Building Simulation

, Volume 10, Issue 4, pp 589–606 | Cite as

Low-Reynolds number mixing ventilation flows: Impact of physical and numerical diffusion on flow and dispersion

  • Twan van Hooff
  • Bert Blocken
Open Access
Research Article Indoor/Outdoor Airflow and Air Quality


Quality assurance in computational fluid dynamics (CFD) is essential for an accurate and reliable assessment of complex indoor airflow. Two important aspects are the limitation of numerical diffusion and the appropriate choice of inlet conditions to ensure the correct amount of physical diffusion. This paper presents an assessment of the impact of both numerical and physical diffusion on the predicted flow patterns and contaminant distribution in steady Reynolds-averaged Navier–Stokes (RANS) CFD simulations of mixing ventilation at a low slot Reynolds number (Re≈2,500). The simulations are performed on five different grids and with three different spatial discretization schemes; i.e. first-order upwind (FOU), second-order upwind (SOU) and QUICK. The impact of physical diffusion is assessed by varying the inlet turbulence intensity (TI) that is often less known in practice. The analysis shows that: (1) excessive numerical and physical diffusion leads to erroneous results in terms of delayed detachment of the wall jet and locally decreased velocity gradients; (2) excessive numerical diffusion by FOU schemes leads to deviations (up to 100%) in mean velocity and concentration, even on very high-resolution grids; (3) difference between SOU and FOU on the coarsest grid is larger than difference between SOU on coarsest grid and SOU on 22 times finer grid; (4) imposing TI values from 1% to 100% at the inlet results in very different flow patterns (enhanced or delayed detachment of wall jet) and different contaminant concentrations (deviations up to 40%); (5) impact of physical diffusion on contaminant transport can markedly differ from that of numerical diffusion.


computational fluid dynamics (CFD) numerical and physical diffusion mixing ventilation contaminant dispersion artificial diffusion 



Twan van Hooff is a postdoctoral fellow of the Research Foundation—Flanders (FWO) (project FWO 1.2.R97.15N), its financial support is gratefully acknowledged. The authors also gratefully acknowledge the academic partnership with ANSYS CFD.


  1. Abdilghanie AM, Collins LR, Caughey DA (2009). Comparison of turbulence modeling strategies for indoor flows. Journal of Fluids Engineering, 131(5): 051402.CrossRefGoogle Scholar
  2. Ai ZT, Mak CM (2014a). Determination of single-sided ventilation rates in multistory buildings: Evaluation of methods. Energy and Buildings, 69: 292–300.CrossRefGoogle Scholar
  3. Ai ZT, Mak CM (2014b). Modeling of coupled urban wind flow and indoor air flow on a high-density near-wall mesh: Sensitivity analyses and case study for single-sided ventilation. Environmental Modelling & Software, 60: 57–68.CrossRefGoogle Scholar
  4. Anderson J (1995). Computational Fluid Dynamics: The Basics with Applications. New York: McGraw-Hill.Google Scholar
  5. ANSYS (2009). Fluent 12 User’s Guide. Lebanon, NH, USA: Fluent Inc.Google Scholar
  6. Awbi HB (2003). Ventilation of Buildings. London: Spon Press.Google Scholar
  7. Blocken B (2014). 50 years of computational wind engineering: Past, present and future. Journal of Wind Engineering and Industrial Aerodynamics, 129: 69–102.CrossRefGoogle Scholar
  8. 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.CrossRefGoogle Scholar
  9. Cao SJ, Meyers J (2013). Influence of turbulent boundary conditions on RANS simulations of pollutant dispersion in mechanically ventilated enclosures with transitional slot Reynolds number. Building and Environment, 59: 397–407.CrossRefGoogle Scholar
  10. Casey M, Wintergerste T (2000). Best Practice Guidelines, ERCOFTAC Special Interest Group on Quality and Trust in Industrial CFD, ERCOFTAC, Triomflaan 43, B-1160, Brussels.Google Scholar
  11. Celik IB, Ghia U, Roache PJ, Freitas CJ, Coleman H, Raad PE (2008). Procedure for estimation and reporting of uncertainty due to discretization in CFD applications. Journal of Fluids Engineering, 130(7): 078001.CrossRefGoogle Scholar
  12. Chang KC, Hsieh WD, Chen CS (1995). A modified low-Reynoldsnumber turbulence model applicable to recirculating flow in pipe expansion. Journal of Fluids Engineering, 117: 417–423.CrossRefGoogle Scholar
  13. Chen C, Lin C-H, Long Z, Chen Q (2014). Predicting transient particle transport in enclosed environments with the combined CFD and Markov chain method. Indoor Air, 24: 81–92.CrossRefGoogle Scholar
  14. Chen C, Liu W, Lin C-H, Chen Q (2015). A Markov chain model for predicting transient particle transport in enclosed environments. Building and Environment, 90: 30–36.CrossRefGoogle Scholar
  15. Chen Q (1995). Comparison of different k–ε models for indoor air flow computations. Numerical Heat Transfer, Part B: Fundamentals, 28: 353–369.CrossRefGoogle Scholar
  16. Chen Q (2009). Ventilation performance prediction for buildings: A method overview and recent applications. Building and Environment, 44: 848–858.CrossRefGoogle Scholar
  17. Chen Q, Srebric J (2002). A procedure for verification, validation, and reporting of indoor environment CFD analyses. HVAC&R Research, 8: 201–216.CrossRefGoogle Scholar
  18. Chen Q, Zhai Z (2004). The use of CFD tools for indoor environmental design. In: Malkawi A, Augenbroe G (Eds.), Advanced Building Simulation, New York: Spon Press. pp. 119–140.Google Scholar
  19. Chung KC, Hsu SP (2001). Effect of ventilation pattern on room air and contaminant distribution. Building and Environment, 36: 989–998.CrossRefGoogle Scholar
  20. Ferziger JH, Peric M (1996). Computational Methods in Fluid Dynamics. New York: Springer.CrossRefzbMATHGoogle Scholar
  21. Freitas CJ (2002). The issue of numerical uncertainty. Applied Mathematical Modelling, 26: 237–248.CrossRefzbMATHGoogle Scholar
  22. Gan G, Awbi HB (1994). Numerical simulation of the indoor environment. Building and Environment, 29: 449–459.CrossRefGoogle Scholar
  23. Goethals K, Janssens A (2013). Sensitivity of the predicted convective heat transfer in a cooled room to the computational fluid dynamics simulation approach. Journal of Building Performance Simulation, 6: 420–436.CrossRefGoogle Scholar
  24. Hu C-H, Kurabuchi T, Ohba M (2005). Numerical study of crossventilation using two-equation RANS turbulence models. International Journal of Ventilation, 4: 123–132.Google Scholar
  25. Jiang Y, Chen Q (2002). Effect of fluctuating wind direction on cross natural ventilation in buildings from large eddy simulation. Building and Environment, 37: 379–386.CrossRefGoogle Scholar
  26. Jones PJ, Whittle GE (1992). Computational fluid dynamics for building air flow prediction—Current status and capabilities. Building and Environment, 27: 321–338.CrossRefGoogle Scholar
  27. Joubert P, Sandu A, Beghein C, Allard F (1996). Numerical study of the influence of inlet boundary conditions on the air movement in a ventilated enclosure. In: Proceedings of 5th International Conference on Air Distribution in Rooms (ROOMVENT), Yokohama, Japan, pp. 235–242.Google Scholar
  28. Karava P, Stathopoulos T, Athienitis AK (2011). Airflow assessment in cross-ventilated buildings with operable façade elements. Building and Environment, 46: 266–279.CrossRefGoogle Scholar
  29. Kato S, Murakami S, Mochida A, Akabayashi S, Tominaga Y (1992). Velocity–pressure field of cross ventilation with open windows analyzed by wind tunnel and numerical simulation. Journal of Wind Engineering and Industrial Aerodynamics, 44: 2575–2586.CrossRefGoogle Scholar
  30. Kurabuchi T, Ohba M, Endo T, Akamine Y, Nakayama F (2004). Local dynamic similarity model of cross-ventilation: Part 1—Theoretical framework. International Journal of Ventilation, 2: 371–382.CrossRefGoogle Scholar
  31. Leonard BP (1979). A stable and accurate convective modelling procedure based on quadratic upstream interpolation. Computer Methods in Applied Mechanics and Engineering, 19: 59–98.CrossRefzbMATHGoogle Scholar
  32. Leonard BP, Mokhtari S (1990). Beyond first-order upwinding: The ULTRA-SHARP alternative for non-oscillatory steady state simulation of convection. International Journal for Numerical Methods in Engineering, 30: 729–766.CrossRefGoogle Scholar
  33. Li Y, Nielsen PV (2011). CFD and ventilation research. Indoor Air, 21: 442–453.CrossRefGoogle Scholar
  34. Liu W, Wen J, Lin C-H, Liu J, Long Z, Chen Q (2013). Evaluation of various categories of turbulence models for predicting air distribution in an airliner cabin. Building and Environment, 65: 118–131.CrossRefGoogle Scholar
  35. Liu W, Jin M, Chen C, Chen Q (2016a). Optimization of air supply location, size, and parameters in enclosed environments using a computational fluid dynamics-based adjoint method. Journal of Building Performance Simulation, 9: 149–161.CrossRefGoogle Scholar
  36. Liu W, Jin M, Chen C, You R, Chen Q (2016b). Implementation of a fast fluid dynamics model in OpenFOAM for simulating indoor airflow. Numerical Heat Transfer, Part A: Applications, 69: 748–762.CrossRefGoogle Scholar
  37. Lo LJ, Novoselac A (2011). CFD simulation of cross-ventilation using fluctuating pressure boundary conditions. ASHRAE Transactions, 117(1): 621–628.Google Scholar
  38. Lo LJ, Novoselac A (2013). Effect of indoor buoyancy flow on winddriven cross ventilation. Building Simulation, 6: 69–79.CrossRefGoogle Scholar
  39. Nielsen PV (1990). Specification of a two-dimensional test case, Aalborg University, IEA Annex 20: Air Flow Patterns within Buildings.Google Scholar
  40. Nielsen PV (1998). The selection of turbulence models for prediction of room airflow. ASHRAE Transactions, 104(1B): 1119–1127.Google Scholar
  41. Nielsen PV (2004). Computational fluid dynamics and room air movement. Indoor Air, 14: 134–143.CrossRefGoogle Scholar
  42. Nielsen PV (2009). CFD in Ventilation Design: a New REHVA Guide Book. Aalborg, Denmark: Department of Civil Engineering, Aalborg University.Google Scholar
  43. Nielsen PV, Allard F, Awbi HB, Davidson L, Schälin A (2007). REHVA Guidebook No 10: Computational fluid dynamics in ventilation design. REHVA, Forssa, Finland.Google Scholar
  44. Peren JI, van Hooff T, Leite BCC, Blocken B (2015). CFD analysis of cross-ventilation of a generic isolated building with asymmetric opening positions: Impact of roof angle and opening location. Building and Environment, 85: 263–276.CrossRefGoogle Scholar
  45. Ramponi R, Blocken B (2012a). CFD simulation of cross-ventilation flow for different isolated building configurations: Validation with wind tunnel measurements and analysis of physical and numerical diffusion effects. Journal of Wind Engineering and Industrial Aerodynamics, 104–106: 408–418.CrossRefGoogle Scholar
  46. Ramponi R, Blocken B (2012b). CFD simulation of cross-ventilation for a generic isolated building: Impact of computational parameters. Building and Environment, 53, 34–48.CrossRefGoogle Scholar
  47. Roache PJ (1997). Quantification of uncertainty in computational fluid dynamics. Annual Review of Fluid Mechanics, 29: 123–160.MathSciNetCrossRefGoogle Scholar
  48. Rouaud O, Havet M (2005). Numerical investigation on the efficiency of transient contaminant removal from a food processing clean room using ventilation effectiveness concepts. Journal of Food Engineering, 68: 163–74.CrossRefGoogle Scholar
  49. Saïd MNA, Jouini DB, Plett EG (1993). Influence of turbulence parameters at supply inlet on room air diffusion. In: Proceedings of ASME Winter Meeting, New Orleans, Louisiana, USA, Paper 93-WA/HT-67.Google Scholar
  50. Skovgaard M, Nielsen PV (1990). Numerical Prediction of Air Distribution in Rooms with Ventilation of the mixing type using the standard k–ε model. Aalborg: Institut for Bygningsteknik, Aalborg Universitet. (Indoor Environmental Technology; No. 13, Vol. R9042)Google Scholar
  51. Sørensen DN, Nielsen PV (2003). Quality control of computational fluid dynamics in indoor environments. Indoor Air, 13: 2–17.CrossRefGoogle Scholar
  52. Tong Z, Chen Y, Malkawi A (2016a). Defining the Influence Region in neighborhood-scale CFD simulations for natural ventilation design. Applied Energy, 182: 625–633.CrossRefGoogle Scholar
  53. Tong Z, Chen Y, Malkawi A, Adamkiewicz G, Spengler JD (2016b). Quantifying the impact of traffic-related air pollution on the indoor air quality of a naturally ventilated building. Environment International, 89–90: 138–146.CrossRefGoogle Scholar
  54. van Hooff T, Blocken B (2010a). Coupled urban wind flow and indoor natural ventilation modelling on a high-resolution grid: A case study for the Amsterdam ArenA stadium. Environmental Modelling & Software, 25: 51–65.CrossRefGoogle Scholar
  55. van Hooff T, Blocken B (2010b). On the effect of wind direction and urban surroundings on natural ventilation of a large semi-enclosed stadium. Computers & Fluids, 39: 1146–55.CrossRefzbMATHGoogle Scholar
  56. van Hooff T, Blocken B, Defraeye T, Carmeliet J, van Heijst GJF (2012a). PIV measurements of a plane wall jet in a confined space at transitional slot Reynolds numbers. Experiments in Fluids, 53: 499–517.CrossRefGoogle Scholar
  57. van Hooff T, Blocken B, Defraeye T, Carmeliet J, van Heijst GJF (2012b). PIV measurements and analysis of transitional flow in a reduced-scale model: Ventilation by a free plane jet with Coanda effect. Building and Environment, 56: 301–313.CrossRefGoogle Scholar
  58. van Hooff T, Blocken B (2013). CFD evaluation of natural ventilation of indoor environments by the concentration decay method: CO2 gas dispersion from a semi-enclosed stadium. Building and Environment, 61: 1–17.CrossRefGoogle Scholar
  59. van Hooff T, Blocken B, van Heijst GJF (2013). On the suitability of steady RANS CFD for forced mixing ventilation at transitional slot Reynolds numbers. Indoor Air, 23: 236–249.CrossRefGoogle Scholar
  60. van Hooff T, Blocken B, Gousseau P, van Heijst GJF (2014). Countergradient diffusion in a slot-ventilated enclosure assessed by LES and RANS. Computers & Fluids, 96: 63–75.CrossRefGoogle Scholar
  61. Versteeg HK, Malalasekera W (2007). An Introduction to Computational Fluid Dynamics: The Finite Volume Method, 2nd edn. Harlow, UK: Pearson.Google Scholar
  62. Wang M, Chen Q (2009). Assessment of various turbulence models for transitional flows in enclosed environment. HVAC&R Research, 15: 1099–1119.CrossRefGoogle Scholar
  63. You R, Chen J, Shi Z, Liu W, Lin C-H, Wei D, Chen Q (2016). Experimental and numerical study of airflow distribution in an aircraft cabin mockup with a gasper on. Journal of Building Performance Simulation, 9: 555–566.CrossRefGoogle Scholar
  64. Zhang Z, Zhang W, Zhai Z, Chen Q (2007). Evaluation of various turbulence models in predicting airflow and turbulence in enclosed environments by CFD: part 2—Comparison with experimental data from literature. HVAC&R Research, 13: 871–886.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2017

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.KU LeuvenLeuvenBelgium
  2. 2.Building Physics and Services, Department of the Built EnvironmentEindhoven University of TechnologyEindhovenThe Netherlands

Personalised recommendations