Modelling burning thermonuclear plasma
File(s)burning_plasma_plain.pdf (753.94 KB)
Accepted version
Author(s)
Rose, Steven
Hatfield, Peter
Scott, Robbie
Type
Journal Article
Abstract
Considerable progress towards the achievement of
thermonuclear burn using inertial confinement fusion has been
achieved at the National Ignition Facility (NIF) in the USA in
the last few years. Other drivers, such as the Z-machine at
Sandia, are also making progress towards this goal. A burning
thermonuclear plasma would provide a unique and extreme
plasma environment; in this paper we discuss a) different
theoretical challenges involved in modelling burning plasmas
not currently considered, b) the use of novel machine learning
based methods that might help large facilities reach ignition,
and c) the connections that a burning plasma might have to
fundamental physics, including QED studies, and the replication
and exploration of conditions that last occurred in the first few
minutes after the Big Bang.
thermonuclear burn using inertial confinement fusion has been
achieved at the National Ignition Facility (NIF) in the USA in
the last few years. Other drivers, such as the Z-machine at
Sandia, are also making progress towards this goal. A burning
thermonuclear plasma would provide a unique and extreme
plasma environment; in this paper we discuss a) different
theoretical challenges involved in modelling burning plasmas
not currently considered, b) the use of novel machine learning
based methods that might help large facilities reach ignition,
and c) the connections that a burning plasma might have to
fundamental physics, including QED studies, and the replication
and exploration of conditions that last occurred in the first few
minutes after the Big Bang.
Date Issued
2020-11-13
Date Acceptance
2020-05-05
Citation
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2020, 378 (2184), pp.1-8
ISSN
1364-503X
Publisher
Royal Society, The
Start Page
1
End Page
8
Journal / Book Title
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Volume
378
Issue
2184
Copyright Statement
© 2020 The Author(s) Published by the Royal Society. All rights reserved.
Identifier
https://royalsocietypublishing.org/doi/10.1098/rsta.2020.0014
Subjects
fusion
machine learning
plasma
General Science & Technology
Publication Status
Published
Date Publish Online
2020-10-12