We can therefore imagine the impact that a high-power arc could have in such a network (KV/MW) if its duration were to exceed a few milliseconds! Given the increase in voltage and the DC waveform, the risk and consequences of arc faults are increased. This observation, combined with a constrained environment (confined areas, high-risk zones such as FFLZ, severe environment zones, etc.), prompted the EWIS engineers to carry out high-power electric arc tests, leading to the following conclusions:
When an electric arc is generated whose power exceeds several hundred kW and whose lifetime is not controllable, it is no longer possible to control it.
When an electric arc is generated, the power of which exceeds several hundred kW and the lifetime of which cannot be controlled, it is no longer possible to mitigate the consequences solely by choosing ‘arc-resistant’ materials and design guides in the confined environment and safety constraints inherent in civil aeronautics.
This is why the detection and elimination of this fault is inevitable. Arc detection systems, which were not mandatory on legacy networks, will certainly be required on the propulsion networks envisaged.
Arc detection is important for electrical safety, because arcs can cause fires, damage electrical equipment and pose a risk to people. To detect arcs, several technologies and methods are used, including: monitoring by current sensors, light sensors, electrical signal analysis, thermal cameras, etc.
Sujet :
Objectives
The aim of the proposed thesis will be to couple AI and physics for the modeling and early detection of electric arcs. The AI tools targeted are Physics-Informed Neural Networks (PINNs). Experimental data will be available to feed these models. In addition, the physical equations to describe the evolution of arcs to be taken into account are well-known (Maxwell, Faraday, Navier-Stokes, etc).
The major challenges of the thesis will be as follows:
• Modeling electric arcs: Developing PINNs models to describe electric arcs accurately, taking into account the physical equations that govern them. As a check on the calculations (or as a learning tool), physical simulations called MHD (Magneto-Hydro-Dynamics) could be carried out for all the geometries envisaged in the SafranTech E&E team.
• Network training: Train neural networks to predict the presence of electric arcs using observation data and the relevant physical equations.
• Early detection: Develop techniques for the early detection of electric arcs based on PINNs models.
• Experimental validation: Test PINN models and detection methods on real experimental data from Safran electrical systems. This data will be available for several “typical” geometries and will enable the PINNs models to be tested on reproducible and controllable cases.
Profil du candidat :
● End of engineering degree / Master’s degree in a relevant field (e.g., computer science, ML/AI, Statistics …)
● Excellent understanding of machine learning and physics basics. Familiar with recent Artificial Intelligence: transformers, diffusion
model, auto-encoder…etc.
● Excellent programming skills, especially with Python, Pytorch,
● Autonomous and able to quickly adapt to recent scientific literature / technologies.
This PhD topic is participating to the Université Paris-Saclay EU COFUND DeMythif.AI program : https://www.dataia.eu/actualites/cofund-demythifai-appel-sujets-de-these. It is reserved to international students who have spent less than 12 months in France in the last 3 years. The candidates will be evaluated by a jury that will select 15 PhD to start in fall 2024. The successful candidates will be fully funded for 3 years, have access to specific scientific and non-scientific training, and be fully part of the Université Paris-Saclay AI community. The aim of this Ph.D. research is to strengthen collaboration with Safran group. The thesis is accompanied by a collaborative contract with Safran, ensuring the environment, interaction with experts, and data availability.
Formation et compétences requises :
● End of engineering degree / Master’s degree in a relevant field (e.g., computer science, ML/AI, Statistics …)
● Excellent understanding of machine learning and physics basics. Familiar with recent Artificial Intelligence: transformers, diffusion
model, auto-encoder…etc.
● Excellent programming skills, especially with Python, Pytorch,
● Autonomous and able to quickly adapt to recent scientific literature / technologies.
This PhD topic is participating to the Université Paris-Saclay EU COFUND DeMythif.AI program : https://www.dataia.eu/actualites/cofund-demythifai-appel-sujets-de-these. It is reserved to international students who have spent less than 12 months in France in the last 3 years. The candidates will be evaluated by a jury that will select 15 PhD to start in fall 2024. The successful candidates will be fully funded for 3 years, have access to specific scientific and non-scientific training, and be fully part of the Université Paris-Saclay AI community. The aim of this Ph.D. research is to strengthen collaboration with Safran group. The thesis is accompanied by a collaborative contract with Safran, ensuring the environment, interaction with experts, and data availability.
How to apply
https://adum.fr/as/ed/voirproposition.pl?site=PSaclay&matricule_prop=51640&langue=en
Adresse d’emploi :
David Lab /UVSQ Versailles
Document attaché : 202312150810_Proposal-PHD.pdf