Engineering topics

MacPhee, A. G.

Publications and source records attributed to MacPhee, A. G..

2 records

DOE OSTI2024

Diagnosing inertial confinement fusion ignition

Abstract Fusion ignition by inertial confinement requires compression and heating of the fusion fuel to temperatures in excess of 5 keV and densities exceeding hundreds of g/cc. In August 2021, this scientific milestone was surpassed at the National Ignition Facility (NIF), when the Lawson criterion for ignition was exceeded generating 1.37MJ of fusion energy (Abu-Shawareb et al 2022 Phys. Rev. Lett. 129 075001), and then in December 2022 target gain >1 was realized with the production of 3.1MJ of fusion energy from a target driven by 2.0MJ of laser energy (Abu-Shawareb et al 2024 Phys. Rev. Lett. 132 065102). At the NIF, inertial confinement fusion research primarily uses a laser indirect drive in which the fusion capsule is surrounded by a high-Z enclosure (‘hohlraum’) used to convert the directed laser energy into a symmetric x-ray drive on the capsule. Precise measurements of the plasma conditions, x-rays, γ -rays and neutrons produced are key to understanding the pathway to higher performance. This paper discusses the diagnostics and measurement techniques developed to understand these experiments, focusing on three main topics: (1) key diagnostic developments for achieving igniting plasmas, (2) novel signatures related to thermonuclear burn and (3) advances to diagnostic capabilities in the igniting regime with a perspective toward developments for intertial fusion energy.

Moore, A. S. (ORCID:0000000221258882)↗

DOE OSTI2024

Statistical data analysis of x-ray spectroscopy data enabled by neural network accelerated Bayesian inference

Bayesian inference applied to x-ray spectroscopy data analysis enables uncertainty quantification necessary to rigorously test theoretical models. However, when comparing to data, detailed atomic physics and radiation transfer calculations of x-ray emission from non-uniform plasma conditions are typically too slow to be performed in line with statistical sampling methods, such as Markov Chain Monte Carlo sampling. Furthermore, differences in transition energies and x-ray opacities often make direct comparisons between simulated and measured spectra unreliable. Here, we present a spectral decomposition method that allows for corrections to line positions and bound–bound opacities to best fit experimental data, with the goal of providing quantitative feedback to improve the underlying theoretical models and guide future experiments. In this work, we use a neural network (NN) surrogate model to replace spectral calculations of isobaric hot-spots created in Kr-doped implosions at the National Ignition Facility. The NN was trained on calculations of x-ray spectra using an isobaric hot-spot model post-processed with Cretin, a multi-species atomic kinetics and radiation code. The speedup provided by the NN model to generate x-ray emission spectra enables statistical analysis of parameterized models with sufficient detail to accurately represent the physical system and extract the plasma parameters of interest.

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