z-logo
open-access-imgOpen Access
Constraining physical models at gigabar pressures
Author(s) -
J. J. Ruby,
J. R. Rygg,
D. A. Chin,
Jim Gaffney,
P. J. Adrian,
D. T. Bishel,
C. J. Forrest,
V. Yu. Glebov,
N. Kabadi,
P. M. Nilson,
Y. Ping,
C. Stöeckl,
G. W. Collins
Publication year - 2020
Publication title -
physical review. e
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.896
H-Index - 304
eISSN - 2470-0053
pISSN - 2470-0045
DOI - 10.1103/physreve.102.053210
Subject(s) - implosion , statistical physics , markov chain monte carlo , physics , bayesian inference , monte carlo method , bayesian probability , experimental data , markov chain , computer science , plasma , nuclear physics , machine learning , artificial intelligence , statistics , mathematics
High-energy-density (HED) experiments in convergent geometry are able to test physical models at pressures beyond hundreds of millions of atmospheres. The measurements from these experiments are generally highly integrated and require unique analysis techniques to procure quantitative information. This work describes a methodology to constrain the physics in convergent HED experiments by adapting the methods common to many other fields of physics. As an example, a mechanical model of an imploding shell is constrained by data from a thin-shelled direct-drive exploding-pusher experiment on the OMEGA laser system using Bayesian inference, resulting in the reconstruction of the shell dynamics and energy transfer during the implosion. The model is tested by analyzing synthetic data from a one-dimensional hydrodynamics code and is sampled using a Markov chain Monte Carlo to generate the posterior distributions of the model parameters. The goal of this work is to demonstrate a general methodology that can be used to draw conclusions from a wide variety of HED experiments.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom