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Thermal Bridge Modeling and a Dynamic Analysis Method Using the Analogy of a Steady-State Thermal Bridge Analysis and System Identification Process for Building Energy Simulation: Methodology and Validation

Author

Listed:
  • Heegang Kim

    (Department of Architecture and Architectural Engineering, Seoul National University, Seoul 08826, Korea)

  • Myoungsouk Yeo

    (Department of Architecture and Architectural Engineering, Seoul National University, Seoul 08826, Korea)

Abstract

It is challenging to apply heat flow through a thermal bridge, which requires the analysis of 2D or 3D heat transfer to building energy simulation (BES). Research on the dynamic analysis of thermal bridges has been underway for many years, but their utilization remains low in BESs. This paper proposes a thermal bridge modeling and a dynamic analysis method that can be easily applied to BESs. The main idea begins with an analogy of the steady-state analysis of thermal bridges. As with steady-state analysis, the proposed method first divides the thermal bridge into a clear wall, where the heat flow is uniform, and the sections that are not the clear wall (the thermal bridge part). For the clear wall part, the method used in existing BESs is applied and analyzed. The thermal bridge part (TB part) is modeled with the linear time-invariant system (LTI system) and the system identification process is performed to find the transfer function. Then, the heat flow is obtained via a linear combination of the two parts. This method is validated by comparing the step, sinusoidal and annual outdoor temperature response of the finite differential method (FDM) simulation. When the thermal bridge was modeled as a third-order model, the root mean square error (RMSE) of annual heat flow with the FDM solution of heat flow through the entire wall was about 0.1 W.

Suggested Citation

  • Heegang Kim & Myoungsouk Yeo, 2020. "Thermal Bridge Modeling and a Dynamic Analysis Method Using the Analogy of a Steady-State Thermal Bridge Analysis and System Identification Process for Building Energy Simulation: Methodology and Vali," Energies, MDPI, vol. 13(17), pages 1-22, August.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4422-:d:404696
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    References listed on IDEAS

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    1. Harish, V.S.K.V. & Kumar, Arun, 2016. "A review on modeling and simulation of building energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1272-1292.
    2. Paola Iodice & Nicola Massarotti & Alessandro Mauro, 2016. "Effects of Inhomogeneities on Heat and Mass Transport Phenomena in Thermal Bridges," Energies, MDPI, vol. 9(3), pages 1-21, February.
    3. Foucquier, Aurélie & Robert, Sylvain & Suard, Frédéric & Stéphan, Louis & Jay, Arnaud, 2013. "State of the art in building modelling and energy performances prediction: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 272-288.
    4. David Bienvenido-Huertas & Juan Antonio Fernández Quiñones & Juan Moyano & Carlos E. Rodríguez-Jiménez, 2018. "Patents Analysis of Thermal Bridges in Slab Fronts and Their Effect on Energy Demand," Energies, MDPI, vol. 11(9), pages 1-18, August.
    5. AL-Saadi, Saleh Nasser & Zhai, Zhiqiang (John), 2013. "Modeling phase change materials embedded in building enclosure: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 659-673.
    6. Nuno Simões & Joana Prata & António Tadeu, 2019. "3D Dynamic Simulation of Heat Conduction through a Building Corner Using a BEM Model in the Frequency Domain," Energies, MDPI, vol. 12(23), pages 1-27, December.
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