Nonlinear anisotropic equilibrium reconstruction in axisymmetric magnetic mirrors
S. J. Frank, I. Agarwal, J. K. Anderson, B. Biswas, E. Claveau, D. Endrizzi, C. Everson, R. W. Harvey, S. Murdock, Yu. V. Petrov, J. Pizzo, T. Qian, K. Sanwalka, K. Shih, D. A. Sutherland, A. Tran, J. Viola, D. Yakovlev, M. Yu, C. B. Forest
TL;DR
This paper addresses nonlinear equilibrium reconstruction for axisymmetric plasmas with anisotropic pressure at high $\beta$, extending beyond conventional isotropic, low-$\beta$ methods. It couples the Pleiades free-boundary Grad-Shafranov solver with a semi-analytic kinetic basis for anisotropic mirror pressure and a scalable constrained Bayesian optimization (SCBO) framework to fit to limited diagnostics while providing uncertainty quantification, via the objective $\chi^2=\sum_i (M_i-\mathcal{M}_i)^2/\sigma_{M_i}^2$. Verification with Maxwellian, hybrid, and kinetic sloshing-ion synthetic data shows the method can recover $\langle\beta_0\rangle$, $\langle E_i\rangle$, and $W_{tot}$ to within roughly 20%, and application to WHAM experimental data identifies sloshing-ion pressure peaks in moderate-density shots, distinguishing them from fast-electron effects. The work demonstrates robust, non-Maxwellian, uncertainty-aware equilibrium reconstructions in axisymmetric magnetic mirrors and suggests broad applicability to high-$\beta$ devices with limited diagnostics, including potential extension to multiobjective optimization and 3D/MHD regimes.
Abstract
Magnetic equilibrium reconstruction is a crucial simulation capability for interpreting diagnostic measurements of experimental plasmas. Equilibrium reconstruction has mostly been applied to systems with isotropic pressure and relatively low plasma $β= 2μ_0p/B^2$. This work extends nonlinear equilibrium reconstruction to high-$β$ plasmas with anisotropic pressure and applies it to the Wisconsin High Temperature Superconducting Axisymmetric Magnetic Mirror experiments to infer the presence of sloshing ions. A novel basis set for the plasma profiles and machine learning algorithm using scalable constrained Bayesian optimization allow accurate nonlinear reconstructions with uncertainty quantification to be made more quickly with fewer experimental diagnostics and improves the robustness of reconstructions at high $β$. In addition to WHAM and other mirrors, such reconstruction techniques are potentially attractive in high-performance devices with constrained diagnostic capabilities such as fusion power plants.
