Ian Lee Tregillis

We present a simple implicit solution for the time-dependent trajectory of a thin Asay foil ejecta diagnostic for the general case where the impinging ejecta cloud is generated by a source function characterized by an arbitrary (sustained) time dependence and a time-independent (stationary) particle velocity distribution. In the limit that the source function time dependence becomes a delta function, this solution-which is amenable to rapid numerical calculations of arbitrary accuracy-exactly recovers a previously published solution for the special case of instantaneous ejecta production. We also derive simple expressions for the free-surface arrival (catch-up) time as well as the true ejecta areal mass accumulation on the accelerating foil and place bounds on the level of error incurred when applying instant-production mass solutions to a sustained-production trajectory. We demonstrate these solutions with example calculations for hypothetical source functions spanning a wide range of ejecta production durations, velocity distributions, and temporal behaviors. These calculations demonstrate how the foil trajectory is often insensitive to the temporal dependence of the source function, instead being dominated by the velocity distribution. We quantify this insensitivity using a "compatibility score" metric. Under certain conditions, one may capitalize upon this insensitivity to obtain a good approximation of the second integral of the velocity distribution from the observed foil trajectory. (c) 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license(https://creativecommons.org/licenses/by/4.0/)

We present a study of using machine learning to enhance hohlraum design for opacity measurement experiments. For opacity experiments we desire a hohlraum that, when its interior walls are illuminated by theNational Ignition Facility (NIF) lasers, will produce a high radiation flux that heats a central sample to a temperature that is constant over a measurement time window. Given a baseline hohlraum design and a computational model, we train a deep neural network to predict the time evolution of the radiation temperature as measured by the Dante diagnostic. This enables us to rapidly explore design space and determine the effect of adjusting design parameters. We also construct an "inverse" machine learning model that predicts the design parameters given a desired time history of radiation temperature. Calculations using the machine learning model demonstrate that improved performance over the baseline hohlraum would reduce uncertainties in experimental opacity measurements.

Although the fundamental physics behind radiation and matter flow is understood, many uncertainties remain in the exact behavior of macroscopic fluids in systems ranging from pure turbulence to coupled radiation hydrodynamics. Laboratory experiments play an important role in studying this physics to allow scientists to test their macroscopic models of these phenomena. However, because the fundamental physics is well understood, precision experiments are required to validate existing codes already tested by a suite of analytic, manufactured and convergence solutions. To conduct such high-precision experiments requires a detailed understanding of the experimental errors and the nature of their uncertainties on the observed diagnostics. In this paper, we study the uncertainties plaguing many radiation-flow experiments, focusing on those using a hohlraum (dynamic or laser-driven) source and a foam-density target. This study focuses on the effect these uncertainties have on the breakout time of the radiation front. We find that, even if the errors in the initial conditions and numerical methods are Gaussian, the errors in the breakout time are asymmetric, leading to a systematic bias in the observed data. We must understand these systematics to produce the high-precision experimental results needed to study this physics.