Predictive design of REHEDS experiments with radiation-hydrodynamic simulations requires knowledge of material properties (e.g. equations of state (EOS), transport coefficients, and radiation physics). Interpreting experimental results requires accurate models of diagnostic observables (e.g. detailed emission, absorption, and scattering spectra). In conditions of Local Thermodynamic Equilibrium (LTE), these material properties and observables can be pre-computed with relatively high accuracy and subsequently tabulated on simple temperature-density grids for fast look-up by simulations. When radiation and electron temperatures fall out of equilibrium, however, non-LTE effects can profoundly change material properties and diagnostic signatures. Accurately and efficiently incorporating these non-LTE effects has been a longstanding challenge for simulations. At present, most simulations include non-LTE effects by invoking highly simplified inline models. These inline non-LTE models are both much slower than table look-up and significantly less accurate than the detailed models used to populate LTE tables and diagnose experimental data through post-processing or inversion. Because inline non-LTE models are slow, designers avoid them whenever possible, which leads to known inaccuracies from using tabular LTE. Because inline models are simple, they are inconsistent with tabular data from detailed models, leading to ill-known inaccuracies, and they cannot generate detailed synthetic diagnostics suitable for direct comparisons with experimental data. This project addresses the challenge of generating and utilizing efficient, accurate, and consistent non-equilibrium material data along three complementary but relatively independent research lines. First, we have developed a relatively fast and accurate non-LTE average-atom model based on density functional theory (DFT) that provides a complete set of EOS, transport, and radiative data, and have rigorously tested it against more sophisticated first-principles multi-atom DFT models, including time-dependent DFT. Next, we have developed a tabular scheme and interpolation methods that compactly capture non-LTE effects for use in simulations and have implemented these tables in the GORGON magneto-hydrodynamic (MHD) code. Finally, we have developed post-processing tools that use detailed tabulated non-LTE data to directly predict experimental observables from simulation output.
Hare, Jack H.; Myers, Clayton E.; Ampleford, David A.; Chittenden, Jeremy P.; Crilly, Aidan C.; Datta, Rishabh D.; Fox, William F.; Halliday, Jack H.; Jennings, Christopher A.; Ji, Hantao J.; Kuranz, Carolyn K.; Lebedev, Sergey L.; Melean, Raul M.; Uzdensky, Dmitri U.
Datta, Rishabh D.; Hare, Jack H.; Myers, Clayton E.; Ampleford, David A.; Chittenden, Jeremy P.; Clayson, Thomas C.; Crilly, Aidan C.; Fox, Will F.; Halliday, Jack H.; Jennings, Christopher A.; Ji, Hantao J.; Kuranz, Carolyn K.; Lebedev, Sergey L.; Melean, Raul M.; Russel, Daniel R.; Suttle, Lee S.; Tang, I T.; Uzdensky, Dmitri U.
The inductively driven transmission line (IDTL) is a miniature current-carrying device that passively couples to fringe magnetic fields in the final power feed on the Z Pulsed Power Facility. The IDTL redirects a small amount of Z's magnetic energy along a secondary path to ground, thereby enabling pulsed power diagnostics to be driven in parallel with the primary load for the first time. IDTL experiments and modeling presented here indicate that IDTLs operate non-perturbatively on Z and that they can draw in excess of 150 kA of secondary current, which is enough to drive an X-pinch backlighter. Additional experiments show that IDTLs are also capable of making cleaner, higher-fidelity measurements of the current flowing in the final feed.
The morphology of the stagnated plasma resulting from Magnetized Liner Inertial Fusion (MagLIF) is measured by imaging the self-emission x-rays coming from the multi-keV plasma, and the evolution of the imploding liner is measured by radiographs. Equivalent diagnostic response can be derived from integrated rad-MHD simulations from programs such as Hydra and Gorgon. There have been only limited quantitative ways to compare the image mor- phology, that is the texture, of simulations and experiments. We have developed a metric of image morphology based on the Mallat Scattering Transformation (MST), a transformation that has proved to be effective at distinguishing textures, sounds, and written characters. This metric has demonstrated excellent performance in classifying ensembles of synthetic stagnation images. We use this metric to quantitatively compare simulations to experimen- tal images, cross experimental images, and to estimate the parameters of the images with uncertainty via a linear regression of the synthetic images to the parameter used to generate them. This coordinate space has proved very adept at doing a sophisticated relative back- ground subtraction in the MST space. This was needed to compare the experimental self emission images to the rad-MHD simulation images. We have also developed theory that connects the transformation to the causal dynamics of physical systems. This has been done from the classical kinetic perspective and from the field theory perspective, where the MST is the generalized Green's function, or S-matrix of the field theory in the scale basis. From both perspectives the first order MST is the current state of the system, and the second order MST are the transition rates from one state to another. . An efficient, GPU accelerated, Python implementation of the MST was developed. Future applications are discussed.