Oxford Centre for High Energy Density Science (OxCHEDS)

Abstract:

We present in-situ x-ray diffraction and velocity measurements of Fe2O3 under laser shock compression at pressures between 38-122 GPa. None of the high-pressure phases reported by static compression studies were observed. Instead, we observed an isostructural phase transition from α-Fe2O3 to a new α′ -Fe2O3 phase at a pressure of 50-62 GPa. The α′ -Fe2O3 phase differs from α-Fe2O3 by an 11% volume drop and a different unit cell compressibility. We further observed a two-wave structure in the velocity profile, which can be related to an intermediate regime where both α and α′ phases coexist. Density functional theory calculations with a Hubbard parameter indicate that the observed unit cell volume drop can be associated with a spin transition following a magnetic collapse.

Abstract:

Inertial confinement fusion fuel suffers increased X-ray radiation losses when carbon from the capsule ablator mixes into the hot-spot. Here we present one and two-dimensional ion VlasovFokker-Planck simulations that resolve hot-spot self heating in the presence a localised spike of carbon mix, totalling 1.9 % of the hot-spot mass. The mix region cools and contracts over tens of picoseconds, increasing its alpha particle stopping power and radiative losses. This makes a localised mix region more severe than an equal amount of uniformly distributed mix. There is also a purely kinetic effect that reduces fusion reactivity by several percent, since faster ions in the tail of the distribution are absorbed by the mix region. Radiative cooling and contraction of the spike induces fluid motion, causing neutron spectrum broadening. This artificially increases the inferred experimental ion temperatures and gives line of sight variations.

Abstract:

We present a machine learning based approach to address the study of transport processes, ubiquitous in continuous mechanics, with particular attention to those phenomena ruled by complex micro-physics, impractical to theoretical investigation, yet exhibiting emergent behavior describable by a closed mathematical expression. Our machine learning model, built using simple components and following a few well established practices, is capable of learning latent representations of the transport process substantially closer to the ground truth than expected from the nominal error characterising the data, leading to sound generalisation properties. This is demonstrated through an idealized study of the long standing problem of heat flux suppression under conditions relevant for fusion and cosmic plasmas. A simple analysis shows that the result applies beyond those case specific assumptions and that, in particular, the accuracy of the learned representation is controllable through knowledge of the data quality (error properties) and a suitable choice of the dataset size. While the learned representation can be used as a plug-in for numerical modeling purposes, it can also be leveraged with the above error analysis to obtain reliable mathematical expressions describing the transport mechanism and of great theoretical value.