Analysis of NIF scaling using physics informed machine learning

Hsu, Abigail Elaine; Cheng, Baolian; Bradley, Paul Andrew

doi:10.1063/1.5130585

Analysis of NIF scaling using physics informed machine learning

Journal Article · Thu Jan 16 00:00:00 EST 2020 · Physics of Plasmas

DOI:https://doi.org/10.1063/1.5130585· OSTI ID:1784693

Hsu, Abigail Elaine ^[1]; ^[2]; ^[2]

Stony Brook Univ., NY (United States). Dept. of Applied Mathematics and Statistics; Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Over 120 DT ice layer thermonuclear (TN) ignition experiments in inertial confinement fusion (ICF) were conducted on the National Ignition Facility (NIF) in the last eight years. None of the experiments achieved ignition. In fact, the measured neutron outputs from the experiments were well below what was expected. Although experiments to fine-tune the target designs are the focus of the national ICF program, insightful analysis of the existing data is a pressing need. In highly integrated ignition experiments, it is impossible to vary only one design parameter without perturbing all the other implosion variables. Thus, to determine the nonlinear relationships between the design parameters and performance from the data, a multivariate analysis based on physics models is necessary. To this end, we apply machine learning and deep learning methods to the existing NIF experimental data to uncover the patterns and physics scaling laws in TN ignition. In this study, we focus on the scaling laws between the implosion parameters and neutron yield using different supervised learning methods. Descriptions, comparisons, and contrasts between the methods are presented. Our results show that these models are able to infer a relationship between the observed stagnation conditions and neutron yields. Furthermore, this exploratory study will help build new capabilities to evaluate capsule designs and provide suggestions for new designs.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1784693

Alternate ID(s):: OSTI ID: 1591980

Report Number(s):: LA-UR--19-28368

Journal Information:: Physics of Plasmas, Journal Name: Physics of Plasmas Journal Issue: 1 Vol. 27; ISSN 1070-664X

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Analysis of NIF scaling using physics informed machine learning

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