Estimation of stagnation performance metrics in magnetized liner inertial fusion experiments using Bayesian data assimilation Article Swipe
Patrick Knapp
,
Michael E. Glinsky
,
Marc-Andre Schaeuble
,
C. A. Jennings
,
Mary Anne Evans
,
James Gunning
,
T. J. Awe
,
G. A. Chandler
,
Matthias Geißel
,
M. R. Gómez
,
Kelly Hahn
,
Ole Hansen
,
Eric Harding
,
A. J. Harvey-Thompson
,
S. Humane
,
Brandon Klein
,
Michael Mangan
,
Taisuke Nagayama
,
Andrew Porwitzky
,
D. E. Ruiz
,
Paul Schmit
,
S. A. Slutz
,
I. C. Smith
,
Matthew Weis
,
David Yager-Elorriaga
,
D. J. Ampleford
,
Kris Beckwith
,
Thomas R. Mattsson
,
Kyle Peterson
,
D. B. Sinars
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1063/5.0087115
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1063/5.0087115
We present a new analysis methodology that allows for the self-consistent integration of multiple diagnostics including nuclear measurements, x-ray imaging, and x-ray power detectors to determine the primary stagnation parameters, such as temperature, pressure, stagnation volume, and mix fraction in magnetized liner inertial fusion (MagLIF) experiments. The analysis uses a simplified model of the stagnation plasma in conjunction with a Bayesian inference framework to determine the most probable configuration that describes the experimental observations while simultaneously revealing the principal uncertainties in the analysis. We validate the approach by using a range of tests including analytic and three-dimensional MHD models. An ensemble of MagLIF experiments is analyzed, and the generalized Lawson criterion χ is estimated for all experiments.
Related Topics
Concepts
Physics
Stagnation pressure
Bayesian probability
Bayesian inference
Inertial confinement fusion
Range (aeronautics)
Inertial frame of reference
Magnetohydrodynamics
Data assimilation
Plasma
Statistical physics
Fusion
Plasma diagnostics
Mechanics
Computational physics
Nuclear physics
Classical mechanics
Statistics
Aerospace engineering
Meteorology
Mathematics
Mach number
Philosophy
Linguistics
Engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1063/5.0087115
- OA Status
- green
- Cited By
- 24
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4280578248
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4280578248Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1063/5.0087115Digital Object Identifier
- Title
-
Estimation of stagnation performance metrics in magnetized liner inertial fusion experiments using Bayesian data assimilationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-05-01Full publication date if available
- Authors
-
Patrick Knapp, Michael E. Glinsky, Marc-Andre Schaeuble, C. A. Jennings, Mary Anne Evans, James Gunning, T. J. Awe, G. A. Chandler, Matthias Geißel, M. R. Gómez, Kelly Hahn, Ole Hansen, Eric Harding, A. J. Harvey-Thompson, S. Humane, Brandon Klein, Michael Mangan, Taisuke Nagayama, Andrew Porwitzky, D. E. Ruiz, Paul Schmit, S. A. Slutz, I. C. Smith, Matthew Weis, David Yager-Elorriaga, D. J. Ampleford, Kris Beckwith, Thomas R. Mattsson, Kyle Peterson, D. B. SinarsList of authors in order
- Landing page
-
https://doi.org/10.1063/5.0087115Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.osti.gov/biblio/1868390Direct OA link when available
- Concepts
-
Physics, Stagnation pressure, Bayesian probability, Bayesian inference, Inertial confinement fusion, Range (aeronautics), Inertial frame of reference, Magnetohydrodynamics, Data assimilation, Plasma, Statistical physics, Fusion, Plasma diagnostics, Mechanics, Computational physics, Nuclear physics, Classical mechanics, Statistics, Aerospace engineering, Meteorology, Mathematics, Mach number, Philosophy, Linguistics, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
24Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 6, 2023: 9, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
62Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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