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Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
Journal of Porous Media
Импакт фактор: 1.49 5-летний Импакт фактор: 1.159 SJR: 0.43 SNIP: 0.671 CiteScore™: 1.58

ISSN Печать: 1091-028X
ISSN Онлайн: 1934-0508

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

Journal of Porous Media

DOI: 10.1615/JPorMedia.2019029049
pages 363-385

EVALUATING MODEL REDUCTION METHODS FOR HEAT AND MASS TRANSFER IN POROUS MATERIALS: PROPER ORTHOGONAL DECOMPOSITION AND PROPER GENERALIZED DECOMPOSITION

Julien Berger
Thermal Systems Laboratory, Mechanical Engineering Graduate Program, Pontifical Catholic University of Paraná, Rua Imaculada ConceiÃgÃco, 1155, CEP: 80215-901, Curitiba - Paraná, Brazil
S. Guernouti
Cerema, Dter Ouest, Nantes, France
M. Woloszyn
Université Savoie Mont Blanc, CNRS, LOCIE, F-73000 Chambéry, France

Краткое описание

This paper explores deeper the features of model reduction methods proper orthogonal decomposition (POD) and proper generalized decomposition (PGD) applied to heat and moisture transfer in porous materials. The first method is an a posteriori one and therefore requires a previous computation of the solution using the large original model to build the reduced basis. The second one is a priori and does not need any previous computation. The reduced order model is built straightforward. Both methods aim at approaching a high-dimensional model with a low-dimensional one. Their efficiencies, in terms of accuracy, complexity reduction, and CPU time gains, are first discussed on a one-dimensional case of nonlinear coupled heat and mass transfer. The reduced order models compute accurate solutions of the problem when compared to the large original model. They also offer interesting complexity reduction: around 97% for the POD and 88% for the PGD on the case study. In further sections, the robustness of the reduced order models are tested for different boundary conditions and materials. The POD method has lack of accuracy to compute the solution when these parameters differ from the ones used for the learning step. It is also shown that PGD resolution is particularly efficient to reduce the complexity of parametric problems.


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