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Journal of Porous Media
Impact-faktor: 1.752 5-jähriger Impact-Faktor: 1.487 SJR: 0.43 SNIP: 0.762 CiteScore™: 2.3

ISSN Druckformat: 1091-028X
ISSN Online: 1934-0508

Volumes:
Volumen 23, 2020 Volumen 22, 2019 Volumen 21, 2018 Volumen 20, 2017 Volumen 19, 2016 Volumen 18, 2015 Volumen 17, 2014 Volumen 16, 2013 Volumen 15, 2012 Volumen 14, 2011 Volumen 13, 2010 Volumen 12, 2009 Volumen 11, 2008 Volumen 10, 2007 Volumen 9, 2006 Volumen 8, 2005 Volumen 7, 2004 Volumen 6, 2003 Volumen 5, 2002 Volumen 4, 2001 Volumen 3, 2000 Volumen 2, 1999 Volumen 1, 1998

Journal of Porous Media

DOI: 10.1615/JPorMedia.2020033288
pages 641-661

UNCERTAINTY QUANTIFICATION FOR NATURAL CONVECTION IN RANDOM POROUS MEDIA WITH INTRUSIVE POLYNOMIAL CHAOS EXPANSION

Changwei Jiang
School of Energy and Power Engineering, Changsha University of Science and Technology, Changsha 410114, China; and Key Laboratory of Efficient and Clean Energy Utilization, College of Hunan Province, Changsha 410114, China
Yi Jiang
School of Energy and Power Engineering, Changsha University of Science and Technology, Changsha 410114, China
Er Shi
School of Energy and Power Engineering, Changsha University of Science and Technology, Changsha 410114, China

ABSTRAKT

The paper presents a framework for the construction of a stochastic projection method for the natural convection in random porous media under the local thermal nonequilibrium conditions. In this approach, the input stochastic field (porosity) is represented by the Karhunen−Loeve expansion and the output fields (e.g., velocity, pressure, fluid phase temperature, and solid phase temperature) are expressed by the polynomial chaos expansion. Using the spectral decomposition, the stochastic problem in random porous media is reformulated to a set of deterministic problems to be solved for each polynomial chaos. A stochastic projection method is implemented to obtain the chaos coefficients in the corresponding deterministic governing equations and the statistics of the numerical solution is obtained. The prediction results are compared against results obtained using a Monte Carlo method. Excellent agreement between these results indicates the efficiency and accuracy of the proposed method.

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