| Autor: |
Jaafar Younes, Minzhou Chen, Kamel Ghali, Risto Kosonen, Arsen K. Melikov, Nesreen Ghaddar |
| Přispěvatelé: |
American University of Beirut, Energy efficiency and systems, Technical University of Denmark, Department of Mechanical Engineering, Aalto-yliopisto, Aalto University |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Building and Environment. 227:109797 |
| ISSN: |
0360-1323 |
| Popis: |
Funding Information: The authors would like to acknowledge HEATCLIM (Heat and health in the changing climate, Grant Numbers. 329306, 329307) funded by the Academy of Finland within the CLIHE (Climate change and health) program. Publisher Copyright: © 2022 Elsevier Ltd Sub-optimal thermal conditions influence the health and well-being of elderly people and deteriorate their cognitive functions due to age-induced changes in thermoregulatory mechanisms. Addressing the thermal comfort needs of elderly is better aided when a robust predictive thermal sensation (TS) model exists. However, available TS models in the literature are based on physiological and subjective data collected from young subjects, and their use to assess elderly TS is inappropriate. In this work, a model for predicting elderly TS under steady and transient states was developed from published experimental data under controlled environment. The model predicts the mean TS of elderly people in terms of their average skin temperature, rate of change of skin temperature and core temperature. The model was coupled with a robust elderly bioheat model, enabling the prediction of elderly TS from environmental conditions. The TS model was further extended with a correlation that links the required physiological data for sensation prediction with few segmental skin temperatures that can be measured to enable the development of TS monitoring devices for the elderly. The model and the approach of using segmental temperatures in TS prediction were validated using different experimental measurements and subjective data than those used in the model development. Good agreement between experimental and predicted TS was achieved under varying steady and transient environments. The model predicts the elderly TS in ambient temperatures ranging from 13 °C to 43 °C and in transient settings with up to a 10 °C step rise or drop in ambient temperature. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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