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Volume element model for 3D dynamic building thermal modeling and simulation

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  • Yang, S.
  • Pilet, T.J.
  • Ordonez, J.C.

Abstract

We present herein the development, experimental validation, and application of a volume element model for 3D dynamic building thermal simulation. The 3D spatial domain in the VEM is discretized with lumped hexahedral elements using ray crossings and ray/triangle intersection techniques that yield sufficiently accurate geometric representation of a building. Subsequently, energy balance is applied to each element in the mesh, and the resulting system of ordinary differential equations is integrated in time to obtain spatiotemporal indoor temperature and relative humidity fields. In this work, we adjusted the model by comparing the simulated indoor air temperatures to the experimental measurements as we calibrated model parameters with high uncertainty. The adjusted model was validated using different experimental data sets, and the numerical results were in a good agreement with the measurements. We employed the validated model to conduct a case study in which the sensible heat gain and loss as well as the time lag were evaluated as functions of different envelope thermal masses. Results showed the trivial effect of floor thermal mass on heat gain, and the changes in sensible heat gain and envelope thermal mass were not linearly proportional, alluding the existence of an optimal envelope design.

Suggested Citation

  • Yang, S. & Pilet, T.J. & Ordonez, J.C., 2018. "Volume element model for 3D dynamic building thermal modeling and simulation," Energy, Elsevier, vol. 148(C), pages 642-661.
    • Handle: RePEc:eee:energy:v:148:y:2018:i:c:p:642-661
    DOI: 10.1016/j.energy.2018.01.156
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    References listed on IDEAS

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    1. Wang, Qinpeng & Augenbroe, Godfried & Kim, Ji-Hyun & Gu, Li, 2016. "Meta-modeling of occupancy variables and analysis of their impact on energy outcomes of office buildings," Applied Energy, Elsevier, vol. 174(C), pages 166-180.
    2. Capizzi, Giacomo & Sciuto, Grazia Lo & Cammarata, Giuliano & Cammarata, Massimiliano, 2017. "Thermal transients simulations of a building by a dynamic model based on thermal-electrical analogy: Evaluation and implementation issue," Applied Energy, Elsevier, vol. 199(C), pages 323-334.
    3. Razmara, M. & Maasoumy, M. & Shahbakhti, M. & Robinett, R.D., 2015. "Optimal exergy control of building HVAC system," Applied Energy, Elsevier, vol. 156(C), pages 555-565.
    4. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
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    Cited by:

    1. Yang, S. & Sensoy, T.S. & Ordonez, J.C., 2018. "Dynamic 3D volume element model of a parabolic trough solar collector for simulation and optimization," Applied Energy, Elsevier, vol. 217(C), pages 509-526.
    2. Giovanni Barone & Annamaria Buonomano & Cesare Forzano & Adolfo Palombo, 2019. "Building Energy Performance Analysis: An Experimental Validation of an In-House Dynamic Simulation Tool through a Real Test Room," Energies, MDPI, vol. 12(21), pages 1-39, October.
    3. Jan K. Kazak & Małgorzata Świąder, 2018. "SOLIS—A Novel Decision Support Tool for the Assessment of Solar Radiation in ArcGIS," Energies, MDPI, vol. 11(8), pages 1-12, August.
    4. Si Chen & Daniel Friedrich & Zhibin Yu & James Yu, 2019. "District Heating Network Demand Prediction Using a Physics-Based Energy Model with a Bayesian Approach for Parameter Calibration," Energies, MDPI, vol. 12(18), pages 1-19, September.

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