Antonelli, Luca | Search NASA

DOE OSTI2021

Shock Ignition Laser-Plasma Interactions in Ignition-Scale Plasmas

Here, we use a subignition scale laser, the 30 kJ Omega, and a novel shallow-cone target to study laser-plasma interactions at the ablation-plasma density scale lengths and laser intensities anticipated for direct drive shock-ignition implosions at National Ignition Facility scale. Our results show that, under these conditions, the dominant instability is convective stimulated Raman scatter with experimental evidence of two plasmon decay (TPD) only when the density scale length is reduced. Particle-in-cell simulations indicate this is due to TPD being shifted to lower densities, removing the experimental back-scatter signature and reducing the hot-electron temperature. The experimental laser energy-coupling to hot electrons was found to be 1%–2.5%, with electron temperatures between 35 and 45 keV. Radiation-hydrodynamics simulations employing these hot-electron characteristics indicate that they should not preheat the fuel in MJ-scale shock ignition experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

The data-driven future of high-energy-density physics

High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of one another become strongly coupled. The study of these plasmas is important for our understanding of astrophysics, nuclear fusion and fundamental physics—however, the nonlinearities and strong couplings present in these extreme physical systems makes them very difficult to understand theoretically or to optimize experimentally. Here we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proved far too nonlinear for human researchers. Furthermore, from a fundamental perspective, our understanding can be improved by the way in which machine learning models can rapidly discover complex interactions in large datasets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to approximately daily), thus moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we suggest proposals for the community in terms of research design, training, best practice and support for synthetic diagnostics and data analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗