Review of Reduced Order Models for Heat and Moisture Transfer in Building Physics with Emphasis in PGD Approaches

Berger, Julien; Mendes, Nathan; Guernouti, Sihem; Woloszyn, Monika; Chinesta, Francisco

doi:10.1007/s11831-016-9184-1

Cited by 15 publications

(13 citation statements)

References 83 publications

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“…Then, to decrease the computational effort of the direct numerical model, model reduction techniques can be employed. Several methods are reported in the literature to model the physical phenomena in building walls [21]. Among all, the Modal Identification Method (MIM) demonstrated successful applications for inverse problem.…”

Section: Estimation Of the Thermal Properties Of An Historic Building Wall By Combining Modal Identification Methods And Optimal Experimementioning

confidence: 99%

Estimation of the thermal properties of an historic building wall by combining modal identification method and optimal experiment design

Berger

Kadoch

2020

Building and Environment

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The estimation of wall thermal properties by in situ measurement enables to increase the reliability of the model predictions for building energy efficiency. Nevertheless, retrieving the unknown parameters has an important computational cost. Indeed, several computations of the heat transfer problem are required to identify these thermal properties. To handle this drawback, an innovative approach is investigated. The first step is to search the optimal experiment design among the sequence of observation of several months. A reduced sequence of observations of three days is identified which guarantees to estimate the parameter with the maximum accuracy. Moreover, the inverse problem is only solved for this short sequence. To decrease further the computational efforts, a reduced order model based on the modal identification method is employed. This a posteriori model reduction method approximates the solution with a lower degree of freedom. The whole methodology is illustrated to estimate the thermal diffusivity of an historical building that has been monitored with temperature sensors for several months. The computational efforts is cut by five. The estimated parameter improves the reliability of the predictions of the wall thermal efficiency.

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Section: Estimation Of the Thermal Properties Of An Historic Building Wall By Combining Modal Identification Methods And Optimal Experimementioning

confidence: 99%

Estimation of the thermal properties of an historic building wall by combining modal identification method and optimal experiment design

Berger

Kadoch

2020

Building and Environment

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“…A detailed tutorial of PGD is proposed by Chinesta, Keunings and Leygue [41], and an application of PGD for simulating thermal processes is provided by Pruliere et al [42]. In addition, two reviews of PGD are provided by Chinesta, Ammar and Cueto and Berger et al [27,43], with attention for general and building physical engineering applications respectively.…”

Section: Proper Generalized Decompositionmentioning

confidence: 99%

“…To this aim, model order reduction techniques are investigated as improvement on static surrogate models in this paper. Different model order reduction methods have already found their way into building performance simulation [23][24][25][26][27]. In this paper two of these methods (Proper Orthogonal Decomposition, POD; Proper Generalized Decomposition, PGD) are investigated, by applications on deterministic and probabilistic analyses of building component thermal performance.…”

Section: Introductionmentioning

confidence: 99%

Model order reduction for efficient deterministic and probabilistic assessment of building envelope thermal performance

Hou

Roels

Janssen

2020

Energy and Buildings

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To lessen the computational expense of building energy simulations, the potential of model order reduction methods for assessing building envelope thermal performance is assessed, through both deterministic and probabilistic case studies of modelling heat transfer through a massive masonry wall. More specifically, two model order reduction methodsproper orthogonal decomposition (POD) and proper generalized decomposition (PGD)are investigated: therefore their calculation accuracies and computational costs are compared with a standard finite element method (FEM). The outcomes show that the POD model strongly outperforms the PGD and FEM models when deterministically simulating wall heat transfer. Additionally, the use of POD to simulate a problem differing from the training simulation can provide an accurate result, and hence the robustness and flexibility of POD is confirmed. This advantage thus expands the application scope of POD for simulating building envelope thermal performance with variable input parameters, rendering it excellent to replace a time-consuming original model in the probabilistic analysis. For the probabilistic case study, it is shown that the sampling errors are much more dominant in the accuracy, implying that the deterministic accuracy of the different models plays a much smaller role, and hence has a very limited effect on the overall convergence behavior of different models. Therefore, the use of POD with a smaller number of modes is strongly suggested in probabilistic analyses to reduce the overall computational expense as much as possible.

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“…[52], nonlinear stochastic problems (Burgers equation, 2D nonlinear diffusion problems) [53], multi-scale and multiphysics problems (visco plasticity, damage, etc.) [16,51], computational fluid dynamics (anisotherm Navier-Stokes problems) [31], and, more recently to, heat and moisture transfer in building materials [15].…”

Section: Proper Generalised Decompositionmentioning

confidence: 99%

Advanced Reduced-Order Models for Moisture Diffusion in Porous Media

et al. 2018

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It is of great concern to produce numerically efficient methods for moisture diffusion through porous media, capable of accurately calculate moisture distribution with a reduced computational effort. In this way, model reduction methods are promising approaches to bring a solution to this issue since they do not degrade the physical model and provide a significant reduction of computational cost. Therefore, this article explores in details the capabilities of two model-reduction techniques -the Spectral Reduced-Order Model (Spectral-ROM) and the Proper Generalised Decomposition (PGD) -to numerically solve moisture diffusive transfer through porous materials. Both approaches are applied to three different problems to provide clear examples of the construction and use of these reduced-order models. The methodology of both approaches is explained extensively so that the article can be used as a numerical benchmark by anyone interested in building a reduced-order model for diffusion problems in porous materials. Linear and non-linear unsteady behaviors of unidimensional moisture diffusion are investigated. The last case focuses on solving a parametric problem in which the solution depends on space, time and the diffusivity properties. Results have highlighted that both methods provide accurate solutions and enable to reduce significantly the order of the model around ten times lower than the large original model. It also allows an efficient computation of the physical phenomena with an error lower than 10 −2 when compared to a reference solution.

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Review of Reduced Order Models for Heat and Moisture Transfer in Building Physics with Emphasis in PGD Approaches

Cited by 15 publications

References 83 publications

Estimation of the thermal properties of an historic building wall by combining modal identification method and optimal experiment design

Estimation of the thermal properties of an historic building wall by combining modal identification method and optimal experiment design

Model order reduction for efficient deterministic and probabilistic assessment of building envelope thermal performance

Advanced Reduced-Order Models for Moisture Diffusion in Porous Media

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