Skip to main content
Download PDF

A cloud-based platform to predict wind pressure coefficients on buildings

Building Simulation volume 15, pages 1507–1525 (2022)Cite this article

Abstract

Natural ventilation (NV) is a key passive strategy to design energy-efficient buildings and improve indoor air quality. Therefore, accurate modeling of the NV effects is a basic requirement to include this technique during the building design process. However, there is an important lack of wind pressure coefficients (Cp) data, essential input parameters for NV models. Besides this, there are no simple but still reliable tools to predict Cp data on buildings with arbitrary shapes and surrounding conditions, which means a significant limitation to NV modeling in real applications. For this reason, the present contribution proposes a novel cloud-based platform to predict wind pressure coefficients on buildings. The platform comprises a set of tools for performing fully unattended computational fluid dynamics (CFD) simulations of the atmospheric boundary layer and getting reliable Cp data for actual scenarios. CFD-expert decisions throughout the entire workflow are implemented to automatize the generation of the computational domain, the meshing procedure, the solution stage, and the post-processing of the results. To evaluate the performance of the platform, an exhaustive validation against wind tunnel experimental data is carried out for a wide range of case studies. These include buildings with openings, balconies, irregular floor-plans, and surrounding urban environments. The Cp results are in close agreement with experimental data, reducing 60%–77% the prediction error on the openings regarding the EnergyPlus software. The platform introduced shows being a reliable and practical Cp data source for NV modeling in real building design scenarios.

References

  • ASHRAE (2009). ASHRAE Handbook—Fundamentals. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.

    Google Scholar 

  • Barlow JB, Rae WH, Pope A (1999). Low-Speed Wind Tunnel Testing, 3rd edn. New York: John Wiley & Sons.

    Google Scholar 

  • Blocken B, Stathopoulos T, Carmeliet J, et al. (2011). Application of computational fluid dynamics in building performance simulation for the outdoor environment: an overview. Journal of Building Performance Simulation, 4: 157–184.

    Google Scholar 

  • Blocken B (2015). Computational Fluid Dynamics for urban physics: Importance, scales, possibilities, limitations and ten tips and tricks towards accurate and reliable simulations. Building and Environment, 91: 219–245.

    Google Scholar 

  • Blocken B (2018). LES over RANS in building simulation for outdoor and indoor applications: A foregone conclusion? Building Simulation, 11: 821–870.

    Google Scholar 

  • Bracht MK, Melo AP, Lamberts R (2021). A metamodel for building information modeling-building energy modeling integration in early design stage. Automation in Construction, 121: 103422.

    Google Scholar 

  • Bre F, Gimenez JM, Fachinotti VD (2018). Prediction of wind pressure coefficients on building surfaces using artificial neural networks. Energy and Buildings, 158: 1429–1441.

    Google Scholar 

  • Bre F, Gimenez JM (2021). CpSimulator examples dataset. https://doi.org/10.5281/zenodo.5796295. Accessed 22 Dec 2021.

  • Carrilho da Graça G, Linden P (2016). Ten questions about natural ventilation of non-domestic buildings. Building and Environment, 107: 263–273.

    Google Scholar 

  • Chand I, Bhargava PK, Krishak NLV (1998). Effect of balconies on ventilation inducing aeromotive force on low-rise buildings. Building and Environment, 33: 385–396.

    Google Scholar 

  • Charisi S, Waszczuk M, Thiis TK (2017). Investigation of the pressure coefficient impact on the air infiltration in buildings with respect to microclimate. Energy Procedia, 122: 637–642.

    Google Scholar 

  • Chen Y, Tong Z, Malkawi A (2017). Investigating natural ventilation potentials across the globe: Regional and climatic variations. Building and Environment, 122: 386–396.

    Google Scholar 

  • Cóstola D, Blocken B, Hensen JLM (2009). Overview of pressure coefficient data in building energy simulation and airflow network programs. Building and Environment, 44: 2027–2036.

    Google Scholar 

  • Cóstola D, Blocken B, Ohba M, et al. (2010). Uncertainty in airflow rate calculations due to the use of surface-averaged pressure coefficients. Energy and Buildings, 42: 881–888.

    Google Scholar 

  • Crawley DB, Lawrie LK, Winkelmann FC, et al. (2001). EnergyPlus: creating a new-generation building energy simulation program. Energy and Buildings, 33: 319–331.

    Google Scholar 

  • Ding C, Lam KP (2019). Data-driven model for cross ventilation potential in high-density cities based on coupled CFD simulation and machine learning. Building and Environment, 165: 106394.

    Google Scholar 

  • DOE (2021). ANSI/ASHRAE/IES Standard 90.1. Prototype Building Models, Secondary School. U.S. Department of Energy. Available at https://www.energycodes.gov/prototype-building-models#Commercial. Accessed 20 Nov 2021.

  • Fabritius B, Tabor G (2016). Improving the quality of finite volume meshes through genetic optimisation. Engineering With Computers, 32: 425–440.

    Google Scholar 

  • Ferziger JH, Peric M (2002). Computational Methods for Fluid Dynamics. New York: Springer Science & Business Media.

    MATH  Google Scholar 

  • Feustel HE (1999). COMIS—An international multizone air-flow and contaminant transport model. Energy and Buildings, 30: 3–18.

    Google Scholar 

  • Franke J, Hirsch C, Jensen AG, et al. (2004), Recommendations on the use of CFD in predicting pedestrian wind environment. Cost Action C14.

  • Franke J, Hellsten A, Schlünzen KH, et al. (2007). Best Practice Guideline for the CFD simulation of flows in the urban environment. COST Action 732. Brussels: COST Office.

  • Gimenez JM, Bre F, Nigro NM, Fachinotti V (2018). Computational modeling of natural ventilation in low-rise non-rectangular floor-plan buildings. Building Simulation, 11: 1255–1271.

    Google Scholar 

  • Gimenez JM, Bre F (2019). Optimization of RANS turbulence models using genetic algorithms to improve the prediction of wind pressure coefficients on low-rise buildings. Journal of Wind Engineering and Industrial Aerodynamics, 193: 103978.

    Google Scholar 

  • Grosso M (1992). Wind pressure distribution around buildings: A parametrical model. Energy and Buildings, 18: 101–131.

    Google Scholar 

  • Gu L (2007), Airflow network modeling in EnergyPlus. In: Proceedings of the 10th International IBPSA Building Simulation Conference, Beijing, China.

  • Hargreaves DM, Wright NG (2007). On the use of the k—ε model in commercial CFD software to model the neutral atmospheric boundary layer. Journal of Wind Engineering and Industrial Aerodynamics, 95: 355–369.

    Google Scholar 

  • Idelsohn S, Nigro N, Larreteguy A, et al. (2020). A pseudo-DNS method for the simulation of incompressible fluid flows with instabilities at different scales. Computational Particle Mechanics, 7: 19–40.

    Google Scholar 

  • Idelsohn SR, Gimenez JM, Nigro NM, et al. (2021). The Pseudo-Direct Numerical Simulation method for multi-scale problems in mechanics. Computer Methods in Applied Mechanics and Engineering, 380: 113774.

    MathSciNet  MATH  Google Scholar 

  • IEA (2016). Key World Energy Statistics. Paris. International Energy Agency.

    Google Scholar 

  • Jones W, Launder B (1972). The prediction of laminarization with a two-equation model of turbulence. International Journal of Heat and Mass Transfer, 15: 301–314.

    Google Scholar 

  • Jung W, Jazizadeh F (2019). Human-in-the-loop HVAC operations: a quantitative review on occupancy, comfort, and energy-efficiency dimensions. Applied Energy, 239: 1471–1508.

    Google Scholar 

  • Kastner P, Dogan T (2020). A cylindrical meshing methodology for annual urban computational fluid dynamics simulations. Journal of Building Performance Simulation, 13: 59–68.

    Google Scholar 

  • Kato M, Launder BE (1993). The modeling of turbulent flow around stationary and vibrating square cylinders. In: Proceedings of the 9th symposium on Turbulent Shear Flows, Kyoto, Japan.

  • Knoll B, Phaff JC, de Gids WF (1997). Pressure simulation program. In: Updated of proceedings of the 16th AIVC Conference: Implementing the Results of Ventilation Research.

  • Liu S, Liu J, Yang Q, et al. (2014). Coupled simulation of natural ventilation and daylighting for a residential community design. Energy and Buildings, 68: 686–695.

    Google Scholar 

  • Liu F (2016). A thorough description of how wall functions are implemented in OpenFOAM. In: Proceedings of CFD with OpenSource Software.

  • Medvecká S, Ivánková O, MacÁk M, et al. (2018). Determination of pressure coefficient for a high-rise building with atypical ground plan. Civil and Environmental Engineering, 14: 138–145.

    Google Scholar 

  • Menter FR (1994). Two-equation eddy-viscosity turbulence models for engineering applications. AIAA Journal, 32: 1598–1605.

    Google Scholar 

  • Montazeri H, Blocken B (2013). CFD simulation of wind-induced pressure coefficients on buildings with and without balconies: Validation and sensitivity analysis. Building and Environment, 60: 137–149.

    Google Scholar 

  • Muehleisen RT, Patrizi S (2013). A new parametric equation for the wind pressure coefficient for low-rise buildings. Energy and Buildings, 57: 245–249.

    Google Scholar 

  • Ntinas GK, Shen X, Wang Y, et al. (2018). Evaluation of CFD turbulence models for simulating external airflow around varied building roof with wind tunnel experiment. Building Simulation, 11: 115–123.

    Google Scholar 

  • OpenFOAM (2021). OpenFOAM. the OpenFOAM foundation. Available at https://openfoam.org/. Accessed 29 Nov 2021.

  • Orme M, Leksmono N (2002). AIVC Guide 5: Ventilation modelling data guide. International Energy Agency, Air Infiltration Ventilation Center.

  • Richards PJ, Hoxey RP (1993). Appropriate boundary conditions for computational wind engineering models using the k—ε turbulence model. In: Murakami S (Ed), Computational Wind Engineering 1. Oxford, UK: Elsevier.

    Google Scholar 

  • Sakiyama NRM, Carlo JC, Frick J, et al. (2020). Perspectives of naturally ventilated buildings: A review. Renewable and Sustainable Energy Reviews, 130: 109933.

    Google Scholar 

  • Seshat (2020). Seshat cluster. Available at http://www.cimec.org.ar/c3/seshat/. Accessed 27 Nov 2021.

  • Shih TH, Liou WW, Shabbir A, et al. (1995). A new k—ε eddy viscosity model for high Reynolds number turbulent flows. Computers & Fluids, 24: 227–238.

    MATH  Google Scholar 

  • Sorgato MJ, Melo AP, Lamberts R (2016). The effect of window opening ventilation control on residential building energy consumption. Energy and Buildings, 133: 1–13.

    Google Scholar 

  • Spalart P, Allmaras S (1992). A one-equation turbulence model for aerodynamic flows. In: Proceedings of 30th Aerospace Sciences Meeting and Exhibit, Reno, NV, USA.

  • Swami MV, Chandra S (1988). Correlations for pressure distribution on buildings and calculation of natural-ventilation airflow. ASHRAE Transactions, 94(1), 243–266.

    Google Scholar 

  • Tamura Y, Ohkuma T, Kawai H, et al. (2004). Revision of AIJ recommendations for wind loads on buildings. In: Proceedings of Structures Congress 2004: Building on the Past, Securing the Future, Nashville, TN, USA.

  • Tominaga Y, Mochida A, Yoshie R, et al. (2008). AIJ guidelines for practical applications of CFD to pedestrian wind environment around buildings. Journal of Wind Engineering and Industrial Aerodynamics, 96: 1749–1761.

    Google Scholar 

  • Tominaga Y (2015). Flow around a high-rise building using steady and unsteady RANS CFD: Effect of large-scale fluctuations on the velocity statistics. Journal of Wind Engineering and Industrial Aerodynamics, 142: 93–103.

    Google Scholar 

  • Tominaga Y, Akabayashi SI, Kitahara T, et al. (2015). Air flow around isolated gable-roof buildings with different roof pitches: Wind tunnel experiments and CFD simulations. Building and Environment, 84: 204–213.

    Google Scholar 

  • Tong Z, Chen Y, Malkawi A, et al. (2016). Energy saving potential of natural ventilation in China: The impact of ambient air pollution. Applied Energy, 179: 660–668.

    Google Scholar 

  • Tong Z, Chen Y, Malkawi A (2017). Estimating natural ventilation potential for high-rise buildings considering boundary layer meteorology. Applied Energy, 193: 276–286.

    Google Scholar 

  • Toparlar Y, Blocken B, Maiheu B, et al. (2017). A review on the CFD analysis of urban microclimate. Renewable and Sustainable Energy Reviews, 80: 1613–1640.

    Google Scholar 

  • TUP (2021). TPU Aerodynamic Database. Global Center of Excellence Program, Tokyo Polytechnic University, Tokyo, Japan. Available at http://wind.arch.t-kougei.ac.jp/system/eng/contents/code/tpu. Accessed: 27 Oct 2021.

  • Tsuchiya M, Murakami S, Mochida A, et al. (1997). Development of a new k—ε model for flow and pressure fields around bluff body. Journal of Wind Engineering and Industrial Aerodynamics, 67–68: 169–182.

    Google Scholar 

  • Wieringa J (1992). Updating the Davenport roughness classification. Journal of Wind Engineering and Industrial Aerodynamics, 41: 357–368.

    Google Scholar 

  • Yakhot V, Orszag SA, Thangam S, et al. (1992). Development of turbulence models for shear flows by a double expansion technique. Physics of Fluids A: Fluid Dynamics, 4: 1510–1520.

    MathSciNet  MATH  Google Scholar 

  • Zhai Z (2014). Computational fluid dynamics applications in green building design. In: Al-Baghdadi MARA (Ed), Computational Fluid Dynamics Applications in Green Design. International Energy and Environment Foundation (IEEF). pp. 1–22.

  • Zhai Z, Mankibi ME, Zoubir A (2015). Review of natural ventilation models. Energy Procedia, 78: 2700–2705.

    Google Scholar 

  • Zhang X, Weerasuriya AU, Lu B, et al. (2020). Pedestrian-level wind environment near a super-tall building with unconventional configurations in a regular urban area. Building Simulation, 13: 439–456.

    Google Scholar 

  • Zheng X, Montazeri H, Blocken B (2020). CFD simulations of wind flow and mean surface pressure for buildings with balconies: Comparison of RANS and LES. Building and Environment, 173: 106747.

    Google Scholar 

Download references

Acknowledgements

For funding this work, we would like to thank the Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación (Agencia I+D+i), Argentina, via the projects PICT-2018 No03252 and PICT-2018 No02464, Res. No401-19.

Author information

Authors and Affiliations

  1. Centro de Investigación de Métodos Computacionales (CIMEC), UNL, CONICET, Predio “Dr. Alberto Cassano”, Colectora Ruta Nacional 168 s/n, 3000, Santa Fe, Argentina

    Facundo Bre & Juan M. Gimenez

Authors
  1. Facundo Bre
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juan M. Gimenez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Facundo Bre.

Additional information

Data availability

For a closer analysis or its reproduction, the results and the input geometry data used to generate them can be found at https://doi.org/10.5281/zenodo.5796295.

Electronic Supplementary Material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bre, F., Gimenez, J.M. A cloud-based platform to predict wind pressure coefficients on buildings. Build. Simul. 15, 1507–1525 (2022). https://doi.org/10.1007/s12273-021-0881-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12273-021-0881-9

Keywords

  • natural ventilation
  • building simulation
  • airflow network model
  • EnergyPlus
  • wind pressure coefficient
  • computational fluid dynamics