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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:
ORCiD logo [1];  [1];  [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
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]
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

Citation Formats

Gaffney, Jim A., Brandon, Scott T., Humbird, Kelli D., Kruse, Michael K. G., Nora, Ryan C., Peterson, J. Luc, and Spears, Brian K. Making inertial confinement fusion models more predictive. United States: N. p., 2019. Web. doi:10.1063/1.5108667.
Gaffney, Jim A., Brandon, Scott T., Humbird, Kelli D., Kruse, Michael K. G., Nora, Ryan C., Peterson, J. Luc, & Spears, Brian K. Making inertial confinement fusion models more predictive. United States. doi:10.1063/1.5108667.
Gaffney, Jim A., Brandon, Scott T., Humbird, Kelli D., Kruse, Michael K. G., Nora, Ryan C., Peterson, J. Luc, and Spears, Brian K. Mon . "Making inertial confinement fusion models more predictive". United States. doi:10.1063/1.5108667.
@article{osti_1597607,
title = {Making inertial confinement fusion models more predictive},
author = {Gaffney, Jim A. and Brandon, Scott T. and Humbird, Kelli D. and Kruse, Michael K. G. and Nora, Ryan C. and Peterson, J. Luc and Spears, Brian K.},
abstractNote = {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 multifidelity 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.},
doi = {10.1063/1.5108667},
journal = {Physics of Plasmas},
number = [8],
volume = [26],
place = {United States},
year = {2019},
month = {8}
}

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