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Energy load prediction on structures and buildings-Effect of numerical model complexity on simulation of heat fluxes across the structure/environment interface

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  • Görtz, J.
  • Jürgensen, J.
  • Stolz, D.
  • Wieprecht, S.
  • Terheiden, K.

Abstract

Civil structures, including buildings, constantly exchange heat fluxes with the environment. This includes heat exchange through conduction, convection, radiation and latent heat. A detailed description of the heat fluxes and corresponding transport processes is essential to estimate cooling and heating requirements and inhibit extreme local strains. Moreover, the temperature distribution inside a structure can be predicted by assessing the thermal loads of the environment with respect to the particular material properties of the structure. This is especially substantial for massive structures as thermal stresses can cause cracking. However, in the design of low-energy and passive houses, knowledge about the incoming and outgoing heat fluxes is also of great importance. Considering the numerous meteorological impacts on civil structures, the exact determination of the heat fluxes is quite complex. Most of the studies from literature on heat exchange of civil structures with the environment rely on multiple, not well-founded hypotheses to compensate for the lack of precise data. Therefore, this work aims to improve the understanding and quantification of the heat fluxes between a civil structure and the environment. Various measurement devices have been installed on a gravity dam to capture spatially distributed environmental impacts as well as the temperature distribution inside the structure. This data is used as input to model, quantify and evaluate the governing heat fluxes and thermal transport processes. It can be shown that the temperature fields in civil structures can be modelled even under complex environmental conditions with high accuracy when all essential key processes are incorporated. Furthermore, it is concluded that some simplified models can also yield a good fit even when the modelling parameters are extended beyond their actual definition.

Suggested Citation

  • Görtz, J. & Jürgensen, J. & Stolz, D. & Wieprecht, S. & Terheiden, K., 2022. "Energy load prediction on structures and buildings-Effect of numerical model complexity on simulation of heat fluxes across the structure/environment interface," Applied Energy, Elsevier, vol. 326(C).
  • Handle: RePEc:eee:appene:v:326:y:2022:i:c:s0306261922012387
    DOI: 10.1016/j.apenergy.2022.119981
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    References listed on IDEAS

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    1. Zhang, Chaobo & Li, Junyang & Zhao, Yang & Li, Tingting & Chen, Qi & Zhang, Xuejun & Qiu, Weikang, 2021. "Problem of data imbalance in building energy load prediction: Concept, influence, and solution," Applied Energy, Elsevier, vol. 297(C).
    2. Menezes, Anna Carolina & Cripps, Andrew & Bouchlaghem, Dino & Buswell, Richard, 2012. "Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap," Applied Energy, Elsevier, vol. 97(C), pages 355-364.
    3. Ciulla, G. & D'Amico, A., 2019. "Building energy performance forecasting: A multiple linear regression approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    4. Yu Hu & Zheng Zuo & Qingbin Li & Yunling Duan, 2013. "Boolean-Based Surface Procedure for the External Heat Transfer Analysis of Dams during Construction," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-17, December.
    5. Zhang, Tiantian & Yang, Hongxing, 2019. "Heat transfer pattern judgment and thermal performance enhancement of insulation air layers in building envelopes," Applied Energy, Elsevier, vol. 250(C), pages 834-845.
    6. Buonomano, Annamaria & Palombo, Adolfo, 2014. "Building energy performance analysis by an in-house developed dynamic simulation code: An investigation for different case studies," Applied Energy, Elsevier, vol. 113(C), pages 788-807.
    7. Lee, Louis S.H. & Jim, C.Y., 2019. "Energy benefits of green-wall shading based on novel-accurate apportionment of short-wave radiation components," Applied Energy, Elsevier, vol. 238(C), pages 1506-1518.
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