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Numerical study and optimization of air-conditioning systems grilles used in indoor environments

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Abstract

The air flow distribution characterizing conditioned indoor environments obtained following conventional design methodologies does not always guarantee both thermal comfort and indoor air quality (IAQ) for all occupants. This occurs because this air flow distribution depends on factors such as the air supply conditions, the grilles and diffusers position/size, and the environmental conditions. Accordingly, the aim of this work is to numerically study the influence of the air supply conditions and the positioning and size of air-conditioning system grilles on the thermal comfort and IAQ in indoor environments. For this purpose, an optimization scheme involving genetic algorithms and computational fluid dynamics techniques has been initially developed. Three objective functions have been next separately optimized, predicted mean vote—predicted percentage dissatisfied (PMV-PPD), percentage of dissatisfied due to draft (PD) and air change effectiveness (ACE). The optimal indoor environment configuration based on the PPD produces the best results in terms of thermal comfort (PPD = 6.6%, PD = 18.7%) indexes. This configuration also features the second lowest energy consumption (774 W). Furthermore, the configuration based on the ACE both presents PMV (≤ − 1.3) and PD (≥ 20%) values ​​far from the acceptability criteria given by the standards, and involves the highest energy consumption (1832 W). Notice that the optimization of thermal comfort indexes implies indirectly optimizing the related systems energy demand as well. When using indexes such as PPD and PD as the optimization objective functions indeed, the total energy consumption is also reduced.

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References

  1. Costa, A., Keane, M.M., Torrens, J.I., Corry, E.: Building operation and energy performance: monitoring, analysis and optimization toolkit. Appl. Energy 101, 310–316 (2013)

    Article  Google Scholar 

  2. Yang, L., Yan, H., Lam, J.C.: Thermal comfort and building energy consumption implications–A review. Appl. Energy 115, 164–173 (2014)

    Article  Google Scholar 

  3. Sharma, S.S.: Determinants of carbon dioxide emissions: Empirical evidence from 69 countries. Appl. Energy 88, 376–382 (2011)

    Article  Google Scholar 

  4. Ho, S.H., Rosario, L., Rahman, M.M.: Three-dimensional analysis for hospital operating room thermal comfort and contaminant removal. Appl. Therm. Eng. 29, 2080–2092 (2009)

    Article  Google Scholar 

  5. Zhou, L., Haghighat, F.: Optimization of ventilation system design and operation in office environment, Part I: Methodology. Build. Environ. 44, 651–656 (2009)

    Article  Google Scholar 

  6. Wang, J., Zhang, T., Zhou, H., Wang, S.: Inverse design of aircraft cabin environment using computational fluid dynamics-based proper orthogonal decomposition method. Indoor Built Environ 27(10), 1379–1391 (2017)

    Article  Google Scholar 

  7. Huang, H., Ooka, R., Chen, H., Kato, S.: Optimum design for smoke-control system in buildings considering robustness using CFD and Genetic Algorithms. Build. Environ. 44, 2218–2227 (2009)

    Article  Google Scholar 

  8. Zhai, Z., Xue, Y., Chen, Q.: Inverse design methods for indoor ventilation systems using CFD-based multi-objective genetic algorithm. Build. Simul. 7(6), 661–669 (2014)

    Article  Google Scholar 

  9. Versteeg, H.K., Malalasekera, W.: An Introduction to Computational Fluid Dynamics. Pearson Educational Limited, London (2007)

    Google Scholar 

  10. Shih, T.-H., Liou, W.W., Shabbir, A., Yang, Z., Zhu, J.: A new k-ϵ eddy viscosity model for high reynolds number turbulent flows. Comput. Fluids 24(3), 227–238 (1995)

    Article  Google Scholar 

  11. Awbi, H., Hatton, A.: Mixed convection from heated room surfaces. Energy Build. 32, 153–166 (2000)

    Article  Google Scholar 

  12. Awbi, H., Hatton, A.: Natural convection from heated room surfaces. Energy Build. 30, 233–244 (1999)

    Article  Google Scholar 

  13. Çengel, Y.A., Boles, M.: Thermodynamics: An Engineering Approach. McGraw-Hill, Newyork (2015)

    Google Scholar 

  14. Li, K., Xue, W., Xu, C., Su, H.: Optimization of ventilation system operation in office environment using POD model reduction and genetic algorithm. Energy Build 67, 34–43 (2013)

    Article  Google Scholar 

  15. ASHRAE, ASHRAE Handbook-Fundamentals, Atlanta, ASHRAE (2013)

  16. ANSI/ASHRAE, “Standard 55, Thermal Environmental Conditions for Human Occupancy,” ANSI/ASHRAE (2010). https://www.techstreet.com/ashrae

  17. Fanger, P.: Thermal comfort: analysis and applications in environmental engineering, R.E. Krieger Pub. Co. (1982)

  18. Fanger, P.O., Melikov, A.K., Hanzawa, H., Ring, J.: Air turbulence and sensation of draught. Energy Build 12, 21–39 (1988)

    Article  Google Scholar 

  19. Fanger, P.O., Christensen, N.K.: Perception of draught in ventilated spaces. Ergonomics 29(2), 215–235 (1986)

    Article  Google Scholar 

  20. ISO 7726. “Ergonomics of the thermal environment – Instruments for measuring physical quantities,” ISO (1998)

  21. Chen, M.S.K., Fan, L.T., Hwang, C.L., Lee, E.S.: Air flow models in a confined space—a study in age distribution. Build. Sci. 4(3), 133–143 (1969)

    Article  Google Scholar 

  22. Sandberg, M.: What is ventilation efficiency? Build. Environ. 16(2), 123–135 (1981)

    Article  Google Scholar 

  23. Gan, G., Awbi, H.B.: “Numerical prediction of the age of air in ventilated rooms,” Roomvent '94 4th International conference on air distribution in rooms 15–28 (1994)

  24. Novoselac, A., Srebric, J.: Comparison of Air Exchange Efficiency and Contaminant Removal Effectiveness as IAQ Indices. Transact. Am. Soc. Heat. Refrig. Air Cond. Eng. 109(2), 339–349 (2003)

    Google Scholar 

  25. Kumar Singh, N., Premachandran, B.: Analysis of turbulent natural and mixed convection flows using the v2–f model. ASME J. Heat Transfer. (2016). https://doi.org/10.1115/1.4032639

    Article  Google Scholar 

  26. ANSYS, ANSYS Fluent User's Guide, Release 17.1 (2016). https://www.ansys.com/.

  27. Fluent, “Gambit 2.4.6” (2004)

  28. ANSYS, Inc, “ANSYS Fluent, ver. 17.1”, United States (2016). http://www.ansys.com

  29. Mitchell, M.: An Introduction to Genetic Algorithms. Londres: Massachusetts Institute of Technology (1988)

  30. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, New York (1996)

    Book  Google Scholar 

  31. Safe, M., Carballido, J., Ponzoni, I., Brignole, N.: “On stopping criteria for genetic algorithms,” advances in artificial intelligence – SBIA 2004. Lect. Notes Comput. Sci. 3171, 405–413 (2004)

  32. Mathworks, Inc, “MATLAB R2017a” (2017). http://www.mathworks.com

  33. Mathworks, Inc, “Matlab’s genetic algorithm and direct search toolbox” (2004). http://www.mathworks.com

  34. Blay, D., Mergui, S., Niculae, C.: Confined Turbulent Mixed Convection in the Presence of a Horizontal Buoyant Wall Jet. Fundam Mixed Convect ASME HTD 213, 65–72 (1992)

    Google Scholar 

  35. Walikewitz, N., Jänicke, B., Langner, M., Meier, F., Endlicher, W.: The difference between the mean radiant temperature and the air temperature within indoor environments: A case study during summer conditions. Build. Environ. 84, 151–161 (2015)

    Article  Google Scholar 

  36. Int-Hout, D.: Basics of well-mixed room air distribution. ASHRAE J. 57(7), 12–9 (2015)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the “Master’s Program in Peruvian Universities,” promoted by the Ministry of Education (MINEDU), the National Council of Science, Technology and Technological Innovation (CONCYTEC) and the National Fund for Scientific, Technological and Technological Innovation (FONDECYT).

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Correspondence to Cesar Celis.

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Angeles-Rodríguez, L., Celis, C. Numerical study and optimization of air-conditioning systems grilles used in indoor environments. Int J Energy Environ Eng 12, 787–804 (2021). https://doi.org/10.1007/s40095-021-00412-1

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