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Guidelines for developing efficient thermal conduction and storage models within building energy simulations

Author

Listed:
  • Hillary, Jason
  • Walsh, Ed
  • Shah, Amip
  • Zhou, Rongliang
  • Walsh, Pat

Abstract

Improving building energy efficiency is of paramount importance due to the large proportion of energy consumed by thermal operations. Consequently, simulating a building's environment has gained popularity for assessing thermal comfort and design. The extended timeframes and large physical scales involved necessitate compact modelling approaches. The accuracy of such simulations is of chief concern, yet there is little guidance offered on achieving accurate solutions whilst mitigating prohibitive computational costs. Therefore, the present study addresses this deficit by providing clear guidance on discretisation levels required for achieving accurate but computationally inexpensive models. This is achieved by comparing numerical models of varying discretisation levels to benchmark analytical solutions with prediction accuracy assessed and reported in terms of governing dimensionless parameters, Biot and Fourier numbers, to ensure generality of findings. Furthermore, spatial and temporal discretisation errors are separated and assessed independently. Contour plots are presented to intuitively determine the optimal discretisation levels and time-steps required to achieve accurate thermal response predictions. Simulations derived from these contour plots were tested against various building conditions with excellent agreement observed throughout. Additionally, various scenarios are highlighted where the classical single lumped capacitance model can be applied for Biot numbers much greater than 0.1 without reducing accuracy.

Suggested Citation

  • Hillary, Jason & Walsh, Ed & Shah, Amip & Zhou, Rongliang & Walsh, Pat, 2017. "Guidelines for developing efficient thermal conduction and storage models within building energy simulations," Energy, Elsevier, vol. 125(C), pages 211-222.
  • Handle: RePEc:eee:energy:v:125:y:2017:i:c:p:211-222
    DOI: 10.1016/j.energy.2017.02.127
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Shen, Pengyuan & Braham, William & Yi, Yunkyu, 2018. "Development of a lightweight building simulation tool using simplified zone thermal coupling for fast parametric study," Applied Energy, Elsevier, vol. 223(C), pages 188-214.
    2. Marzullo, Thibault & Keane, Marcus M. & Geron, Marco & Monaghan, Rory F.D., 2019. "A computational toolchain for the automatic generation of multiple Reduced-Order Models from CFD simulations," Energy, Elsevier, vol. 180(C), pages 511-519.
    3. Abokersh, Mohamed Hany & Spiekman, Marleen & Vijlbrief, Olav & van Goch, T.A.J. & Vallès, Manel & Boer, Dieter, 2021. "A real-time diagnostic tool for evaluating the thermal performance of nearly zero energy buildings," Applied Energy, Elsevier, vol. 281(C).
    4. Hillary, Jason & Walsh, Ed & Shah, Amip & Zhou, Rongliang & Walsh, Pat, 2018. "An optimised logarithmic discretisation approach for accurate and efficient compact thermal models," Energy, Elsevier, vol. 147(C), pages 995-1006.

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