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.
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.
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.
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.
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