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.

Abstract Computational physicists are commonly faced with the task of resolving discrepancies between the predictions of a complex, integrated multi-physics numerical simulation and corresponding experimental datasets. Such efforts commonly require a slow iterative procedure. However, a different approach is available in cases where the multi-physics system of interest admits closed-form analytic solutions. In this situation, the ambiguity is conveniently broken into separate consideration of theory-simulation comparisons (issues of verification) and theory-data comparisons (issues of validation). We demonstrate this methodology via application to the specific example of a fluid-instability based ejecta source model (“RMI+SSVD”) under development at Los Alamos National Laboratory and implemented in FLAG, a Los Alamos continuum mechanics code. The formalism is conducted in the forward sense (i.e., from source to measurement) and enables us to compute, purely analytically, piezoelectric ejecta mass measurements for a specific class of explosively driven metal coupon experiments. We incorporate published measurement uncertainties on relevant experimental parameters to estimate a time-dependent uncertainty on these analytic predictions. This motivates the introduction of a “compatibility score” metric, our primary tool for quantitative analysis of the RMI+SSVD model.