001     485
005     20180208215100.0
024 7 _ |2 DOI
|a 10.1016/j.cageo.2008.02.020
024 7 _ |2 WOS
|a WOS:000261632000030
037 _ _ |a PreJuSER-485
041 _ _ |a eng
082 _ _ |a 550
084 _ _ |2 WoS
|a Computer Science, Interdisciplinary Applications
084 _ _ |2 WoS
|a Geosciences, Multidisciplinary
100 1 _ |a Herbst, M.
|b 0
|u FZJ
|0 P:(DE-Juel1)129469
245 _ _ |a On preconditioning for a parallel solution of the Richards equation
260 _ _ |a Amsterdam [u.a.]
|b Elsevier Science
|c 2008
300 _ _ |a 1958 - 1963
336 7 _ |a Journal Article
|0 PUB:(DE-HGF)16
|2 PUB:(DE-HGF)
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|0 0
|2 EndNote
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a article
|2 DRIVER
440 _ 0 |a Computers & Geosciences
|x 0098-3004
|0 19264
|y 12
|v 34
500 _ _ |a We greatly acknowledge the John von Neumann-Institute for Computing (NIC), Julich, for providing the opportunity to use their parallel cornputing resources.
520 _ _ |a In this paper, we present a class of preconditioning methods for a parallel solution of the three-dimensional Richards equation. The preconditioning methods Jacobi scaling, block-Jacobi, incomplete lower-upper, incomplete Cholesky and algebraic multigrid were applied in combination with a parallel conjugate gradient solver and tested for robustness and convergence using two model scenarios. The first scenario was an infiltration into initially dry, sandy soil discretised in 500,000 nodes. The second scenario comprised spatially distributed soil properties using 275,706 numerical nodes and atmospheric boundary conditions. Computational results showed a high efficiency of the nonlinear parallel solution procedure for both scenarios using up to 64 processors. Using 32 processors for the first scenario reduced the wall clock time to slightly more than 1% of the single processor run. For scenario 2 the use of 64 processors reduces the wall clock time to slightly more than 20% of the 8 processors wall clock time. The difference in the efficiency of the various preconditioning methods is moderate but not negligible. The use of the multigrid preconditioning algorithm is recommended, since on average it performed best for both scenarios. (c) 2008 Elsevier Ltd. All rights reserved.
536 _ _ |a Terrestrische Umwelt
|c P24
|2 G:(DE-HGF)
|0 G:(DE-Juel1)FUEK407
|x 0
588 _ _ |a Dataset connected to Web of Science
650 _ 7 |a J
|2 WoSType
653 2 0 |2 Author
|a Three-dimensional
653 2 0 |2 Author
|a Multi-processor
653 2 0 |2 Author
|a Unsaturated flow
653 2 0 |2 Author
|a Water flow
653 2 0 |2 Author
|a Preconditioner
653 2 0 |2 Author
|a Multigrid
700 1 _ |a Gottschalk, S.
|b 1
|u FZJ
|0 P:(DE-Juel1)VDB57509
700 1 _ |a Reissel, M.
|b 2
|u FZJ
|0 P:(DE-Juel1)VDB49397
700 1 _ |a Hardelauf, H.
|b 3
|u FZJ
|0 P:(DE-Juel1)129466
700 1 _ |a Kasteel, R.
|b 4
|u FZJ
|0 P:(DE-Juel1)VDB724
700 1 _ |a Javaux, M.
|b 5
|u FZJ
|0 P:(DE-Juel1)129477
700 1 _ |a Vanderborght, J.
|b 6
|u FZJ
|0 P:(DE-Juel1)129548
700 1 _ |a Vereecken, H.
|b 7
|u FZJ
|0 P:(DE-Juel1)129549
773 _ _ |a 10.1016/j.cageo.2008.02.020
|g Vol. 34, p. 1958 - 1963
|p 1958 - 1963
|q 34<1958 - 1963
|0 PERI:(DE-600)1499977-8
|t Computers & geosciences
|v 34
|y 2008
|x 0098-3004
856 7 _ |u http://dx.doi.org/10.1016/j.cageo.2008.02.020
909 C O |o oai:juser.fz-juelich.de:485
|p VDB
913 1 _ |k P24
|v Terrestrische Umwelt
|l Terrestrische Umwelt
|b Erde und Umwelt
|0 G:(DE-Juel1)FUEK407
|x 0
914 1 _ |y 2008
915 _ _ |0 StatID:(DE-HGF)0010
|a JCR/ISI refereed
920 1 _ |k ICG-4
|l Agrosphäre
|d 31.10.2010
|g ICG
|0 I:(DE-Juel1)VDB793
|x 1
920 1 _ |k JARA-SIM
|l Jülich-Aachen Research Alliance - Simulation Sciences
|g JARA
|0 I:(DE-Juel1)VDB1045
|x 2
970 _ _ |a VDB:(DE-Juel1)101001
980 _ _ |a VDB
980 _ _ |a ConvertedRecord
980 _ _ |a journal
980 _ _ |a I:(DE-Juel1)IBG-3-20101118
980 _ _ |a I:(DE-Juel1)VDB1045
980 _ _ |a UNRESTRICTED
981 _ _ |a I:(DE-Juel1)IBG-3-20101118
981 _ _ |a I:(DE-Juel1)VDB1045


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21