Skip to main content
SpringerLink
Log in

Efficient Prediction of Indoor Airflow in Naturally Ventilated Residential Buildings Using a CFD-DNN Model Approach

  • Conference paper
  • First Online:
Proceedings of the 2023 International Conference on Green Building, Civil Engineering and Smart City (GBCESC 2023)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 328))

  • 218 Accesses

Abstract

Predicting indoor airflow in multi-storey residential buildings is crucial for energy-efficient natural ventilation systems. The indoor environment significantly affects human well-being due to extended indoor time and potential health risks. Precise and efficient airflow simulations are necessary to ensure thermal comfort and air quality. This study introduces a novel approach combining Computational Fluid Dynamics (CFD) simulations with machine learning techniques to predict indoor airflow. Specifically, we explore using a Deep Neural Network (DNN) model for accurate indoor airflow forecasting. The DNN effectively reproduces airflow patterns and temperature distributions. Integrating CFD simulations halves test scenario anticipation time, highlighting efficient indoor airflow prediction potential. Using a data-driven approach, this research demonstrates the feasibility of swiftly and accurately predicting indoor airflow in naturally ventilated residential buildings. Such models can optimize indoor air quality, thermal comfort, and energy efficiency, contributing to sustainable building design and operation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Hajdukiewicz, M., Geron, M., Keane, M.M.: Calibrated CFD simulation to evaluate thermal comfort in a highly-glazed naturally ventilated room. Build. Environ. 70, 73–89 (2013). https://doi.org/10.1016/j.buildenv.2013.08.020

    Article  Google Scholar 

  2. Jamaludin, A.A., Hussein, H., Ariffin, A.R.M., Keumala, N.: A study on different natural ventilation approaches at a residential college building with the internal courtyard arrangement. Energy and Buildings 72, 340–352 (2014). https://doi.org/10.1016/j.enbuild.2013.12.050

    Article  Google Scholar 

  3. Shaikh, P.H., Bin, N., Nor, M., Nallagownden, P.: Robust stochastic control model for energy and comfort management of buildings. Aust. J. Basic Appl. Sci. 7, 137–144 (2013)

    Google Scholar 

  4. Costa, A., Keane, M.M., Torrens, J.I., Corry, E.: Building operation and energy performance: monitoring, analysis and optimisation toolkit. Appl. Energy 101, 310–316 (2013). https://doi.org/10.1016/j.apenergy.2011.10.037

    Article  Google Scholar 

  5. Yang, R., Wang, L.: Multi-objective optimization for decision-making of energy and comfort management in building automation and control. Sustain. Cities Soc. 2(1), 1–7 (2012). https://doi.org/10.1016/j.scs.2011.09.001

    Article  Google Scholar 

  6. Chenari, B., Carrilho, J.D., Gameiro da Silva, M.: Towards sustainable, energy-efficient and healthy ventilation strategies in buildings: a review. Renew. Sustain. Energy Rev. 59, 1426–1447 (2016). https://doi.org/10.1016/j.rser.2016.01.074

    Article  Google Scholar 

  7. Chen, Y., Tong, Z., Zheng, Y., Samuelson, H., Norford, L.: Transfer learning with deep neural networks for model predictive control of HVAC and natural ventilation in smart buildings. J. Clean. Prod. 254, 119866 (2020). https://doi.org/10.1016/j.jclepro.2019.119866

    Article  Google Scholar 

  8. Chen, Z., Jiang, Y., Tong, Z., Tong, S.: Residual stress distribution design for gear surfaces based on genetic algorithm optimization. Materials 14(2), 1–17 (2021). https://doi.org/10.3390/ma14020366

    Article  Google Scholar 

  9. Park, D.Y., Chang, S.: Effects of combined central air conditioning diffusers and window-integrated ventilation system on indoor air quality and thermal comfort in an office. Sustain. Cities Soc. 61(October), 2020 (2019). https://doi.org/10.1016/j.scs.2020.102292

    Article  Google Scholar 

  10. Huang, M., Liao, Y.: Development of an indoor environment evaluation model for heating, ventilation and air-conditioning control system of office buildings in subtropical region considering indoor health and thermal comfort. Indoor Built Environ. 31(3), 807–819 (2022). https://doi.org/10.1177/1420326X211035550

    Article  Google Scholar 

  11. Tominaga, Y., Blocken, B.: Wind tunnel experiments on cross-ventilation flow of a generic building with contaminant dispersion in unsheltered and sheltered conditions. Build. Environ. 92, 452–461 (2015). https://doi.org/10.1016/j.buildenv.2015.05.026

    Article  Google Scholar 

  12. Common, M.: Uncertainty and the environment: implications for decision-making and environmental policy. Richard A. Young. Edward Elgar, Cheltenham, UK; Northampton, MA, 2001. ISBN 1 84064 626 8 (hardcover, 59.95 pounds sterling, $85.00). J. Econ. Psychol. 24(3), 422–424 (2003). https://doi.org/10.1016/S0167-4870(03)00018-7

    Article  Google Scholar 

  13. Al-qahtani, A.S.: Subjective assessment of indoor air quality in office buildings, Doctoral dissertation (1993)

    Google Scholar 

  14. Wargocki, P., et al.: Ventilation and health in non-industrial indoor environments: report from a european multidisciplinary scientific consensus meeting (EUROVEN). Indoor Air 12(2), 113–128 (2002). https://doi.org/10.1034/j.1600-0668.2002.01145.x

    Article  Google Scholar 

  15. Klepeis, N.E., et al.: The national human activity pattern survey (NHAPS): a resource for assessing exposure to environmental pollutants. J. Expo. Anal. Environ. Epidemiol. 11(3), 231–252 (2001). https://doi.org/10.1038/sj.jea.7500165

    Article  Google Scholar 

  16. Nielsen, P.V.: Fifty years of CFD for room air distribution. Build. Environ. 91, 78–90 (2015). https://doi.org/10.1016/j.buildenv.2015.02.035

    Article  Google Scholar 

  17. Liu, J., Heidarinejad, M., Gracik, S., Srebric, J.: The impact of exterior surface convective heat transfer coefficients on the building energy consumption in urban neighborhoods with different plan area densities. Energy Build 86, 449–463 (2015). https://doi.org/10.1016/j.enbuild.2014.10.062

    Article  Google Scholar 

  18. Liu, J., Heidarinejad, M., Pitchurov, G., Zhang, L., Srebric, J.: An extensive comparison of modified zero-equation, standard k-ε, and LES models in predicting urban airflow. Sustain. Cities Soc. 40(March), 28–43 (2018). https://doi.org/10.1016/j.scs.2018.03.010

    Article  Google Scholar 

  19. Liu, P.C., Te Lin, H., Chou, J.H.: Evaluation of buoyancy-driven ventilation in atrium buildings using computational fluid dynamics and reduced-scale air model. Build. Environ. 44(9), 1970–1979 (2009). https://doi.org/10.1016/j.buildenv.2009.01.013

    Article  Google Scholar 

  20. Cook, M.J., Ji, Y., Hunt, G.R.: CFD modelling of natural ventilation: combined wind and buoyancy forces. Int. J. Vent. 1(3), 169–179 (2003). https://doi.org/10.1080/14733315.2003.11683632

    Article  Google Scholar 

  21. Zhai, Z.J.: Sensitivity analysis and application guides for integrated building energy and CFD simulation 2. coupling-relevant building and environmental characteristics. Mech. Eng. 38(9), 1060–1068 (2006)

    Google Scholar 

  22. Cook, M.J., Lomas, K.J.: Buoyancy-driven displacement ventilation flows: evaluation of two eddy viscosity turbulence models for prediction. Build. Serv. Eng. Res. Technol. 19(1), 15–21 (1998). https://doi.org/10.1177/014362449801900103

    Article  Google Scholar 

  23. Yazarlou, T., Barzkar, E.: Louver and window position effect on cross-ventilation in a generic isolated building: a CFD approach. Indoor Built Environ. 31(6), 1511–1529 (2022). https://doi.org/10.1177/1420326X211061685

    Article  Google Scholar 

  24. Zhang, Z.-Y., Yin, W., Wang, T.-W., O’Donovan, A.: Effect of cross-ventilation channel in classrooms with interior corridor estimated by computational fluid dynamics. Indoor Built Environ. 31(4), 1047–1065 (2022). https://doi.org/10.1177/1420326X211054341

    Article  Google Scholar 

  25. Qi, J., Wei, C.: Performance evaluation of climate-adaptive natural ventilation design: a case study of semi-open public cultural building. Indoor Built Environ. 30(10), 1714–1724 (2020). https://doi.org/10.1177/1420326X20961495

    Article  Google Scholar 

  26. Guo, X., Li, W., Iorio, F.: Convolutional neural networks for steady flow approximation. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 13–17-August-2016, pp. 481–490 (2016). https://doi.org/10.1145/2939672.2939738

  27. Umetani, N., Bickel, B.: Learning three-dimensional flow for interactive aerodynamic design. ACM Trans. Graph. 37(4), 1–10 (2018). https://doi.org/10.1145/3197517.3201325

    Article  Google Scholar 

  28. Goodfellow, I., Bengio, Y., Courville, A.: Deep learning book pdf. Nature 29(7553), 1–73 (2016)

    Google Scholar 

  29. Huang, C.-J., Kuo, P.-H.: A short-term wind speed forecasting model by using artificial neural networks with stochastic optimization for renewable energy systems. Energies 11(10), 2777 (2018). https://doi.org/10.3390/en11102777

    Article  Google Scholar 

  30. Zhou, J., Liu, H., Yanhe, X., Jiang, W.: A hybrid framework for short term multi-step wind speed forecasting based on variational model decomposition and convolutional neural network. Energies 11(9), 2292 (2018). https://doi.org/10.3390/en11092292

    Article  Google Scholar 

  31. Harbola, S., Coors, V.: One dimensional convolutional neural network architectures for wind prediction. Energy Convers. Manag. 195(February), 70–75 (2019). https://doi.org/10.1016/j.enconman.2019.05.007

    Article  Google Scholar 

  32. Berger, M.J., Aftosmis, M.J., Marshall, D.D., Murman, S.M.: Performance of a new CFD flow solver using a hybrid programming paradigm. J Parallel Distrib Comput 65(4), 414–423 (2005). https://doi.org/10.1016/j.jpdc.2004.11.010

    Article  Google Scholar 

  33. Spalart, P.R., Garbaruk, A.V.: Correction to: a new “λ2” term for the spalart–allmaras turbulence model, active in axisymmetric flows. Flow Turbul. Combust. 109(1), 253–253 (2021). https://doi.org/10.1007/s10494-021-00313-7

    Article  Google Scholar 

  34. Hintea, D., Brusey, J., Gaura, E.: A study on several machine learning methods for estimating cabin occupant equivalent temperature. In: ICINCO 2015 - 12th International Conference on Informatics in Control, Automation and Robotics, Proceedings, vol. 1, pp. 629–634 (2015). https://doi.org/10.5220/0005573606290634

  35. R. M. & Associates: Rhinoceros. NURBS modeling for windows, p. 256 (2008)

    Google Scholar 

  36. Mu, D., Gao, N., Zhu, T.: Wind tunnel tests of inter-flat pollutant transmission characteristics in a rectangular multi-storey residential building, part A: effect of wind direction. Build. Environ. 108, 159–170 (2016). https://doi.org/10.1016/j.buildenv.2016.08.032

    Article  Google Scholar 

  37. Jiang, Y., Chen, Q.: Study of natural ventilation in buildings by large eddy simulation. J. Wind Eng. Ind. Aerodyn.Aerodyn. 89(13), 1155–1178 (2001). https://doi.org/10.1016/S0167-6105(01)00106-4

    Article  Google Scholar 

Download references

Acknowledgements

This research has been supported with a Presidential Scholarship from Kyung Hee University, Republic of Korea.

The author would like to express our sincere appreciation to The Human Behaviour and Energy Laboratory (HuBEL) at the Kyung Hee University, the Republic of Korea, supported facilitates research.

Author information

Authors and Affiliations

  1. Department of Architectural Engineering, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 17104, Republic of Korea

    Tran Van Quang

  2. Faculty of Environment, Tan Binh District, Ho Chi Minh City University of Natural Resources and Environment, 236B, Le Van Sy Street, Ward 1, Ho Chi Minh, Vietnam

    Nguyen Lu Phuong

  3. Department of Built Environment Engineering, School of Future Environments, Auckland University of Technology, Auckland CBD, 55 Wellesley Street East, Auckland, 1010, New Zealand

    Dat Tien Doan

Authors
  1. Tran Van Quang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nguyen Lu Phuong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dat Tien Doan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The author contributed equally to the preparation of this manuscript.

Corresponding author

Correspondence to Dat Tien Doan .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Wei Guo

  2. Guilin University of Technology, Guilin, China

    Kai Qian

  3. Guizhou University, Guiyang, Guizhou, China

    Honggang Tang

  4. Guizhou University, Guiyang, Guizhou, China

    Lei Gong

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Van Quang, T., Phuong, N.L., Doan, D.T. (2024). Efficient Prediction of Indoor Airflow in Naturally Ventilated Residential Buildings Using a CFD-DNN Model Approach. In: Guo, W., Qian, K., Tang, H., Gong, L. (eds) Proceedings of the 2023 International Conference on Green Building, Civil Engineering and Smart City. GBCESC 2023. Lecture Notes in Civil Engineering, vol 328. Springer, Singapore. https://doi.org/10.1007/978-981-99-9947-7_76

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-9947-7_76

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9946-0

  • Online ISBN: 978-981-99-9947-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation