Low-Reynolds number mixing ventilation flows: Impact of physical and numerical diffusion on flow and dispersion
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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.
Keywordscomputational 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.
- Anderson J (1995). Computational Fluid Dynamics: The Basics with Applications. New York: McGraw-Hill.Google Scholar
- ANSYS (2009). Fluent 12 User’s Guide. Lebanon, NH, USA: Fluent Inc.Google Scholar
- Awbi HB (2003). Ventilation of Buildings. London: Spon Press.Google Scholar
- 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
- 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
- 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
- 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
- Lo LJ, Novoselac A (2011). CFD simulation of cross-ventilation using fluctuating pressure boundary conditions. ASHRAE Transactions, 117(1): 621–628.Google Scholar
- Nielsen PV (1990). Specification of a two-dimensional test case, Aalborg University, IEA Annex 20: Air Flow Patterns within Buildings.Google Scholar
- Nielsen PV (1998). The selection of turbulence models for prediction of room airflow. ASHRAE Transactions, 104(1B): 1119–1127.Google Scholar
- Nielsen PV (2009). CFD in Ventilation Design: a New REHVA Guide Book. Aalborg, Denmark: Department of Civil Engineering, Aalborg University.Google Scholar
- 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
- 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
- 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
- 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
- Versteeg HK, Malalasekera W (2007). An Introduction to Computational Fluid Dynamics: The Finite Volume Method, 2nd edn. Harlow, UK: Pearson.Google Scholar
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