Sensitivity of inertial confinement fusion hot spot properties to the deuterium-tritium fuel adiabat

Abstract

We determine the dependence of key Inertial Confinement Fusion (ICF) hot spot simulation properties on the deuterium-tritium fuel adiabat, here modified by addition of energy to the cold shell. Variation of this parameter reduces the simulation to experiment discrepancy in some, but not all, experimentally inferred quantities. Using simulations with radiation drives tuned to match experimental shots N120321 and N120405 from the National Ignition Campaign (NIC), we carry out sets of simulations with varying amounts of added entropy and examine the sensitivities of important experimental quantities. Neutron yields, burn widths, hot spot densities, and pressures follow a trend approaching their experimentally inferred quantities. Ion temperatures and areal densities are sensitive to the adiabat changes, but do not necessarily converge to their experimental quantities with the added entropy. This suggests that a modification to the simulation adiabat is one of, but not the only explanation of the observed simulation to experiment discrepancies. In addition, we use a theoretical model to predict 3D mix and observe a slight trend toward less mixing as the entropy is enhanced. Instantaneous quantities are assessed at the time of maximum neutron production, determined dynamically within each simulation. These trends contribute to ICF science, as an effort to understand the NIC simulation to experiment discrepancy, and in their relation to the high foot experiments, which features a higher adiabat in the experimental design and an improved neutron yield in the experimental results.

Document Details

Document Type
Pub Defense Publication
Publication Date
Feb 01, 2015
Source ID
10.1063/1.4908278

Entities

People

  • Bing Cheng
  • D. C. Wilson
  • D. H. Sharp
  • Halston Lim
  • J. Glimm
  • Jeremy Melvin
  • V. Rana

Organizations

  • Army Research Office
  • Brookhaven National Laboratory
  • Los Alamos National Laboratory
  • Stony Brook University

Tags

Fields of Study

  • Physics

Readers

  • Computational Modeling and Simulation
  • Information Retrieval
  • Pulsed Power and Plasma Physics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference