Open Access
| Issue |
E3S Web of Conf.
Volume 396, 2023
The 11th International Conference on Indoor Air Quality, Ventilation & Energy Conservation in Buildings (IAQVEC2023)
|
|
|---|---|---|
| Article Number | 01044 | |
| Number of page(s) | 7 | |
| Section | Indoor Environmental Quality (IEQ), Human Health, Comfort and Productivity | |
| DOI | https://doi.org/10.1051/e3sconf/202339601044 | |
| Published online | 16 June 2023 | |
- A. Stamou, I. Katsiris, Verification of a CFD model for indoor airflow and heat transfer, Building and Environment, 41, 9, 1171-1181 (2006) [Google Scholar]
- J. Srebric, V.Vukovic, G. He, X. Yang, CFD boundary conditions for contaminant dispersion, heat transfer and airflow simulations around human occupants in indoor environment, Building and Environment, 43, 4, 294-303 (2008) [CrossRef] [Google Scholar]
- Q. Zhou, R. Ooka, Neural Network for indoor airflow prediction with CFD database, Journal of Physics: Conference Series, Volume 2069, 8th International Building Physics Conference 2021 (2021) [Google Scholar]
- K. Fukami, K. Fukagawa, K. Taira, Assessment of supervised machine learning methods for fluid flows, Theoretical and Computational Fluid Dynamics, 34, 497-519 (2020) [CrossRef] [Google Scholar]
- H. Tanaka, Y. Matusoka, T. Kawakami, Y. Azegami, M. Yamamoto, K. Ohtake, T. Sone, Optimization Calculations and Machine Learning Aimed at Reduction of Winds Forces Acting on Tall Building and Mitigation of Wind Environment, International Journal of High-Rise Buildings, 8, 4, 291-302 (2019) [Google Scholar]
- K. Fukami, K. Fukagawa, K. Taira, Super-resolution reconstruction of turbulent flows with machine learning, Journal of Fluid Mechanics, 870, 10, 106-120 (2019) [CrossRef] [Google Scholar]
- T. Murata, K. Fukami, K. Fukagawa, Nonlinear mode decomposition with convolutional neural networks for fluid dynamics, Journal of Fluid Mechanics, 882, 10, A13 (2020) [CrossRef] [Google Scholar]
- P. A. Srinivasan, L. Guastoni, H. Azizpour, P. Schlatter, R. Vinuesa, Predictions of turbulent shear flows using deep neural networks, Physical Review Fluids, 4, 054603 (2019) [CrossRef] [Google Scholar]
- G. Calzolari, W. Liu, Deep learning to replace, improve, or aid CFD analysis in build environment applications: A review, Building and Environment, 206, 108315 (2021) [CrossRef] [Google Scholar]
- N. B. Erichson, L. Mathelin, Z. Yao, S. L. Brunton, M. W. Mahoney, J. N. Kutz, Shallow neural networks for fluid flow reconstruction with limited sensors, PROCEEDINGS OF THE ROYAL SOCIETY A, MATHEMATICAL, PHYSICAL AND ENGINEERING SCIENCES, 476, 2238, (2020) [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.