Open Access
| Issue |
E3S Web of Conf.
Volume 562, 2024
BuildSim Nordic 2024
|
|
|---|---|---|
| Article Number | 09004 | |
| Number of page(s) | 11 | |
| Section | Indoor Environmental Quality (IEQ), CFD and Air Flow | |
| DOI | https://doi.org/10.1051/e3sconf/202456209004 | |
| Published online | 07 August 2024 | |
- T. W. Tsang, K. W. Mui, L. T. Wong, Computational Fluid Dynamics (CFD) studies on airborne transmission in hospitals: A review on the research approaches and the challenges. J. Build. Eng. 63, 105533 (2023) [CrossRef] [Google Scholar]
- F. Ascione, R. F. De Masi, M. Mastellone, G. P. Vanoli, The design of safe classrooms of educational buildings for facing contagions and transmission of diseases: A novel approach combining audits, calibrated energy models, building performance (BPS) and computational fluid dynamic (CFD) simulations. Energy Build. 230, 110533 (2021). https://doi.org/10.1016/j.enbuild.2020.110533 [CrossRef] [Google Scholar]
- M. A. William, M. J. Suárez-López, S. Soutullo, A. A. Hanafy, Evaluating heating, ventilation, and air-conditioning systems toward minimizing the airborne transmission risk of Mucormycosis and COVID-19 infections in built environment. Case Stud. Therm. Eng. 28, 101567 (2021). https://doi.org/10.1016/j.csite.2021.101567 [CrossRef] [Google Scholar]
- A. J. Edwards, L. Benson, Z. Guo, M. López-García, C. J. Noakes, D. Peckham, M. F. King, A mathematical model for assessing transient airborne infection risks in a multizone hospital ward. Build. Environ. 238, 110344 (2023). https://doi.org/10.1016/j.buildenv.2023.110344 [CrossRef] [Google Scholar]
- Y. Yu, A. C. Megri, S. Jiang, A review of the development of airflow models used in building load calculation and energy simulation. Build. Simul. 12, 347–363 (2019). https://doi.org/10.1007/s12273-018-0494-0 [Google Scholar]
- Y. Lu, J. Dong, J. Liu, Zonal modelling for thermal and energy performance of large space buildings: A review. Renew. Sustain. Energy Rev. 133, 110241 (2020). https://doi.org/10.1016/j.rser.2020.110241 [CrossRef] [Google Scholar]
- S. Togari, Y. Arai, K. Miura, A simplified model for predicting vertical temperature distribution in a large space. ASHARE Trans. 99(1), 84-99 (1993). [Google Scholar]
- L. De Backer, J. Laverge, A. Janssens, M. De Paepe, The use of a zonal model to calculate the stratification in a large building, in Proceedings of the 35th AIVC Conference: Ventilation and airtightness in transforming the building stock to high performance, Poznań, Poland, September 24-25 (2014). [Google Scholar]
- A. Bring, P. Sahlin, M. Vuolle, Models for Building Indoor Climate and Energy Simulation, a Report of IEA SHC Task 22: Building Energy Analysis Tools, Subtask B: Model Documentation (Available online: https://www.equa.se/dncenter/T22Brep.pdf, accessed on 5 December 2023). [Google Scholar]
- L. Georges, M. Thalfeldt, Ø. Skreiberg, W. Fornari, Validation of a transient zonal model to predict the detailed indoor thermal environment: Case of electric radiators and wood stoves. Build. Environ. 149, 169-181 (2019). https://doi.org/10.1016/j.buildenv.2018.12.020 [CrossRef] [Google Scholar]
- K. Choi, S. Park, J. Joe, S. I. Kim, J. H. Jo, E. J. Kim, Y. H. Cho, Review of infiltration and airflow models in building energy simulations for providing guidelines to building 181, 113327 (2023). [Google Scholar]
- WHO (World Health Organization), Roadmap to improve and ensure good indoor ventilation in the context of COVID-19 (2021) (Available online: https://www.who.int/publications/i/item/9789240021280, accessed on 5 December 2023). [Google Scholar]
- T. W. Tsang, K. W. Mui, L. T. Wong, Computational Fluid Dynamics (CFD) studies on airborne transmission in hospitals: A review on the research approaches and the challenges. J. Build. Eng. 63, 105533 (2023). https://doi.org/10.1016/j.jobe.2022.105533 [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.