%0 Journal Article
%A Berger, Julien
%A Guernouti, S.
%A Woloszyn, M.
%D 2019
%I Begell House
%K model reduction method, proper generalized decomposition, proper orthogonal decomposition, heat and moisture transfer
%N 3
%P 363-385
%R 10.1615/JPorMedia.2019029049
%T EVALUATING MODEL REDUCTION METHODS FOR HEAT AND MASS TRANSFER IN POROUS MATERIALS: PROPER ORTHOGONAL DECOMPOSITION AND PROPER GENERALIZED DECOMPOSITION
%U http://dl.begellhouse.com/journals/49dcde6d4c0809db,3ec9f1a97eac7027,3583da414abaea15.html
%V 22
%X 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.
%8 2019-03-19