Abstract
This work seeks to analyze the thermal comfort of the occupants in a large building of Commerce and Services, integrating measures of assessment and energy efficiency promotion. The building is still in the construction phase and at its conclusion, will correspond to a supermarket located in the Central region of Portugal. For the evaluation of thermal comfort, Fanger’s methodology was used, where the Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD) were calculated based on a detailed analysis of the environmental variables. These are essential to obtain, namely, mean air velocity, mean radiant temperature, mean air temperature and relative humidity. The other crucial variables are the metabolic rate and the thermal clothing resistance. The simulations necessary for the thermal comfort assessment were performed in ANSYS Fluent, in order to minimize the energy consumption in the cold thermal zone of the building, the sales area with frozen and chilled food, by means of reducing the inflow of air, without compromising thermal Comfort. The final results showed that the reduction of the amount of air to be inflated did not compromise the thermal comfort of the occupants. The Computational Fluid Dynamics (CFD) methodology allowed the creation of comfort maps, albeit for a single zone due to computational limitations. According to the results, the most comfortable zone was located right below the air insufflation with the summer being a more comfortable season. In winter, the main problem detected was the cold located near the floor.
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Acknowledgements
The authors would like to express their gratitude for the support given by FCT within the R&D Units Project Scope UIDB/00319/2020 (ALGORITMI) and R&D Units Project Scope UIDP/04077/2020 (MEtRICs).
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Noversa, R., Silva, J., Rodrigues, N., Martins, L., Teixeira, J., Teixeira, S. (2020). Thermal Simulation of a Supermarket Cold Zone with Integrated Assessment of Human Thermal Comfort. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12254. Springer, Cham. https://doi.org/10.1007/978-3-030-58817-5_17
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