Calibrated simulations of Z opacity experiments that reproduce the experimentally measured plasma conditions.

Abstract

Recently, frequency-resolved iron opacity measurements at electron temperatures of 170-200 eV and electron densities of (0.7-4.0)×10(22)cm(-3) revealed a 30-400% disagreement with the calculated opacities [J. E. Bailey et al., Nature (London) 517, 56 (2015)]. The discrepancies have a high impact on astrophysics, atomic physics, and high-energy density physics, and it is important to verify our understanding of the experimental platform with simulations. Reliable simulations are challenging because the temporal and spatial evolution of the source radiation and of the sample plasma are both complex and incompletely diagnosed. In this article, we describe simulations that reproduce the measured temperature and density in recent iron opacity experiments performed at the Sandia National Laboratories Z facility. The time-dependent spectral irradiance at the sample is estimated using the measured time- and space-dependent source radiation distribution, in situ source-to-sample distance measurements, and a three-dimensional (3D) view-factor code. The inferred spectral irradiance is used to drive 1D sample radiation hydrodynamics simulations. The images recorded by slit-imaged space-resolved spectrometers are modeled by solving radiation transport of the source radiation through the sample. We find that the same drive radiation time history successfully reproduces the measured plasma conditions for eight different opacity experiments. These results provide a quantitative physical explanation for the observed dependence of both temperature and density on the sample configuration. Simulated spectral images for the experiments without the FeMg sample show quantitative agreement with the measured spectral images. The agreement in spectral profile, spatial profile, and brightness provides further confidence in our understanding of the backlight-radiation time history and image formation. These simulations bridge the static-uniform picture of the data interpretation and the dynamic-gradient reality of the experiments, and they will allow us to quantitatively assess the impact of effects neglected in the data interpretation.