Open Access
Issue
E3S Web Conf.
Volume 689, 2026
14th International Symposium on Heating, Ventilation, and Air Conditioning (ISHVAC 2025)
Article Number 05007
Number of page(s) 8
Section Indoor Air Quality and Ventilation
DOI https://doi.org/10.1051/e3sconf/202668905007
Published online 21 January 2026
  1. P. A. Mirzaei, “An Overview of Heat and Mass Transport in Buildings,” in Computational Fluid Dynamics and Energy Modelling in Buildings, 2022, pp. 1–23. [Google Scholar]
  2. K. De Jonge and J. and Laverge, “Modeling dynamic behavior of volatile organic compounds in a zero energy building,” International Journal of Ventilation, vol. 20, no. 3-4, pp. 193–203, 2021/12/08 2021, doi: https://doi.org/10.1080/14733315.2020.17770 12. [Google Scholar]
  3. M. Saad, M. A. William, A. A. Hassan, and A. A. Hanafy, “Influence of air ceiling diffusers in enclosed spaces: An experimental and numerical investigation,” Energy Reports, vol. 9, pp. 59–71, 2023/09/01/ 2023, doi: https://doi.org/10.1016/j.egyr.2023.05.253. [Google Scholar]
  4. M. A. Aziz, I. A. M. Gad, E. S. F. A. Mohammed, and R. H. Mohammed, “Experimental and numerical study of influence of air ceiling diffusers on room air flow characteristics,” Energy and Buildings, vol. 55, pp. 738–746, 2012/12/01/ 2012, doi: https://doi.org/10.1016/j.enbuild.2012.09.027. [Google Scholar]
  5. R. Zhang and P. A. Mirzaei, “Fast and dynamic urban neighbourhood energy simulation using CFDf-CFDc-BES coupling method,” Sustainable Cities and Society, vol. 66, p. 102545, 2021/03/01/ 2021, doi: https://doi.org/10.1016/j.scs.2020.102545. [Google Scholar]
  6. Y. Tominaga, L. Wang, Z. Zhai, and T. Stathopoulos, “Accuracy of CFD simulations in urban aerodynamics and microclimate: Progress and challenges,” Building and Environment, vol. 243, p. 110723, 2023/09/01/ 2023, doi: https://doi.org/10.1016/j.buildenv.2023.110723. [Google Scholar]
  7. G. Calzolari and W. Liu, “Deep learning to replace, improve, or aid CFD analysis in built environment applications: A review,” Building and Environment, vol. 206, p. 108315, 2021/12/01/ 2021, doi: https://doi.org/10.1016/j.buildenv.2021.108315. [Google Scholar]
  8. J. Ren and S.-J. Cao, “Development of self- adaptive low-dimension ventilation models using OpenFOAM: Towards the application of AI based on CFD data,” Building and Environment, vol. 171, 3 2020, doi: https://doi.org/10.1016/j.buildenv.2020.10667 1. [Google Scholar]
  9. W. Zuo and Q. Chen, “Real-time or faster-than- real-time simulation of airflow in buildings,” Indoor Air, vol. 19, no. 1, pp. 33–44, 2009/02/01 2009, doi: https://doi.org/10.1111/j.1600-0668.2008.00559.x. [Google Scholar]
  10. S. H. Roodkoly, Z. Q. Fard, M. Tahsildoost, Z. Zomorodian, and M. Karami, “Development of a simulation-based ANN framework for predicting energy consumption metrics: a case study of an office building,” Energy Efficiency, vol. 17, no. 1, p. 5, 2024/01/17 2024, doi: https://doi.org/10.1007/s12053-024-10185-1. [Google Scholar]
  11. N. Forouzandeh, Z. Z. Sadat, S. Zohreh, and M. and Tahsildoost, “Room energy demand and thermal comfort predictions in early stages of design based on the Machine Learning methods,” Intelligent Buildings International, vol. 15, no. 1, pp. 3–20, 2023/01/02 2023, doi: https://doi.org/10.1080/17508975.2022.20491 90. [Google Scholar]
  12. V. Equere, P. A. Mirzaei, S. Riffat, and Y. Wang, “Integration of topological aspect of city terrains to predict the spatial distribution of urban heat island using GIS and ANN,” Sustainable Cities and Society, vol. 69, p. 102825, 2021/06/01/ 2021, doi: https://doi.org/10.1016/j.scs.2021.102825. [Google Scholar]
  13. S.-J. Cao and C. Ren, “Ventilation control strategy using low-dimensional linear ventilation models and artificial neural network,” Building and Environment, vol. 144, pp. 316–333, 10 2018, doi: https://doi.org/10.1016/j.buildenv.2018.08.03 2. [Google Scholar]
  14. P. A. Mirzaei, M. Moshfeghi, H. Motamedi, Y. Sheikhnejad, and H. Bordbar, “A simplified tempo-spatial model to predict airborne pathogen release risk in enclosed spaces: An Eulerian-Lagrangian CFD approach,” Building and Environment, vol. 207, p. 108428, 2022/01/01/ 2022, doi: https://doi.org/10.1016/j.buildenv.2021.10842 8. [Google Scholar]
  15. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. [Google Scholar]
  16. N. K. Kim, D. H. Kang, W. Lee, and H. W. Kang, “Airflow pattern control using artificial intelligence for effective removal of indoor airborne hazardous materials,” Building and Environment, vol. 204, 10 2021, doi: https://doi.org/10.1016/j.buildenv.2021.10814 8. [Google Scholar]
  17. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, . . . Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems, vol. 27, 2014, doi: https://dl.acm.org/doi/10.5555/2969033.2969125. [Google Scholar]
  18. C. Tan and X. Zhong, “A Rapid Wind Velocity Prediction Method in Built Environment Based on CycleGAN Model,” in The International Conference on Computational Design and Robotic Fabrication, 2022: Springer, pp. 253–262, doi: https://doi.org/10.1007/978-981-19- 8637-6_22. [Google Scholar]
  19. C. A. Faulkner, D. S. Jankowski, J. E. Castellini, W. Zuo, P. Epple, M. D. Sohn, . . . W. Saad, “Fast prediction of indoor airflow distribution inspired by synthetic image generation artificial intelligence,” Building Simulation, vol. 16, no. 7, pp. 1219–1238, 2023/07/01 2023, doi: https://doi.org/10.1007/s12273-023-0989-1. [Google Scholar]
  20. Y.-J. Kim, M. Anis, and Y. K. Yi, “Integrating Pix2Pix and computational fluid dynamics for enhanced indoor airflow prediction: A case study with wing-walls,” Journal of Building Engineering, vol. 91, p. 109517, 2024/08/15/ 2024, doi: https://doi.org/10.1016/j.jobe.2024.109517. [Google Scholar]
  21. P. Wargocki, J. A. Porras-Salazar, S. Contreras-Espinoza, and W. Bahnfleth, “The relationships between classroom air quality and children's performence in school,” Building and Environment, vol. 173, p. 106749, 2020/04/15/ 2020, doi: https://doi.org/10.1016/j.buildenv.2020.106749. [Google Scholar]
  22. K. Rizzo, M. Camilleri, D. Gatt, and C. Yousif, “Optimising Mechanical Ventilation for Indoor Air Quality and Thermal Comfort in a Mediterranean School Building,” Sustainability, vol. 16, no. 2, doi: https://doi.org/10.3390/su16020766. [Google Scholar]
  23. R.-D. López-Carreño, P. Pujadas, and F. Pardo-Bosch, “Optimizing Ventilation Systems in Barcelona Schools: An AHP-Based Assessment for Improved Indoor Air Quality and Comfort,” Applied Sciences, vol. 14, no. 23, doi: https://doi.org/10.3390/app142311138. [Google Scholar]
  24. N. Izadyar and W. Miller, “Ventilation strategies and design impacts on indoor airborne transmission: A review,” Building and Environment, vol. 218, p. 109158, 2022/06/15/ 2022, doi: https://doi.org/10.1016/j.buildenv.2022.109158. [Google Scholar]
  25. M. Á. Campano, A. Pinto, I. Acosta, and J. J. Sendra, “Validation of a Dynamic Simulation of a Classroom HVAC System by Comparison with a Real Model,” Sustainable Development and Renovation in Architecture, Urbanism and Engineering, pp. 381–392, 2017// 2017, doi: https://doi.org/10.1007/978-3-319-51442-0_31. [Google Scholar]
  26. J. Franke, A. Hellsten, K. H. Schlünzen, and B. Carissimo, “The COST 732 Best Practice Guideline for CFD simulation of flows in the urban environment: a summary,” International Journal of Environment and Pollution, vol. 44, pp. 419–427, 2011, doi: https://doi.org/10.1504/IJEP.2011.038443. [Google Scholar]
  27. S. Vinchurkar and P. W. Longest, “Evaluation of hexahedral, prismatic and hybrid mesh styles for simulating respiratory aerosol dynamics,” Computers & Fluids, vol. 37, no. 3, pp. 317–331, 2008/3// 2008, doi: https://doi.org/10.1016/j.compfluid.2007.05.0 01. [Google Scholar]
  28. R. H. Mohammed, “A simplified method for modeling of round and square ceiling diffusers,” Energy and Buildings, vol. 64, pp. 473–482, 2013/09/01/ 2013, doi: https://doi.org/10.1016/j.enbuild.2013.05.021. [Google Scholar]
  29. P. Isola, J. Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-Image Translation with Conditional Adversarial Networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 21–26 July 2017 2017, pp. 5967–5976, doi: https://doi.org/10.1109/CVPR.2017.632. [Google Scholar]
  30. U. Sara, M. Akter, and M. S. Uddin, “Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study,” Journal of Computer and Communications, vol. 07, pp. 8–18, 01/01 2019, doi: https://doi.org/10.4236/jcc.2019.73002. [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.