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A computational toolchain for the automatic generation of multiple Reduced-Order Models from CFD simulations

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  • Marzullo, Thibault
  • Keane, Marcus M.
  • Geron, Marco
  • Monaghan, Rory F.D.

Abstract

This paper describes the development of a systematic tool chain capable of automatically extracting accurate and efficient Reduced-Order Models (ROMs) from Computational Fluid Dynamics (CFD) simulations. These ROMs can then be used to support the design and operation of Near-Zero Energy Buildings (NZEB), with a higher accuracy than traditional zonal models but at a fraction of the computational cost of CFD. This study assesses the accuracy and time to solution of these ROMs when solved for appropriate Boundary Conditions (BCs), found in the built environment, in order to define the usability envelope of the automatically extracted ROMs. The parameters used in this study are inlet temperatures (K) and mass flow rates (kg/s). Results demonstrate that the absolute error can be maintained at under 0.5 K for changes in temperature of up to ±15 K, and under 0.25 K for changes in mass flow rates of up to ±45% of the original value. The results show that this method has the potential for applications in the built environment where the ROM accuracy and low computational cost can bridge a gap between low order RC models and high order CFD, further improving the energy efficiency in smart buildings.

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  • 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.
  • Handle: RePEc:eee:energy:v:180:y:2019:i:c:p:511-519
    DOI: 10.1016/j.energy.2019.05.094
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    References listed on IDEAS

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    1. Nageler, P. & Schweiger, G. & Schranzhofer, H. & Mach, T. & Heimrath, R. & Hochenauer, C., 2018. "Novel method to simulate large-scale thermal city models," Energy, Elsevier, vol. 157(C), pages 633-646.
    2. Harish, V.S.K.V. & Kumar, Arun, 2016. "Reduced order modeling and parameter identification of a building energy system model through an optimization routine," Applied Energy, Elsevier, vol. 162(C), pages 1010-1023.
    3. 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.
    4. Shaikh, Pervez Hameed & Nor, Nursyarizal Bin Mohd & Nallagownden, Perumal & Elamvazuthi, Irraivan & Ibrahim, Taib, 2014. "A review on optimized control systems for building energy and comfort management of smart sustainable buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 409-429.
    5. Allegrini, Jonas & Orehounig, Kristina & Mavromatidis, Georgios & Ruesch, Florian & Dorer, Viktor & Evins, Ralph, 2015. "A review of modelling approaches and tools for the simulation of district-scale energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1391-1404.
    6. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    7. Cai, Hanmin & Ziras, Charalampos & You, Shi & Li, Rongling & Honoré, Kristian & Bindner, Henrik W., 2018. "Demand side management in urban district heating networks," Applied Energy, Elsevier, vol. 230(C), pages 506-518.
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    Cited by:

    1. Lu, Yanyu & Dong, Jiankai & Liu, Jing, 2020. "Zonal modelling for thermal and energy performance of large space buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    2. Qibo Liu & Yimeng Zhang & Wendong Ma & Juan Ren, 2023. "Application of an Architect-Friendly Digital Design Approach to the Wind Environment of Campus Dormitory Buildings," Sustainability, MDPI, vol. 15(12), pages 1-25, June.

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    Keywords

    CFD; ROM; Buildings; Zonal model;
    All these keywords.

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