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

De Simon, Lia and Iglesias, Marco and jones, Benjamin and Wood, Christopher (2018) Quantifying uncertainty in thermophysical properties of walls by means of Bayesian inversion. Energy and Buildings . ISSN 1872-6178 (In Press)

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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.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/939768
Keywords: U-value, Bayesian framework, heat transfer, inverse problems, building performance
Schools/Departments: University of Nottingham, UK > Faculty of Engineering > Department of Architecture and Built Environment
University of Nottingham, UK > Faculty of Science > School of Mathematical Sciences
Depositing User: Iglesias Hernandez, Marco
Date Deposited: 18 Jul 2018 11:49
Last Modified: 04 May 2020 19:41
URI: http://eprints.nottingham.ac.uk/id/eprint/53006

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