Hydro-scaling of direct-drive cylindrical implosions at the OMEGA and the National Ignition Facility

Abstract

Deceleration-phase Rayleigh–Taylor instability (RTI) growth during inertial confinement fusion capsule implosions significantly affects the performance as it mixes cold ablator material into the fuel. Precise measurements of such instability growth are essential for both validating the existing simulation codes and improving our predictive capability. RTI measurements on the inner surface of a spherical shell are limited and are often inferred indirectly at limited convergence. In contrast, cylindrical implosions allow for direct diagnostic access to the converging interface by imaging down the cylinder axis while retaining the effects of convergence. We have performed direct-drive cylindrical implosion experiments at both the OMEGA and the NIF laser facilities using scaled targets. RTI growth is demonstrated to be scale-invariant between the cylindrical targets at OMEGA and similar targets at the NIF, which are scaled up by a factor of three in the radial dimension. Single-mode (m = 20) instability growth factors of ∼14 are measured at a convergence ratio (CR) ∼ 2.5 with nearly identical mode growth at both scales. The measurements are in agreement with xRAGE radiation-hydrodynamics simulations. In addition, we have developed the Bayesian-inference-engine method to account for the variations in the target alignment, magnification, and the parallax effect in the measurement, allowing a more precise comparison between the experimental data and the simulations.

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

“…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. There has been significant interest in using Bayesian inference to improve diagnostics 106 , and to synthesize observations in both focused HEDP experiments 51 and full-scale ICF experiments 50,[107][108][109] . The ultimate aims of using these methods to improve physics understanding, and the reliability of simulations in extrapolating to new designs 48 or facilities, have been addressed though machine learning 42,110 , Bayesian model calibration 50 , and transfer learning 58,111 .…”

Physics Flash Fall 2020

“…The team performed cylindrical implosion experiments both at NIF and Omega to measure the scale-invariant hydrodynamic growth [1,2,3] . The NIF cylinder target was three times larger in radial dimension compared to the Omega cylinder, which was roughly 1 mm in diameter.…”