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Monte Carlo Methods and Applications

Managing Editor: Sabelfeld, Karl K.

Editorial Board: Binder, Kurt / Bouleau, Nicolas / Chorin, Alexandre J. / Dimov, Ivan / Dubus, Alain / Egorov, Alexander D. / Ermakov, Sergei M. / Halton, John H. / Heinrich, Stefan / Kalos, Malvin H. / Lepingle, D. / Makarov, Roman / Mascagni, Michael / Mathe, Peter / Niederreiter, Harald / Platen, Eckhard / Sawford, Brian R. / Schmid, Wolfgang Ch. / Schoenmakers, John / Simonov, Nikolai A. / Sobol, Ilya M. / Spanier, Jerry / Talay, Denis

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CiteScore 2017: 0.67

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Source Normalized Impact per Paper (SNIP) 2017: 0.860

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1569-3961
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Volume 10, Issue 3-4

Issues

Stochastic Eulerian model for the flow simulation in porous media. Unconfined aquifers*

Dmitry Kolyukhin
  • Weierstrass Institute for Applied Analysis and Stochastics Mohrenstra”se 39, D - 10117 Berlin, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Karl Sabelfeld
  • Weierstrass Institute for Applied Analysis and Stochastics Mohrenstra”se 39, D - 10117 Berlin, Germany
  • Institute of Computational Mathematics and Mathematical Geophysics Russian Acad. Sci., Lavrentieva str., 6 630090 Novosibirsk Russia
  • Other articles by this author:
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Published Online: 2008-05-09 | DOI: https://doi.org/10.1515/mcma.2004.10.3-4.345

This work deals with a stochastic unconfined aquifer flow simulation in statistically isotropic saturated porous media. This approach is a generalization of the 3D model we developed in [13]. In this paper we deal with a 2D model obtained via depth-averaging of the 3D model. The average hydraulic conductivity is assumed to be a random field with a lognormal distribution. Assuming the fluctuations in the hydraulic conductivity to be small we construct a stochastic Eulerian model for the flow as a Gaussian random field with a spectral tensor of a special structure derived from Darcy's law. A randomized spectral representation is then used to simulate this random field. A series of test calculations confirmed the high accuracy and computational efficiency of the method.

About the article

Published Online: 2008-05-09

Published in Print: 2004-12-01


Citation Information: Monte Carlo Methods and Applications mcma, Volume 10, Issue 3-4, Pages 345–357, ISSN (Online) 1569-3961, ISSN (Print) 0929-9629, DOI: https://doi.org/10.1515/mcma.2004.10.3-4.345.

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[1]
Orazgeldi Kurbanmuradov, Karl Sabelfeld, and Peter R. Kramer
Journal of Computational Physics, 2013, Volume 245, Page 218

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