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International Journal for Multiscale Computational Engineering

Publicado 6 números por año

ISSN Imprimir: 1543-1649

ISSN En Línea: 1940-4352

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.4 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.3 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 2.2 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00034 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.46 SJR: 0.333 SNIP: 0.606 CiteScore™:: 3.1 H-Index: 31

Indexed in

Fast Deflation Methods with Applications to Two-Phase Flows

Volumen 6, Edición 1, 2008, pp. 13-24
DOI: 10.1615/IntJMultCompEng.v6.i1.20
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SINOPSIS

Traditional Krylov iterative solvers, such as the preconditioned conjugate gradient method, can be accelerated by incorporating a second-level preconditioner. We use deflation as a second-level preconditioner, which is very efficient in many applications. In this paper, we give some theoretical results for the general deflation method applied to singular matrices, which provides us more insight into the properties and the behavior of the method. Moreover, we discuss stability issues of the deflation method and consider some ideas for a more stable method. In the numerical experiments, we apply the deflation method and its stabilized variant to singular linear systems derived from two-phase bubbly flow problems. Because of the appearance of bubbles, those linear systems are ill-conditioned, and therefore, they are usually hard to solve using traditional preconditioned Krylov iterative methods. We show that our deflation methods can be very efficient to solve the linear systems. Finally, we also investigate numerically the stability of these methods by examining the corresponding inner-outer iterations in more detail.

CITADO POR
  1. Tang J. M., Nabben R., Vuik C., Erlangga Y. A., Comparison of Two-Level Preconditioners Derived from Deflation, Domain Decomposition and Multigrid Methods, Journal of Scientific Computing, 39, 3, 2009. Crossref

  2. Deb Pulok Kanti, Akter Farhana, Imtiaz Syed Ahmad, Hossain M. Enamul, Nonlinearity and solution techniques in reservoir simulation: A review, Journal of Natural Gas Science and Engineering, 46, 2017. Crossref

  3. Erlangga Yogi A., Nabben Reinhard, On the convergence of two-level Krylov methods for singular symmetric systems, Numerical Linear Algebra with Applications, 24, 6, 2017. Crossref

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