Abstract
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.
Highlights
Computational fluid dynamics (CFD) can be a powerful tool to analyze complex indoor airflows
To the best knowledge of the authors, the impact of the chosen discretization schemes in combination with the grid resolution employed on the calculated velocities and concentrations for low-Reynolds number ventilation flows has not yet been investigated in detail
This study focused on the occurrence of numerical and physical diffusion in computational fluid dynamics (CFD) simulations of mixing ventilation driven by a transitional wall jet in a cubical enclosure
Summary
Computational fluid dynamics (CFD) can be a powerful tool to analyze complex indoor airflows. Kato et al 1992; Jiang and Chen 2002; Kurabuchi et al 2004; Hu et al 2005; van Hooff and Blocken 2010a,b; Lo and Novoselac 2011, 2013; Ramponi and Blocken 2012a,b; Ai and Mak 2014a,b; Perén et al 2015; Tong et al 2016a,b), for indoor airflow studies Chung and Hsu 2001; Rouaud and Havet 2005; van Hooff and Blocken 2013; Chen et al 2014, 2015). For the modeling of indoor airflow (ventilation flows) several similar guidelines as well as journal publications on specific topics have been published For the modeling of indoor airflow (ventilation flows) several similar guidelines as well as journal publications on specific topics have been published (e.g. Jones and Whittle 1992; Chen and Srebric 2002; Sørensen and Nielsen 2003; Nielsen 2004; Nielsen et al 2007)
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