Data-driven assessment of magnetic charged particle confinement parameter scaling in magnetized liner inertial fusion experiments on Z

In magneto-inertial fusion, the ratio of the characteristic fuel length perpendicular to the applied magnetic field R to the a-particle Larmor radius .a is a critical parameter setting the scale of electron thermal-conduction loss and charged burn-product confinement. Using a previously developed deep-learning-based Bayesian inference tool, we obtain the magnetic-field fuel-radius product BR cx R=.a from an ensemble of 16 magnetized liner inertial fusion (MagLIF) experiments. Observations of the trends in BR are consistent with relative trade-offs between compression and flux loss as well as the impact of mix from 1D resistive radiation magneto-hydrodynamics simulations in all but two experiments, for which 3D effects are hypothesized to play a significant role. Finally, we explain the relationship between BR and the generalized Lawson parameter v. Our results indicate the ability to improve performance in MagLIF through careful tuning of experimental inputs, while also highlighting key risks from mix and 3D effects that must be mitigated in scaling MagLIF to higher currents with a next-generation driver.

Automated summary

In magneto-inertial fusion, the ratio of the characteristic fuel length perpendicular to the applied magnetic field R to the a-particle Larmor radius .a is crucial and determines the scale of electron thermal-conduction loss and charged burn-product confinement. By using a deep-learning-based Bayesian inference tool, the magnetic-field fuel-radius product BR cx R=.a was obtained from 16 magnetized liner inertial fusion experiments. The results indicate the potential for improving MagLIF performance through careful tuning of experimental inputs, while also emphasizing the need to mitigate risks from mix and 3D effects when scaling MagLIF to higher currents with a next-generation driver.