Minimizing the laser imprint through machine learning within the frameword of inertial confinement fusion
The postdoc will be based at the CELIA laboratory which develops studies on different patterns of inertial fusion by laser. In order to optimize the implosion of the target, the laser pulse is shaped spatially and temporally, in particular by a pre-pulse of a hundred picoseconds and intensity of a few hundred TW /cm2. However, the latter introduces spatial inhomogeneities to the surface and volume of the target, amplified by the initial solid behavior of matter. These fingerprints generated by the pre-pulse will degrade the symmetry of the target during its implosion, and therefore decrease the effectiveness of inertial confinement. At present, most models assume a plasma state from the beginning of the interaction, and are thus unable to account for certain experimental observations. To overcome this lack, we have just developed an original multi-physics simulation tool that includes the phase transition of a homogeneous material induced by the laser. In order to mitigate the laser imprint effect, a polystyrene foam (heterogeneous material) can be deposited on the surface of the target. The multiple optical reflections in the foam smooth the spatial profile of laser intensity, thus reducing absorption inhomogeneities. In order to reduce the influence of the laser fingerprint, the post-doctoral fellowship will aim to develop a microscopic model describing the evolution of the optical response of a foam during the solid-to-plasma transition. The first step of the work will be to couple the Helmholtz equation (describing laser propagation) to a solid transition model-plasma, and to study the influence of parameters. The second step will be to use an artificial intelligence algorithm (neural network) to optimize the optical response of the foam.
Computational statistics for post-flight analysis in atmospheric reentry
The post-doctorate corresponds to the context of flight tests of an instrumented vehicle (space shuttle, capsule or probe) which enters into the atmosphere. The aim is to reconstruct, from measurements (inertial unit, radar, meteorological balloon, etc.), the trajectory and various quantities of interest, in order to better understand the physical phenomena and to validate the predictive models. We focus on Bayesian statistics, associated with Markov chain Monte Carlo (MCMC) methods. The post-doctoral fellow will develop and extend the proposed approach and will benefit from a scientific collaboration with Audrey Giremus, professor at the University of Bordeaux and specialist in the field. We will in particular try to increase the performance of high dimensional sampling. Special attention will be paid to the machine learning issue of the exploitation of an aerological database. The final objective will consist in developping an evolving software prototype dedicated to the post-flight analysis of flight tests, that exploits the various sources of information. The evaluations will be based on simulated and real data, with comparison to existing tools. The collaboration work will lead to scientific communications and publications.
Design of a crystal growth process
Laser fusion facilities, like LMJ, require the use of large optical components. Some of them are large KDP or DKDP (KDP partially deuterated) plates extracted from single crystals.
Currently, DKDP single crystals are produced a by slow growth method were the growth time exceeds two years.
Here, we proposed to study a rapid growth method to reducing the growth time to a few months.
Crystal plasticity in classical molecular dynamics and mesoscopic upscaling
Thanks to new supercomputer architectures, classical molecular dynamics simulations will soon enter the realm of a thousand billion atoms, never before achieved, thus becoming capable of representing the plasticity of metals at the micron scale. However, such simulations generate a considerable amount of data, and the difficulty now lies in their exploitation in order to extract the statistical ingredients relevant to the scale of "mesoscopic" plasticity (the scale of continuous models).
The evolution of a material is complex, as it depends on lines of crystalline defects (dislocations) whose evolution is governed by numerous mechanisms. In order to feed models at higher scales, the quantities to be extracted are the velocities and lengths of dislocations, as well as their evolution over time. These data can be extracted using specific analysis techniques based on characterization of the local environment ('distortion score', 'local deformation'), a posteriori or in situ during simulation. Finally, machine learning tools can be used to analyze the statistics obtained and extract and synthesize (by model reduction) a minimal description of plasticity for models at higher scales.
Design of a high-energy phase contrast radiography chain
As part of hydrodynamic experiments carried out at CEA-DAM, the laboratory is seeking, using pulsed X-ray imaging, to radiograph thick objects (several tens of mm), made of low-density materials (around 1 g/cm3), inside which shock waves propagate at very high speeds (several thousand m/s). For this type of application, it is necessary to use energetic X-ray sources (beyond 100 keV). Conventional X-ray imaging, which provides contrast due to variations in absorption cross sections, proves insufficient to capture the small density variations expected during the passage of the shock wave. A theoretical study recently carried out in the laboratory showed that the complementary exploitation of the information contained in the X-ray phase should enable better detectability. The aim of the post-doctorate is to provide experimental proof of concept for this theoretical study. For greater ease of implementation, the work will mainly focus on the dimensioning of a static X-ray chain, where the target is stationary and the source emits continuous X-ray radiation. Firstly, the candidate will have to characterize in detail the spectrum of the selected X-ray source as well as the response of the associated detector. In a second step, he (she) will design and have manufactured interference gratings adapted to high-energy phase measurements, as well as a representative model of the future moving objects to be characterized. Finally, the student will carry out radiographic measurements and compare them with predictive simulations. The student should have a good knowledge of radiation-matter interaction and/or physical and geometric optics. Proficiency in object-oriented programming and/or the Python and C++ languages would be a plus.