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Newton-Krylov-Schwarz Method for a Spherical Shallow Water Model*

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Domain Decomposition Methods in Science and Engineering XIX

Part of the book series: Lecture Notes in Computational Science and Engineering ((LNCSE,volume 78))

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Abstract

In this paper we study the application of Newton-Krylov-Schwarz method to fully implicit, fully coupled solution of a global shallow water model. In particular, we are interested in developing a scalable parallel solver when the shallow water equations (SWEs) are discretized on the cubed-sphere grid using a second-order finite volume method.

* The first author was supported in part by NSFC grant 10801125, in part by 973 China grant 2005CB321702, and in part by 863 China grants 2006AA01A125. The second author was supported in part by DOE under DE-FC-02-06ER25784, and in part by NSF under grants CCF-0634894 and DMS 0913089.

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Author information

Authors and Affiliations

  1. Institute of Software, Chinese Academy of Sciences, Beijing, 100190, P. R. China

    Chao Yang

  2. Department of Computer Science, University of Colorado at Boulder, Boulder, CO, 80309, USA

    Xiao-Chuan Cai

Authors
  1. Chao Yang
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  2. Xiao-Chuan Cai
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Corresponding author

Correspondence to Chao Yang .

Editor information

Editors and Affiliations

  1. , Department of Mathematics, Xiangtan University, Xiangtan, 411105, China, People's Republic

    Yunqing Huang

  2. FB Mathematik und Informatik, Freie Universität Berlin, Arnimallee 6, Berlin, 14195, Berlin, Germany

    Ralf Kornhuber

  3. Department of Mathematics, Courant Institute of Math. Sciences, New York University, Mercer St. 251, New York, 10012, New York, USA

    Olof Widlund

  4. Eberly College of Science, Dept. Mathematics, Pennsylvania State University, University Park, 16802, Pennsylvania, USA

    Jinchao Xu

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Yang, C., Cai, XC. (2011). Newton-Krylov-Schwarz Method for a Spherical Shallow Water Model*. In: Huang, Y., Kornhuber, R., Widlund, O., Xu, J. (eds) Domain Decomposition Methods in Science and Engineering XIX. Lecture Notes in Computational Science and Engineering, vol 78. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11304-8_15

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