We present the results of a study where we use machine learning to enhance hohlraum design for opacity measurement experiments. Opacity experiments on laser facilities use hohlraums, which, when ...their interior walls are illuminated by the National Ignition Facility (NIF) lasers, 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 could reduce sensitivities and uncertainties in experimental opacity measurements.
•We demonstrate that machine learning models can be used to augment simulation to design opacity experiments.•Forward machine learning models allow design space to be explored.•We develop a machine learning model to learn the inverse mapping from diagnostics to experiment design.•Simulations indicate that measurement uncertainties can be improved using our models.
Mix of ablator material into fuel of an ICF capsule adds non-burning material, diluting the fuel and reducing burn. The amount of the reduction is dependent in part on the morphology of the mix. A ...probability distribution function (PDF) burn model has been developed 6 that utilizes the average concentration of mixed materials as well as the variance in this quantity across cells provided by the BHR turbulent transport model 3 and its revisions 4 to describe the mix in terms of a PDF of concentrations of fuel and ablator material, and provides the burn rate in mixed material. Work is underway to develop the MARBLE ICF platform for use on the National Ignition Facility in experiments to quantify the influence of heterogeneous mix on fusion burn. This platform consists of a plastic (CH) capsule filled with a deuterated plastic foam (CD) with a density of a few tens of milligrams per cubic centimeter, with tritium gas filling the voids in the foam. This capsule will be driven using x-ray drive on NIF, and the resulting shocks will induce turbulent mix that will result in the mixing of deuterium from the foam with the tritium gas. In order to affect the morphology of the mix, engineered foams with voids of diameter up to 100 microns will be utilized. The degree of mix will be determined from the ratio of DT to DD neutron yield. As the mix increases, the yield from reactions between the deuterium of the CD foam with tritium from the gas will increase. The ratio of DT to DD neutrons will be compared to a variation of the PDF burn model that quantifies reactions from initially separated reactants.
We present a set of high-resolution three-dimensional MHD simulations of steady light, supersonic jets, exploring the influence of jet Mach number and the ambient medium on jet propagation and energy ...deposition over long distances. The results are compared to simple self-similar scaling relations for the morphological evolution of jet-driven structures and to previously published two-dimensional simulations. For this study we simulated the propagation of light jets with internal Mach numbers 3 and 12 to lengths exceeding 100 initial jet radii in both uniform and stratified atmospheres. The propagating jets asymptotically deposit approximately half of their energy flux as thermal energy in the ambient atmosphere, almost independent of jet Mach number or the external density gradient. Nearly one-quarter of the jet total energy flux goes directly into dissipative heating of the ICM, supporting arguments for effective feedback from AGNs to cluster media. The remaining energy resides primarily in the jet and cocoon structures. Despite having different shock distributions and magnetic field features, global trends in energy flow are similar among the different models. As expected, the jets advance more rapidly through stratified atmospheres than uniform environments. The asymptotic head velocity in King-type atmospheres shows little or no deceleration. This contrasts with jets in uniform media with heads that slow as they propagate. This suggests that the energy deposited by jets of a given length and power depends strongly on the structure of the ambient medium. While our low Mach number jets are more easily disrupted, their cocoons obey evolutionary scaling relations similar to the high Mach number jets.
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