Because the heat loss through thermal bridges in buildings envelopes is still a weak point, buildings without any solutions against its issue can affect significant heat losses and thus higher heating energy consumption [1]. Therefore, heat bridges should be considered as an important factor in terms of energy consumption in buildings. In particular, the heat bridges in existing buildings may differ from the heat bridge performance indicator theoretically derived depending on the construction shapes/materials and degree of deterioration [2]. Therefore, quantitative evaluation of on-site thermal bridges is necessary to improve and identify the insulation performance of buildings [3]. In general, methods assumed with steady state conditions, such as the average method used for on-site assessment, do not take into account dynamic boundary conditions based on actual weather conditions, so a long measurement time is required [4]. In addition, from a statistical perspective, it is difficult to estimate the uncertainty of the measurement results of the results because the traditional frequency approach derives a single value from the results. As a way to resolve these problems, studies have recently been actively carried out that have introduced the Bayesian approach to the reverse heat transfer model [5]. The purpose of this study is to apply Bayesian methodologies to heat transfer models to derive quantitative indicators of heat bridges in the form of probability distribution. The data of the heat bridges is obtained by Infrared thermography and the collected data is applied as a boundary condition of the Bayesian heat transfer model. The thermal parameters to be obtained are then derived in the form of probability distributions through the Bayesian approach. The heat transfer model consists of a finite difference method form based on the Euler method. In addition, for ease of on-site evaluation, the input variables of the heat transfer model assume constant surface heat transfer coefficients, and the surface temperature, ambient air temperature, and indoor heat flow rate are used. This result provides a probability distribution of thermal parameters for estimating the steady state indicator of the thermal bridge.[1]&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T. G. Theodosiou at al , "The impact of thermal bridges on the energy demand of buildings with double brick wall constructions, Energy and Buildings", Volume 40, pp 2083-2089, 2008.[2]&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Fabio Bisegna at al, “Evaluation of green buildings’ overall performance through in situ monitoring and simulations,” Energies, 6(12):6525-6547 , 2013.[3]&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Pieter de Wilde at al, “The gap between predicted and measured energy performance of buildings: A framework for investigation,” Automation in Construction, Volume 41, Pages 40-49, 2014.[4]&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Pieter de Wilde at al, “Inferring the thermal resistance and effective thermal mass of a wall using frequent temperature and heat flux measurements,” Energy and Buildings, Volume 78, Pages 10-16, 2014.[5]&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Marco Iglesias at al, “Bayesian inferences of the thermal properties of a wall using temperature and heat flux measurements,” International Journal of Heat and Mass Transfer, Volume 116, Pages 417-431, 2018.

Bayesian Inference for Incidence Factor of the Thermal Bridge Using In-Situ Measurement Infrared Thermography

Paper Proceedings: https://asmedigitalcollection.asme.org/ES/ES2021/volume/84881

ASME 2021 Energy Sustainability (ES2021)

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