Exploring the parameter space of MagLIF implosions using similarity scaling. II. Current scaling

Magnetized liner inertial fusion (MagLIF) is a magneto-inertial-fusion (MIF) concept, which is presently being studied on the Z pulsed power facility. The MagLIF platform has achieved interesting plasma conditions at stagnation and produced significant fusion yields in the laboratory. Given the relative success of MagLIF, there is a strong interest to scale the platform to higher peak currents. However, scaling MagLIF is not entirely straightforward due to the large dimensionality of the experimental input parameter space and the numerous physical processes involved in MIF implosions. In this work, we propose a novel method to scale MagLIF loads to higher currents. Our method is based on similarity (or similitude) scaling and attempts to preserve much of the physics regimes already known or being studied on today's Z pulsed-power driver. By avoiding significant deviations into unexplored and/or less well-understood regimes, the risk of unexpected outcomes on future scaled-up experiments is reduced. Using arguments based on similarity scaling, we derive the scaling rules for the experimental input parameters characterizing a MagLIF load (as functions of the characteristic current driving the implosion). We then test the estimated scaling laws for various metrics measuring performance against results of 2D radiation-magneto-hydrodynamic hydra simulations. Agreement is found between the scaling theory and the simulation results.

Automated summary

Magnetized liner inertial fusion (MagLIF) is a promising concept for achieving fusion reactions, which has shown interesting results in laboratory experiments. However, scaling this concept to higher peak currents is challenging due to the complex nature of the experimental parameters and physical processes involved. In this work, a novel method based on similarity scaling is proposed to scale MagLIF loads, aiming to preserve known physics regimes and reduce the risk of unexpected outcomes in future experiments.