Deep-Learning-Enabled Assessment of Magnetic Confinement in Magnetized Liner Inertial Fusion.

Abstract

Magnetized Liner Inertial Fusion (MagLIF) is a magneto-inertial fusion (MIF) concept being studied on the Z-machine at Sandia National Laboratories. MagLIF relies on quasi-adiabatic heating of a gaseous deuterium (DD) fuel and flux compression of a background axially oriented magnetic field to achieve fusion relevant plasma conditions. The magnetic flux per fuel radial extent determines the confinement of charged fusion products and is thus of fundamental interest in understanding MagLIF performance. It was recently shown that secondary DT neutron spectra and yields are sensitive to the magnetic field conditions within the fuel, and thus provide a means by which to characterize the magnetic confinement properties of the fuel. 1 , 2 , 3 We utilize an artificial neural network to surrogate the physics model of Refs. [1] , [2] , enabling Bayesian inference of the magnetic confinement parameter for a series of MagLIF experiments that systematically vary the laser preheat energy deposited in the target. This constitutes the first ever systematic experimental study of the magnetic confinement properties as a function of fundamental inputs on any neutron-producing MIF platform. We demonstrate that the fuel magnetization decreases with deposited preheat energy in a fashion consistent with Nernst advection of the magnetic field out of the hot fuel and diffusion into the target liner.