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Quantifying uncertainty in thermophysical properties of walls by means of Bayesian inversion

De Simon, Lia; Iglesias, Marco; Jones, Benjamin; Wood, Christopher

Authors

Lia De Simon lia.desimon@nottingham.ac.uk



Abstract

We introduce a computational framework to statistically infer thermophysical properties of any given wall from in-situ measurements of air temperature and surface heat fluxes. The proposed framework uses these measurements, within a Bayesian calibration approach, to sequentially infer input parameters of a one-dimensional heat diffusion model that describes the thermal performance of the wall. These inputs include spatially-variable functions that characterise the thermal conductivity and the volumetric heat capacity of the wall. We encode our computational framework in an algorithm that sequentially updates our probabilistic knowledge of the thermophysical properties as new measurements become available, and thus enables an on-the-fly uncertainty quantification of these properties. In addition, the proposed algorithm enables us to investigate the effect of the discretisation of the underlying heat diffusion model on the accuracy of estimates of thermophysical properties and the corresponding predictive distributions of heat flux. By means of virtual/synthetic and real experiments we show the capabilities of the proposed approach to (i) characterise heterogenous thermophysical properties associated with, for example, unknown cavities and insulators; (ii) obtain rapid and accurate uncertainty estimates of effective thermal properties (e.g. thermal transmittance); and (iii) accurately compute an statistical description of the thermal performance of the wall which is, in turn, crucial in evaluating possible retrofit measures.

Citation

De Simon, L., Iglesias, M., Jones, B., & Wood, C. (2018). Quantifying uncertainty in thermophysical properties of walls by means of Bayesian inversion. Energy and Buildings, 177, 220-245. doi:10.1016/j.enbuild.2018.06.045

Journal Article Type Article
Acceptance Date Jun 21, 2018
Online Publication Date Aug 8, 2018
Publication Date Oct 15, 2018
Deposit Date Jul 18, 2018
Publicly Available Date Aug 9, 2019
Journal Energy and Buildings
Print ISSN 0378-7788
Electronic ISSN 1872-6178
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 177
Pages 220-245
DOI https://doi.org/10.1016/j.enbuild.2018.06.045
Keywords U-value, Bayesian framework, heat transfer, inverse problems, building performance
Public URL http://eprints.nottingham.ac.uk/id/eprint/53006
Publisher URL https://www.sciencedirect.com/science/article/pii/S0378778817334035
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0

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