001     1043071
005     20260203123818.0
024 7 _ |2 doi
|a 10.1063/5.0244435
024 7 _ |2 ISSN
|a 1527-2419
024 7 _ |2 ISSN
|a 1070-664X
024 7 _ |2 ISSN
|a 1089-7674
024 7 _ |2 datacite_doi
|a 10.34734/FZJ-2025-02758
024 7 _ |2 WOS
|a WOS:001434107100005
037 _ _ |a FZJ-2025-02758
082 _ _ |a 530
100 1 _ |0 P:(DE-HGF)0
|a Brönner, M.
|b 0
|e Corresponding author
245 _ _ |a Particle swarm optimization of 1D isochoric compression designs for fast ignition
260 _ _ |a [Erscheinungsort nicht ermittelbar]
|b American Institute of Physics
|c 2025
336 7 _ |2 DRIVER
|a article
336 7 _ |2 DataCite
|a Output Types/Journal article
336 7 _ |0 PUB:(DE-HGF)16
|2 PUB:(DE-HGF)
|a Journal Article
|b journal
|m journal
|s 1753091678_26185
336 7 _ |2 BibTeX
|a ARTICLE
336 7 _ |2 ORCID
|a JOURNAL_ARTICLE
336 7 _ |0 0
|2 EndNote
|a Journal Article
520 _ _ |a A method to study isochoric compression to mass densities relevant for direct-drive fast ignition schemes is presented. The method is based on the combination of one-dimensional radiation-hydrodynamic simulations using the code MULTI-IFE [Ramis and Meyer-ter Vehn, Comput. Phys. Commun. 203, 226 (2016)] and a particle swarm optimization technique [Kennedy and Eberhart, in Proceedings of ICNN'95 - International Conference on Neural Networks (IEEE, Perth, WA, Australia, 1995), Vol. 4, pp. 1942–1948]. The compression of the fuel is optimized through variations of the incident temporal laser power profiles. Uniform mass density profiles are achieved by using appropriate objective functions that allow comparisons between the fuel assemblies obtained from simulations. Several objective functions were created and evaluated on their merits to yield isochoric compression assembly. Ultimately, such a profile is presented in conjunction with the technique to achieve it. A useful objective function is calculating the deviation of the simulated mass density profile from the ideal uniform mass density profile over a volume of the compressed target up to the radial position of the outgoing shock wave.
536 _ _ |0 G:(DE-HGF)POF4-5111
|a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)
|c POF4-511
|f POF IV
|x 0
536 _ _ |0 G:(DE-Juel-1)SDLPP
|a Simulation and Data Lab Plasma Physics
|c SDLPP
|x 1
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |0 P:(DE-HGF)0
|a Atzeni, S.
|b 1
700 1 _ |0 P:(DE-HGF)0
|a Callahan, D.
|b 2
700 1 _ |0 P:(DE-HGF)0
|a Gaffney, J.
|b 3
700 1 _ |0 P:(DE-Juel1)132115
|a Gibbon, Paul
|b 4
|u fzj
700 1 _ |0 P:(DE-HGF)0
|a Jarrott, L. C.
|b 5
700 1 _ |0 P:(DE-HGF)0
|a Mateo, A.
|b 6
700 1 _ |0 P:(DE-HGF)0
|a Savino, L.
|b 7
700 1 _ |0 P:(DE-HGF)0
|a Schott, N.
|b 8
700 1 _ |0 P:(DE-HGF)0
|a Theobald, W.
|b 9
700 1 _ |0 P:(DE-HGF)0
|a Roth, M.
|b 10
773 _ _ |0 PERI:(DE-600)1472746-8
|a 10.1063/5.0244435
|g Vol. 32, no. 2, p. 022710
|n 2
|p 022710
|t Physics of plasmas
|v 32
|x 1527-2419
|y 2025
856 4 _ |u https://juser.fz-juelich.de/record/1043071/files/broenner_pop2025.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:1043071
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)132115
|a Forschungszentrum Jülich
|b 4
|k FZJ
913 1 _ |0 G:(DE-HGF)POF4-511
|1 G:(DE-HGF)POF4-510
|2 G:(DE-HGF)POF4-500
|3 G:(DE-HGF)POF4
|4 G:(DE-HGF)POF
|9 G:(DE-HGF)POF4-5111
|a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|v Enabling Computational- & Data-Intensive Science and Engineering
|x 0
914 1 _ |y 2025
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2024-12-11
915 _ _ |a Creative Commons Attribution-NonCommercial CC BY-NC 4.0
|0 LIC:(DE-HGF)CCBYNC4
|2 HGFVOC
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2024-12-11
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a National-Konsortium
|0 StatID:(DE-HGF)0430
|2 StatID
|d 2025-11-06
|w ger
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b PHYS PLASMAS : 2022
|d 2025-11-06
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2025-11-06
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2025-11-06
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
|d 2025-11-06
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2025-11-06
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2025-11-06
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2025-11-06
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1150
|2 StatID
|b Current Contents - Physical, Chemical and Earth Sciences
|d 2025-11-06
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2025-11-06
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2025-11-06
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21