Ensemble simulations of inertial confinement fusion implosions

Abstract

The achievement of inertial confinement fusion ignition on the National Ignition Facility relies on the collection and interpretation of a limited (and expensive) set of experimental data. These data are therefore supplemented with state‐of‐the‐art multidimensional radiation‐hydrodynamic simulations to provide a better understanding of implosion dynamics and behavior. We present a relatively large number (∼ 4000) of systematically perturbed 2D simulations to probe our understanding of low‐mode fuel and ablator asymmetries seeded by asymmetric illumination. We find that Gaussian process surrogate models are able to predict both the total neutron yield and the degradation in performance due to asymmetries. The surrogates are then applied to simulations containing new sources of degradation to quantify the impact of the new source.

Suppressing simulation bias in multi-modal data using transfer learning

“…To sample the design space more adequately, we used a database of over 92 thousand simulations of the target capsule. The simulations are deterministic, employ a two-dimensional radiation hydrodynamic code HYDRA [29], and took tens of millions of CPU hours [9,28] to complete.…”

“…Third, running ensembles of ICF simulations and building surrogate models is fairly novel [9] and we attempt to predict experimental images pixel-by-pixel for a suite of shots for the very first time. Important pieces of information about the images that are crucial for our work are not always available.…”

“…This method is, however, not designed to work with a small number of observations and with diverse types of outputs, such as time series, images, or videos. With the current trend for deploying more sensors and more types of sensors in many aspects of our life [4] and science [5][6][7], as well as the advances in simulating such data [8][9][10][11], there is a need to develop calibration methods that leverage all available data types. This is especially important when the observational data set is very sparse and more data types could break the non-uniqueness in the estimation.…”

The data-driven future of high-energy-density physics

“…Work to optimize the three information sources in Figure 3 include the acceleration of multiphysics simulations using deep learning 55,97 , infrastructure for intelligent control of large-scale simulations 98 , intelligent and data-informed design of experiments 42,43,99 , as well as optimization of experimental facilities 100 . These developments have enabled simulation studies of unprecedented size 101 and the generation of open-source ICF datasets 102 that motivate novel deep learning research 54,57,103,104 . AI tools have been applied to the automatic analysis and featurization of complex data types like spectra, images 54,105 , and line-of-sight dependent quantities.…”

A 3D dynamic model to assess the impacts of low-mode asymmetry, aneurysms and mix-induced radiative loss on capsule performance across inertial confinement fusion platforms

“…Figure 7 shows the result of hydro-scaling of N170601, which would require an increase by a factor of 1.6 in capsule energy to reach ignition conditions, and also shows that this does not occur until temper atures at minimum volume exceed 5.5 keV. Finally, the requirements dT dt > 0 and d 2 T dt 2 > 0 were examined in a subset of the ensemble of 60 000 2D ICF implosion simulations at the LANL Trinity supercomputer [42,43] and are plotted in figure 8. Generally, simulations with both dT dt and d 2 T dt 2 > 0 ignited, and simulations with either dT dt or d 2 T dt 2 < 0 failed to ignite.…”