Impact of imposed mode 2 laser drive asymmetry on inertial confinement fusion implosions

Abstract

Low-mode asymmetries have emerged as one of the primary challenges to achieving high-performing inertial confinement fusion implosions. These asymmetries seed flows in the implosions, which will manifest as modifications to the measured ion temperature (Tion) as inferred from the broadening of primary neutron spectra. The effects are important to understand (i) to learn to control and mitigate low-mode asymmetries and (ii) to experimentally more closely capture thermal Tion used as input in implosion performance metric calculations. In this paper, results from and simulations of a set of experiments with a seeded mode 2 in the laser drive are described. The goal of this intentionally asymmetrically driven experiment was to test our capability to predict and measure the signatures of flows seeded by the low-mode asymmetry. The results from these experiments [first discussed in M. Gatu Johnson et al., Phys. Rev. E 98, 051201(R) (2018)] demonstrate the importance of interplay of flows seeded by various asymmetry seeds. In particular, measured Tion and self-emission x-ray asymmetries are expected to be well captured by interplay between flows seeded by the imposed mode 2 and the capsule stalk mount. Measurements of areal density asymmetry also indicate the importance of the stalk mount as an asymmetry seed in these implosions. The simulations brought to bear on the problem (1D LILAC, 2D xRAGE, 3D ASTER, and 3D Chimera) show how thermal Tion is expected to be significantly lower than Tion as inferred from the broadening of measured neutron spectra. They also show that the electron temperature is not expected to be the same as Tion for these implosions.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 01, 2019
Source ID
10.1063/1.5066435

Entities

People

  • Aidan Crilly
  • Alex Bennett Zylstra
  • B. D. Appelbe
  • Brandon Lahmann
  • Brian M Haines
  • C. Forrest
  • C. Stoeckl
  • C. Walsh
  • David-Tomline Michel
  • F. H. Séguin
  • F. J. Marshall
  • I. V. Igumenshchev
  • J. A. Delettrez
  • J. P. Chittenden
  • J. P. Knauer
  • Johan A. Frenje
  • M. Gatu Johnson
  • R. Janezic
  • Richard Petrasso
  • V. Yu. Glebov
  • W. Grimble

Organizations

  • Imperial College London
  • Los Alamos National Laboratory
  • Massachusetts Institute of Technology
  • United States Department of Energy
  • University of Rochester

Tags

Fields of Study

  • Physics

Readers

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  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Directed Energy
  • Microelectronics