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

• Published in: Physics of plasmas 2023-05, Vol.30 (5)
• Main Authors: Lewis, William E., Mannion, Owen M., Ruiz, D. E., Jennings, Christopher A., Knapp, Patrick F., Gomez, Matthew R., Harvey-Thompson, Adam J., Weis, Matthew R., Slutz, Stephen A., Ampleford, David J., Beckwith, Kristian
• Format: Article
• Language: English
• Subjects: 70 PLASMA PHYSICS AND FUSION TECHNOLOGY; Bayesian analysis; Bayesian inference; Charged particles; Conduction losses; Confinement; Fuels; fusion experiments; Inertial fusion (reactor); Larmor radius; Machine learning; Magnetohydrodynamic simulation; Magnetohydrodynamics; Parameters; Plasma physics; Statistical inference
• ISSN: 1070-664X; 1089-7674
• DOI: 10.1063/5.0142805

In magneto-inertial fusion, the ratio of the characteristic fuel length perpendicular to the applied magnetic field R to the α-particle Larmor radius ϱ α 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 B R ∝ R / ϱ α 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 χ. 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.