Bayesian inversion for in-situ thermal characterisation of walls in the presence of thermal anomalies

Iglesias, Marco; Li, Xue; Sovetova, Meruyert; Wu, Yupeng

Abstract

This paper develops a Bayesian inverse modelling framework for the in-situ characterisation of walls' thermal performance in the presence of thermal anomalies subject to uncertainty. For any given wall, the proposed framework uses in-situ contact measurements of temperature and heat flux in order to approximate the (posterior) distribution of a set of parameters which are inputs of a 3D heat transfer model of the wall that accounts for the presence of unknown anomalies. The set of inputs that we infer include (i) the geometry of the thermal anomaly as well was its material properties, (ii) the as-built thermophysical properties of the clear part (i.e. without anomaly) of the wall (iii) surface resistances and (iv) modelling errors that arise from unaccounted sources of uncertainty in the boundary conditions. The geometry of the unknown thermal anomaly is parameterised with a level-set function that is inferred within the proposed Bayesian approach. In order to incorporate prior statistical information of the thermal anomaly, a Gaussian process conditioned on measurements from thermal images is employed to build a prior on the level-set function. The posterior distribution of inferred inputs is approximated via the implementation of an Ensemble Kalman Inversion algorithm that provides stable and accurate approximations of the posterior. This paper also proposes a methodology that uses the inferred 3D model to derive a thermally equivalent 1D model of the wall that is suitable for its integration with existing building energy performance software. The proposed inverse modelling technique is experimentally validated by characterising the thermal performance of an insulation panel in the presence of a thermal anomaly. Our computations show that the proposed approach produces a 3D model that accurately describes thermal performance, and a derived equivalent 1D model that effectively reproduces the dynamic thermal performance of the insulation panel in the presence of the thermal anomaly.

Citation

Iglesias, M., Li, X., Sovetova, M., & Wu, Y. (2024). Bayesian inversion for in-situ thermal characterisation of walls in the presence of thermal anomalies. Energy and Buildings, 319, Article 114558. https://doi.org/10.1016/j.enbuild.2024.114558

• Journal Article Type: Article
• Acceptance Date: Jul 12, 2024
• Online Publication Date: Jul 17, 2024
• Publication Date: Sep 15, 2024
• Deposit Date: Aug 8, 2024
• Publicly Available Date: Aug 14, 2024
• Journal: Energy and Buildings
• Print ISSN: 0378-7788
• Electronic ISSN: 1872-6178
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 319
• Article Number: 114558
• DOI: https://doi.org/10.1016/j.enbuild.2024.114558
• Public URL: https://nottingham-repository.worktribe.com/output/37320170
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0378778824006741?via%3Dihub