Title: Making inertial confinement fusion models more predictive

Abstract

Computer models of inertial confinement fusion (ICF) implosions play an essential role in experimental design and interpretation as well as our understanding of fundamental physics under the most extreme conditions that can be reached in the laboratory. Building truly predictive models is a significant challenge, with the potential to greatly accelerate progress to high yield and ignition. One path to more predictive models is to use experimental data to update the underlying physics in a way that can be extrapolated to new experiments and regimes. We describe a statistical framework for the calibration of ICF simulations using data collected at the National Ignition Facility (NIF). We perform Bayesian inferences for a series of laser shots using an approach that is designed to respect the physics simulation as much as possible and then build a second model that links the individual-shot inferences together. We show that this approach is able to match multiple X-ray and neutron diagnostics for a whole series of NIF “BigFoot” shots. Within the context of 2D radiation hydrodynamic simulations, our inference strongly favors a significant reduction in fuel compression over other known degradation mechanisms (namely, hohlraum issues and engineering perturbations). This analysis is expanded using a multifidelitymore » technique to pick fuel-ablator mix from several candidate causes of the degraded fuel compression (including X-ray preheat and shock timing errors). Finally, we use our globally calibrated model to investigate the extra laser drive energy that would be required to overcome the inferred fuel compression issues in NIF BigFoot implosions.« less

Authors:

Gaffney, Jim A.  ^[1];

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• Search DOE PAGES for ORCID "0000-0002-2408-0047"
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Brandon, Scott T. ^[1]; Humbird, Kelli D. ^[1]; Kruse, Michael K. G.  ^[1];

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• Search DOE PAGES for ORCID "0000-0002-8901-3857"
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Nora, Ryan C.  ^[1];

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• Search DOE PAGES for ORCID "0000-0002-5478-8820"
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Peterson, J. Luc  ^[1];

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• Search DOE PAGES for ORCID "0000-0002-5167-5708"
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Spears, Brian K. ^[1]

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+ Show Author Affiliations

1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Publication Date:

2019-08-12

Research Org.:

Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Sponsoring Org.:

USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:

1597607

Alternate Identifier(s):

OSTI ID: 1556809

Report Number(s):

LLNL-JRNL-770914
Journal ID: ISSN 1070-664X; 961192; TRN: US2103148

Grant/Contract Number:  

AC52-07NA27344

Resource Type:

Accepted Manuscript

Journal Name:

Physics of Plasmas

Additional Journal Information:

Journal Volume: 26; Journal Issue: 8; Journal ID: ISSN 1070-664X

Publisher:

American Institute of Physics (AIP)

Country of Publication:

United States

Language:

English

Subject:

70 PLASMA PHYSICS AND FUSION TECHNOLOGY; Artificial neural networks; Bayesian inference; Hydrodynamics simulations; Nuclear fusion; Calibration methods; Physics; Mathematics; Computing