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Research Article

Numerical analysis of horizontal temperature distribution in large buildings by thermo-aeraulic zonal approach

Nisrine Laghmich1, Zaid Romani2( ), Remon Lapisa3, Abdeslam Draoui1
FSTT – ETTE – UAE/U10FST - Abdelmalek Essaâdi University, BP 416 Tangier, Morocco
LaRAT, National School of Architecture of Tetouan, Morocco
Faculty Engineering, Universitas Negeri Padang, Padang, Indonesia
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Abstract

The commercial and public services sectors including shopping centers, worship buildings, theatres, and other types, account for more than 20% of the electricity consumption in the world. These building typologies are characterized by large spaces and high and temporary occupation. Besides, the horizontal temperature distribution in these buildings becomes one of the important parameters on occupant's comfort and energy efficiency. In the present study, a thermo-aeraulic zonal model using TRNSYS and CONTAM simulation tools is developed to analyze the spatial temperature distribution in a large building. Parametric studies relating to mesh discretization of building volume are performed to optimize the computational time and convergence. Extensive computational simulation is carried out to analyze the impact of building height, internal loads, natural ventilation and climatic conditions on the spatial temperature distribution, building energy performance, and thermal comfort. The developed simulation model in this study is effective to predict the horizontal temperature distribution with reasonable computation time compared to CFD simulations. The results show that the internal heat gains lead to an increase in the horizontal temperature gradient which should not be negligible especially in the case of large buildings. On the other side, natural night ventilation can reduce the peak tempearture in building by 3 ℃ for normal occupation building with limited internal gains. Furthermore, good spatial temperature distribution can decrease annual building energy needs about 32%. It can be helpful for architects and building developers to make an optimal choice regarding to building envelope and HVAC design.

Keywords

building simulationlarge buildingmesh grid discretizationthermo-aeraulic zonal modelspatial temperature distribution

References

 
ASHRAE (2010). ASHRAE Standard: Energy Standard for Buildings Except Low-Rise Residential Buildings. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.
 

Bozonnet E, Belarbi R, Allard F (2005). Modelling solar effects on the heat and mass transfer in a street canyon, a simplified approach. Solar Energy, 79: 10–24.
Crossref Google Scholar
 

Cheng Y, Yang B, Lin Z, et al. (2018). Cooling load calculation methods in spaces with stratified air: A brief review and numerical investigation. Energy and Buildings, 165: 47–55.
Crossref Google Scholar
 

Ciulla G, D'Amico A (2019). Building energy performance forecasting: A multiple linear regression approach. Applied Energy, 253: 113500.
Crossref Google Scholar
 
Dols WS, Polidoro BJ (2007). NIST Technical Note 1887. CONTAM User Guide and Program Documentation.
 

Dols WS, Wang L, Emmerich SJ, et al. (2015). Development and application of an updated whole-building coupled thermal, airflow and contaminant transport simulation program (TRNSYS/CONTAM). Journal of Building Performance Simulation, 8: 326-337.
Crossref Google Scholar
 
Ebert R (1993). Développement d'un environnement de simulation de systèmes complexes. Application Aux bâtiments. PhD Thesis, Ecole Nationale des Ponts et Chaussées, France. (in French)
 

Fang H, Zhao D, Tan G, et al. (2017). Study of underfloor air distribution using zonal model-based simulation and experimental measurements. Energy and Buildings, 152: 96–107.
Crossref Google Scholar
 

Feustel HE, Dieris J (1992). A survey of airflow models for multizone structures. Energy and Buildings, 18: 79–100.
Crossref Google Scholar
 

González-Briones A, de la Prieta F, Mohamad M, et al. (2018). Multi-agent systems applications in energy optimization problems: A state-of-the-art review. Energies, 11: 1928.
Crossref Google Scholar
 

Haghighat F, Jiang Z, Wang JCY, et al. (1992). Air movement in buildings using computational fluid dynamics. Journal of Solar Energy Engineering, 114: 84–92.
Crossref Google Scholar
 

Haghighat F, Li Y, Megri AC (2001). Development and validation of a zonal model — POMA. Building and Environment, 36: 1039–1047.
Crossref Google Scholar
 

Hensen J (1999). Comparison of coupled and decoupled solutions for temperature and air flow in a building. ASHRAE Transactions, 105(2): 962–969.
Google Scholar
 
Horowitz S, Maguire J, Tabares-Velasco PC, et al. (2016). EnergyPlus and SEEM Modeling Enhancements via Software-to-Software Comparison Using NREL's BEopt Test Suite. Available athttp://www.nrel.gov/publications. https://doi.org/10.2172/1295388
Crossref
 

Huang C, Li R, Liu Y, et al. (2019). Study of indoor thermal environment and stratified air-conditioning load with low-sidewall air supply for large space based on Block-Gebhart model. Building and Environment, 147: 495–505.
Crossref Google Scholar
 
IEA (2016). Towards a Zero-Emission, Efficient, and Resilient Buildings and Construction Sector. Global Status Report. Available at https://www.worldgbc.org/sites/default/files/UNEP%20188_GABC_en%20(web).pdf.
 

Kan EM, Kan SL, Soh Y, et al. (2016). Multi-agent control system with intelligent optimisation for building energy and comfort management. International Journal of Automation and Logistics, 2: 60.
Crossref Google Scholar
 
Klein SA (2018). TRNSYS 18: A Transient System Simulation Program. Solar Energy Laboratory, University of Wisconsin, Madison, USA. Available at http://www.trnsys.com/.
 
Knox M, Clevenger C (2013). Energy modeling: A tutorial and introduction to eQUEST.
 

Kontoleon KJ, Eumorfopoulou EA (2008). The influence of wall orientation and exterior surface solar absorptivity on time lag and decrement factor in the Greek region. Renewable Energy, 33: 1652–1664.
Crossref Google Scholar
 

Laghmich N, Romani Z, Lapisa R, et al. (2019). The impact of internal gains on thermal stratification for public buildings. IOP Conference Series: Materials Science and Engineering, 609: 042090.
Crossref Google Scholar
 
Lapisa R, Abadie M, Bozonnet E, et al. (2014). Numerical analysis of thermal stratification modelling effect on comfort for the case of a commercial low-rise building. In: Proceedings of the 13th International Conference on Indoor Air Quality and Climate, Hong Kong, China.
 

Lapisa R, Bozonnet E, Salagnac P, et al. (2018). Optimized design of low-rise commercial buildings under various climates—Energy performance and passive cooling strategies. Building and Environment, 132: 83–95.
Crossref Google Scholar
 
Lebrun J (1970). Exigences Physiologiques et Modalités Physiques de La Climatisation Par Source Statique Concentrée. PhD Thesis, University of Liège, Belgium. (in French)
 
Lefebvre G (1987). Analyse et Reduction Modales D'un Modele de Comportement Thermique de Batiment. PhD Thesis, Université Paris VI. (in French)
 

Marzullo T, Keane MM, Geron M, et al. (2019). A computational toolchain for the automatic generation of multiple Reduced- Order Models from CFD simulations. Energy, 180: 511–519.
Crossref Google Scholar
 

Megri AC (2007). Building load and energy simulation programs and the design process. International Journal of Ventilation, 6: 177–192.
Crossref Google Scholar
 
Mendonça KC, Inard C (2002). A Zonal Model for Predicting Simultaneous Heat and Moisture Transfer in Buildings. In: Proceedings of Indoor Air Conference.
 
Morrison Hershfield (2018). Guide to Low Thermal Energy Demand for Large Buildings. Fort St. John, BC, Canada: BC Housing.
 

Ndione J, Yoshino H, Mochida A (2008). New method of coupling multizone and CFD for building simulation. Journal of Asian Architecture and Building Engineering, 7: 125–129.
Crossref Google Scholar
 

Nematchoua MK, Yvon A, Roy SEJ, et al. (2019). A review on energy consumption in the residential and commercial buildings located in tropical regions of Indian Ocean: A case of Madagascar island. Journal of Energy Storage, 24: 100748.
Crossref Google Scholar
 
Ng LC, Persily AK (2011). Airflow and indoor air quality analyses capabilities of energy simulation software. In: Proceedings of the 12th International Conference on Indoor Air Quality and Climate.
 
Ngendakumana P (1978). Modélisation Simplifiée Du Comportement Thermique D'un Bâtiment et Vérifications Expérimentales. PhD Thesis, University of Liege. (in French)
 
Nunes P (2019). Performance Des Équipements: Vers Un Confort Accru Des Usagers. Editions XPAIR. (in French)
 

Pan Y, Li Y, Huang Z, et al. (2010). Study on simulation methods of atrium building cooling load in hot and humid regions. Energy and Buildings, 42: 1654–1660.
Crossref Google Scholar
 

Ren Z, Stewart J (2003). Simulating air flow and temperature distribution inside buildings using a modified version of COMIS with sub-zonal divisions. Energy and Buildings, 35: 257–271.
Crossref Google Scholar
 

Sadineni SB, Madala S, Boehm RF (2011). Passive building energy savings: a review of building envelope components. Renewable and Sustainable Energy Reviews, 15: 3617–3631.
Crossref Google Scholar
 

Schulze T, Gürlich D, Eicker U (2018). Performance assessment of controlled natural ventilation for air quality control and passive cooling in existing and new office type buildings. Energy and Buildings, 172: 265–278.
Crossref Google Scholar
 

Shan X, Xu W, Lee Y-K, et al. (2018). Evaluation of thermal environment by coupling CFD analysis and wireless-sensor measurements of a full-scale room with cooling system. Sustainable Cities and Society, 45: 395–405.
Crossref Google Scholar
 

Shan X, Luo N, Sun K, et al. (2020). Coupling CFD and building energy modelling to optimize the operation of a large open office space for occupant comfort. Sustainable Cities and Society, 60: 102257.
Crossref Google Scholar
 

Stewart J, Ren Z (2006). COwZ—A subzonal indoor airflow, temperature and contaminant dispersion model. Building and Environment, 41: 1631–1648.
Crossref Google Scholar
 

Teshome EJ, Haghighat FF (2004). Zonal models for indoor air flow— A critical review. International Journal of Ventilation, 3: 119–129.
Crossref Google Scholar
 

Verbeke J, Cools R (1995). The Newton-raphson method. International Journal of Mathematical Education in Science and Technology, 26: 177–193.
Crossref Google Scholar
 

Voeltzel A, Carrié FR, Guarracino G (2001). Thermal and ventilation modelling of large highly-glazed spaces. Energy and Buildings, 33: 121–132.
Crossref Google Scholar
 

Wang L, Wong NH (2009). Coupled simulations for naturally ventilated rooms between building simulation (BS) and computational fluid dynamics (CFD) for better prediction of indoor thermal environment. Building and Environment, 44: 95–112.
Crossref Google Scholar
 

Wang Y, Chen Y, Li C (2019). Airflow modeling based on zonal method for natural ventilated double skin façade with Venetian blinds. Energy and Buildings, 191: 211–223.
Crossref Google Scholar
 

Yamamoto T, Ozaki A, Lee M (2019). Development of a thermal environment analysis method for a dwelling containing a colonnade space through coupled energy simulation and computational fluid dynamics. Energies, 12: 2560.
Crossref Google Scholar
 

Yang L, Chen H, Jun L, et al. (2017). Study on the distribution of vertical temperature in a large space building using low-sidewall air supply system based on block-gebhart model. Procedia Engineering, 205: 2631–2638.
Crossref Google Scholar
 

Yu Y, Megri AC, Jiang S (2019). A review of the development of airflow models used in building load calculation and energy simulation. Building Simulation, 12: 347–363.
Crossref Google Scholar
 

Zhai ZJ, Chen QY (2006). Sensitivity Analysis and Application Guides for Integrated Building Energy and CFD Simulation. Energy and Buildings, 38: 1060–1068.
Crossref Google Scholar
Building Simulation
Volume 15 Issue 1,
January 2022
Pages 99-115
DOI: 10.1007/s12273-021-0781-z
Cite this article:
Laghmich N, Romani Z, Lapisa R, et al. Numerical analysis of horizontal temperature distribution in large buildings by thermo-aeraulic zonal approach. Building Simulation, 2022, 15(1): 99-115. https://doi.org/10.1007/s12273-021-0781-z

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Received: 20 July 2020
Revised: 16 January 2021
Accepted: 09 February 2021
Published: 04 May 2021
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021
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