Fragmentation of a liquid sheet subjected to a strong initial velocity gradient
When a shock wave reflects off the free surface of a metal sample, it interacts with geometric irregularities (treated as defects such as those caused by a machining tool). At sufficiently high pressures, the shock reflection, followed by the material's expansion into the surrounding medium, can trigger the high-speed ejection (several thousand m/s) of liquid-phase metal sheets from the free surface. These sheets stretch and fragment, creating a particle cloud that can interfere with certain experiments. This process is extremely difficult to characterize experimentally or simulate, as it occurs at challenging spatio-temporal scales and involves material fracture physics that current computational codes based on hydrodynamic equations cannot naturally reproduce.
This thesis aims to address this specific issue. Drawing notably on theoretical analyses, it seeks to incorporate fragmentation processes into simulations where the material, modeled using a continuum (hydrodynamic) approach, is subjected to very high strain rates. One of the objectives is to predict the size distributions of the particles resulting from sheet fragmentation as a function of their velocity and to compare these predictions with available experimental data.
Accelerated design of High Entropy Oxide thin films for carbon-free energies
Over the past decade, high-entropy oxides have emerged as a promising new class of materials for energy-related applications and corrosion resistance in extreme environments, both of which are central areas of CEA's activities.
However, their optimization remains limited by the vast compositional space that needs to be explored, leading most studies to focus on equimolar compositions.
This PhD project aims to overcome this limitation by combining combinatorial PVD using HiPIMS technology with artificial intelligence approaches based on Bayesian optimization, an approach that has not yet been explored for these materials.
The objective is to master the deposition of high-entropy oxides and optimize their properties for corrosion protection in high-temperature electrolyzers dedicated to low-carbon hydrogen production.
The deposited coatings will exhibit controlled stoichiometry, structure, microstructure, and texture, and will be characterized both in situ and ex situ using complementary techniques.
The project will provide a proof of concept for coupling combinatorial PVD with Bayesian optimization to accelerate the discovery and optimization of new materials.
This PhD project is part of the international doctoral programme of PEPR DIADEM and will bring together CEA (Saclay), ESRF (Grenoble), and NIMS (Japan).