Estimation of stagnation performance metrics in magnetized liner inertial fusion experiments using Bayesian data assimilation
Patrick Knapp, Michael E. Glinsky, Marc-Andre Schaeuble, C. A. Jennings, Mary Anne Evans, James Gunning, T. J. Awe, G. A. Chandler, Matthias Geißel, M. R. Gómez, Kelly Hahn, Ole Hansen, Eric Harding, A. J. Harvey-Thompson, S. Humane, Brandon Klein, Michael Mangan, Taisuke Nagayama, Andrew Porwitzky, D. E. Ruiz, Paul Schmit, S. A. Slutz, I. C. Smith, Matthew Weis, David Yager-Elorriaga, D. J. Ampleford, Kris Beckwith, Thomas R. Mattsson, Kyle Peterson, D. B. Sinars
Abstract
We present a new analysis methodology that allows for the self-consistent integration of multiple diagnostics including nuclear measurements, x-ray imaging, and x-ray power detectors to determine the primary stagnation parameters, such as temperature, pressure, stagnation volume, and mix fraction in magnetized liner inertial fusion (MagLIF) experiments. The analysis uses a simplified model of the stagnation plasma in conjunction with a Bayesian inference framework to determine the most probable configuration that describes the experimental observations while simultaneously revealing the principal uncertainties in the analysis. We validate the approach by using a range of tests including analytic and three-dimensional MHD models. An ensemble of MagLIF experiments is analyzed, and the generalized Lawson criterion χ is estimated for all experiments.