Calculating comfort indexes and applying comfort models to predict thermal sensation vote in sports centres
Predicting the indoor thermal comfort of people while doing sports might pose challenges, as the combination of high metabolic rate, increased humidity of the space due to physical exercise, and the alternate of more and less intense tasks in¿uence perception. This paper is aimed at comparing enviro...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/415501 |
| Acceso en línea: | https://hdl.handle.net/2117/415501 https://dx.doi.org/10.1155/2024/9142303 |
| Access Level: | acceso abierto |
| Palabra clave: | Environmental monitoring Athletic clubs Température--Régulation Environmental quality High-metabolic activities Indoor environmental quality Postoccupancy evaluation Sports centres Thermal comfort Seguiment ambiental Clubs esportius Temperatura--Control Qualitat ambiental Àrees temàtiques de la UPC::Energies |
| Sumario: | Predicting the indoor thermal comfort of people while doing sports might pose challenges, as the combination of high metabolic rate, increased humidity of the space due to physical exercise, and the alternate of more and less intense tasks in¿uence perception. This paper is aimed at comparing environmental data (temperature and relative humidity) and calculating comfort indexes (heat index) and two comfort models (Fanger’s predicted mean value and the adaptive thermal comfort model) with people’s perceptions of the environment. Indoor environmental data for the analysis were collected by monitoring several rooms in eight sports centres in a Mediterranean climate. The thermal sensation votes (TSVs) of the occupants were obtained through an online survey. A detailed explanation of the methodology of the monitoring, creation, and management of the survey and the tools used to analyse the data is provided. Results compare the relation between the TSV and the parameters or indexes calculated. Fanger’s predicted mean vote (PMV) model is not able to correctly predict people’s sensations, neither for low nor for high metabolic rates. Finally, the neutral temperature of the adaptive model for the studied conditions is calculated. Among the studied parameters and indices, temperature exhibits the strongest correlation with the thermal sensation of the occupants. However, occupants did not report a signi¿cant sensation regarding humidity in accordance with the objective conditions of the rooms. The heat index also did not show any signi¿cant correlation with the TSV. Nevertheless, across a wide range of conditions, including variations in metabolic activities, temperature, and relative humidity, the percentage of thermal dissatisfaction (indicated by “very hot” responses) remains consistently high. Notably, the temperature at which a peak in neutral sensation can be achieved is less than 21° for low metabolic rate activities. |
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