Ensemble simulations of inertial confinement fusion implosions

Nora, Ryan; Peterson, Jayson Luc; Spears, Brian Keith; Field, John Everett; Brandon, Scott

doi:10.1002/sam.11344

Title: Ensemble simulations of inertial confinement fusion implosions

Journal Article · Wed May 24 00:00:00 EDT 2017 · Statistical Analysis and Data Mining

DOI:https://doi.org/10.1002/sam.11344· OSTI ID:1346130

^[1]; Peterson, Jayson Luc ^[1]; Spears, Brian Keith ^[1]; Field, John Everett ^[1]; Brandon, Scott ^[1]

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

The achievement of inertial confinement fusion ignition on the National Ignition Facility relies on the collection and interpretation of a limited (and expensive) set of experimental data. These data are therefore supplemented with state-of-the-art multi-dimensional radiation-hydrodynamic simulations to provide a better understanding of implosion dynamics and behavior. We present a relatively large number (~4000) of systematically perturbed 2D simulations to probe our understanding of low-mode fuel and ablator asymmetries seeded by asymmetric illumination. We find that Gaussian process surrogate models are able to predict both the total neutron yield and the degradation in performance due to asymmetries. Furthermore, the surrogates are then applied to simulations containing new sources of degradation to quantify the impact of the new source.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1346130

Report Number(s):: LLNL-PROC-694784

Journal Information:: Statistical Analysis and Data Mining, Vol. 10, Issue 4; Conference: Conference on Data Analysis, Santa Fe, NM (United States), 2-4 Mar 2016; ISSN 1932-1864

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 19 works

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Cited By (7)

Zonal flow generation in inertial confinement fusion implosions Peterson, J. L.; Humbird, K. D.; Field, J. E. Physics of Plasmas, Vol. 24, Issue 3 https://doi.org/10.1063/1.4977912	journal	March 2017
Deep learning: A guide for practitioners in the physical sciences Spears, Brian K.; Brase, James; Bremer, Peer-Timo Physics of Plasmas, Vol. 25, Issue 8 https://doi.org/10.1063/1.5020791	journal	August 2018
Making inertial confinement fusion models more predictive Gaffney, Jim A.; Brandon, Scott T.; Humbird, Kelli D. Physics of Plasmas, Vol. 26, Issue 8 https://doi.org/10.1063/1.5108667	journal	August 2019
Analysis of NIF scaling using physics informed machine learning Hsu, Abigail; Cheng, Baolian; Bradley, Paul A. Physics of Plasmas, Vol. 27, Issue 1 https://doi.org/10.1063/1.5130585	journal	January 2020
A 3D dynamic model to assess the impacts of low-mode asymmetry, aneurysms and mix-induced radiative loss on capsule performance across inertial confinement fusion platforms Springer, P. T.; Hurricane, O. A.; Hammer, J. H. Nuclear Fusion, Vol. 59, Issue 3 https://doi.org/10.1088/1741-4326/aaed65	journal	December 2018
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Employing machine learning for theory validation and identification of experimental conditions in laser-plasma physics Gonoskov, A.; Wallin, E.; Polovinkin, A. Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-43465-3	journal	May 2019

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