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Title: Improving the fidelity of simulations of power flow and target design on Sandia's Z machine - an Efficient Generalized Ohm's Law Modeling Approach in a Traditional Lagrangian-Eulerian Computational Framework.

Abstract

Abstract not provided.

Authors:
;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1500197
Report Number(s):
SAND2018-2496PE
661262
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the Presentation at Army Research Laboratory held March 13, 2018 in Washington DC.
Country of Publication:
United States
Language:
English

Citation Formats

McGregor, Duncan Alisdair Odum, and Robinson, Allen C. Improving the fidelity of simulations of power flow and target design on Sandia's Z machine - an Efficient Generalized Ohm's Law Modeling Approach in a Traditional Lagrangian-Eulerian Computational Framework.. United States: N. p., 2018. Web.
McGregor, Duncan Alisdair Odum, & Robinson, Allen C. Improving the fidelity of simulations of power flow and target design on Sandia's Z machine - an Efficient Generalized Ohm's Law Modeling Approach in a Traditional Lagrangian-Eulerian Computational Framework.. United States.
McGregor, Duncan Alisdair Odum, and Robinson, Allen C. Thu . "Improving the fidelity of simulations of power flow and target design on Sandia's Z machine - an Efficient Generalized Ohm's Law Modeling Approach in a Traditional Lagrangian-Eulerian Computational Framework.". United States. https://www.osti.gov/servlets/purl/1500197.
@article{osti_1500197,
title = {Improving the fidelity of simulations of power flow and target design on Sandia's Z machine - an Efficient Generalized Ohm's Law Modeling Approach in a Traditional Lagrangian-Eulerian Computational Framework.},
author = {McGregor, Duncan Alisdair Odum and Robinson, Allen C.},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {3}
}

Conference:
Other availability
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