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Thermal modeling of an office environment with variable volume air condition system using zonal method for control system applications

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Abstract

Appropriate thermal modeling is the first step in controlling thermal comfort and reducing energy consumption. In this article, the thermal modeling of an office room with a variable air volume (VAV) system is considered. Various thermal modeling methods are reviewed, and finally, the zonal method is selected. The main advantage of the selected method is its simple formulation but still accurate results. Furthermore, the output equations are in the state-space forms which are suitable for control applications. The thermal model is obtained by considering the airflow patterns in the office room. One of the key features of the obtained model is its applicability to winter as well as summer conditions. DesignBuilder software is also used to validate the thermal model. Results indicate the accuracy of the obtained thermal model in all weather conditions.

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Abbreviations

A :

Surface area (m2)

\({A}_{r}\) :

Archimedes number

C BL :

Boundary layer constant

C p :

Specific heat capacity (J/(kg・K))

h:

Heat transfer coefficient (W/k)

i, j, k :

Subscript refer to ith, jth, and kth zone

\(\overset'k\) :

Jet constant

\(\dot{m}\) :

Mass flow rate (kg/s)

n jet :

Exponent for jet

n plume :

Exponent for boundary layer

n plume :

Exponent for plume

Q Human :

Heat generated by humans (w)

Q lighting :

Heat generated by lighting (w)

Q source :

Generated heat (w)

\({T}_{c}\) :

The coil temperature

\({T}_{i}\) :

The temperature in the main part

\({T}_{j}\) :

The temperature near the ceiling

\({T}_{k}\) :

The temperature near the outside wall

\({T}_{r}\) :

The outside temperature

\({v}_{0}\) :

Air velocity (m/s)

x:

Length (m)

Ø:

The heat generated by the device (W)

\(\rho\) :

Air density (kg/m3)

\({\delta }_{p}\) :

Penetration of jet (m)

References

  • Allard, F. (1987). Contribution à l'étude des transferts de chaleur dans les cavités thermiquement entraînées à grand nombre de Rayleigh Lyon.

  • Aqilah, N., Zaki, S. A., Hagishima, A., Rijal, H. B., & Yakub, F. (2021). Analysis on electricity use and indoor thermal environment for typical air-conditioning residential buildings in Malaysia. Urban Climate, 37, 100830.

    Article  Google Scholar 

  • Asim, N., Badiei, M., Mohammad, M., Razali, H., Rajabi, A., Chin Haw, L., & Jameelah Ghazali, M. (2022). Sustainability of heating, ventilation and air-conditioning (HVAC) systems in buildings—An overview. International Journal of Environmental Research and Public Health, 19(2), 1016.

    Article  Google Scholar 

  • Baniyounes, A. M., Ghadi, Y. Y., Zahia, M. M. A., Adwan, E., & Oliemat, K. (2021). Energy, economic and environmental analysis of fuzzy logic controllers used in smart buildings.

  • Borro, L., Mazzei, L., Raponi, M., Piscitelli, P., Miani, A., & Secinaro, A. (2021). The role of air conditioning in the diffusion of Sars-CoV-2 in indoor environments: A first computational fluid dynamic model, based on investigations performed at the Vatican State Children’s hospital. Environmental Research, 193, 110343.

    Article  Google Scholar 

  • Christ, P., & Sattelmayer, T. (2018). Thermal modeling of an automotive HVAC unit using a coupled POD and flow resistance network approach (0148–7191).

  • Elveny, M., Jalil, A. T., Davarpanah, A., Alfakeer, M., Bahajjaj, A. A. A., & Ouladsmane, M. (2021). CFD-based simulation to reduce greenhouse gas emissions from industrial plants. International Journal of Chemical Reactor Engineering, 19(11), 1179–1186.

    Article  Google Scholar 

  • Farj, M. A., Aftab, S. M. A., & Ahmad, K. A. (2018). Parametric study to determine optimum HVAC in a hospital operation theatre. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 44(1), 99–122.

    Google Scholar 

  • Fracastoro, G., Mutani, G., & Perino, M. (2001). Experimental and theoretical analysis of natural ventilation by means of windows opening. Clima2000/Napoli 2001 World Congress–Napoli (1).

  • Griffith, B., & Chen, Q. Y. (2004). Framework for coupling room air models to heat balance model load and energy calculations (RP-1222). Hvac&r Research, 10(2), 91–111.

    Article  Google Scholar 

  • Guo, P., Ding, C., Guo, Z., Liu, T., & Lyu, T. (2022). Coupling CFD simulation and field experiments in summer to prove Feng Shui optimizes courtyard wind environments: A case study of Prince Kung’s mansion in Beijing. Buildings, 12(5), 629.

    Article  Google Scholar 

  • Hamdaoui, M.-A., Benzaama, M.-H., El Mendili, Y., & Chateigner, D. (2021). A review on physical and data-driven modeling of buildings hygrothermal behavior: Models, approaches and simulation tools. Energy and Buildings, 251, 111343.

    Article  Google Scholar 

  • Hu, J., & Karava, P. (2014). Model predictive control strategies for buildings with mixed-mode cooling. Building and Environment, 71, 233–244.

    Article  Google Scholar 

  • Kennedy, H. E. (2021). ASHRAE handbook—Fundamentals. ASHRAE.

  • Liang, W., Huang, J., Jones, P., Wang, Q., & Hang, J. (2018). A zonal model for assessing street canyon air temperature of high-density cities. Building and Environment, 132, 160–169.

    Article  Google Scholar 

  • Megri, A. C., & Haghighat, F. (2007). Zonal modeling for simulating indoor environment of buildings: Review, recent developments, and applications. Hvac&r Research, 13(6), 887–905.

    Article  Google Scholar 

  • Megri, A. C., & Yu, Y. (2017). Study of residential underfloor air distribution (UFAD) systems using a new modelling approach. Indoor and Built Environment, 26(1), 5–20. https://doi.org/10.1177/1420326x15597544

    Article  Google Scholar 

  • Mukherjee, S., Mishra, S., & Wen, J. T. (2012). Building temperature control: A passivity-based approach. 2012 IEEE 51st IEEE conference on decision and control (CDC).

  • Ogata, K. (2010). Modern control engineering (Fifth ed.). Prentice Hall.

  • Pandey, B., Banerjee, R., & Sharma, A. (2021). Coupled EnergyPlus and CFD analysis of PCM for thermal management of buildings. Energy and Buildings, 231, 110598.

    Article  Google Scholar 

  • Pazhoohesh, M., & Zhang, C. (2018). Investigating occupancy-driven air-conditioning control based on thermal comfort level. Journal of Architectural Engineering, 24(2), 04018003.

    Article  Google Scholar 

  • Rao, D. V., & Ukil, A. (2017). Modeling of room temperature dynamics for efficient building energy management. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(2), 717–725.

    Article  Google Scholar 

  • Riederer, P., Marchio, D., Visier, J., Husaunndee, A., & Lahrech, R. (2002). Room thermal modelling adapted to the test of HVAC control systems. Building and Environment, 37(8–9), 777–790.

    Article  Google Scholar 

  • Riederer, P., Marchio, D., Visier, J., Husaunndee, A., & Lahrech, R. (2001). Influence of sensor position in building thermal control: Development and validation of an adapted zone model. BS2001 conference, Rio de Janeiro, Brazil.

  • Riederer, P. (2002). Thermal room modelling adapted to the test of HVAC control systems École Nationale Supérieure des Mines de Paris].

  • Rodriguez, J., & Fumo, N. (2021). Zoned heating, ventilation, and air–conditioning residential systems: A systematic review. Journal of Building Engineering, 43, 102925.

    Article  Google Scholar 

  • Saleem, A. A., Bady, M., Ookawara, S., & Abdel-Rahman, A. K. (2016). Achieving standard natural ventilation rate of dwellings in a hot-arid climate using solar chimney. Energy and Buildings, 133, 360–370.

    Article  Google Scholar 

  • Singh, S., & Abbassi, H. (2018). 1D/3D transient HVAC thermal modeling of an off-highway machinery cabin using CFD-ANN hybrid method. Applied Thermal Engineering, 135, 406–417.

    Article  Google Scholar 

  • Singh, J., Velu, V., Shafeek, M. A., & Nirmal, U. (2021). A review on building energy index (BEI) in Different Green Government Buildings (GGBs) in Malaysia. Malaysian Journal of Science and Advanced Technology, 46–56.

  • Su, Y.-W. (2022). The rebound effects of energy-efficient air conditioners: Buy more, bigger, or use longer? Energy Efficiency, 15(5), 1–17.

    Article  Google Scholar 

  • Wang, B., & Wang, J. (2021). Application of artificial intelligence in computational fluid dynamics. Industrial & Engineering Chemistry Research, 60(7), 2772–2790.

    Article  Google Scholar 

  • Wu, H. T., Li, Q. S., Dai, R. C., Liu, S., Wu, L., Mao, W., & Ji, C. H. (2021). Effects of air-conditioning systems in the public areas of hospitals: A scoping review. Epidemiology & Infection, 149, 1–11. https://doi.org/10.1017/S0950268821001990

  • Xiuming, L., Zongwei, H., Tianyi, Z., & Jiajia, G. (2021). Online model for indoor temperature control based on building thermal process of air conditioning system. Journal of Building Engineering, 39, 1–9.

    Google Scholar 

  • Xu, X., Gao, Z., & Zhang, M. (2023). A review of simplified numerical approaches for fast urban airflow simulation. Building and Environment, 234, 110200. https://doi.org/10.1016/j.buildenv.2023.110200

  • Zhang, K., Liu, Z., Wan, Y., Wang, B., & Ai, X. (2015). Fluid-like properties of chip flow in high-speed metal cutting process. Machining Science and Technology, 19(1), 71–85.

    Article  Google Scholar 

  • Zhao, Y., Li, N., Tao, C., Chen, Q., & Jiang, M. (2021). A comparative study on energy performance assessment for HVAC systems in high-tech fabs. Journal of Building Engineering, 39, 102188.

    Article  Google Scholar 

  • Zhou, P., Wang, S., Zhou, J., Hussain, S. A., Liu, X., Gao, J., & Huang, G. (2023). A modelling method for large-scale open spaces orientated toward coordinated control of multiple air-terminal units. Building Simulation, 16(2), 225–241.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, Mashhad Branch, Islamic Azad University, P. O. B.: 9187147578, Mashhad, Iran

    Mahdi Rastegar-Moghadam, Yadollah Farzaneh & Seyed Mohammad Yasoubi

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  1. Mahdi Rastegar-Moghadam
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  2. Yadollah Farzaneh
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  3. Seyed Mohammad Yasoubi
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Correspondence to Yadollah Farzaneh.

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Rastegar-Moghadam, M., Farzaneh, Y. & Yasoubi, S.M. Thermal modeling of an office environment with variable volume air condition system using zonal method for control system applications. Energy Efficiency 17, 28 (2024). https://doi.org/10.1007/s12053-024-10196-y

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