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

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 NavierStokes (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 predictions of non‐isothermal ventilation flow—How can the user factor be minimized?

“…Only 5% of the submissions were based on first‐order discretization schemes. The use of second‐ or higher‐order discretization schemes has been recommended by—among others—Casey and Wintergerste, Chen and Srebric, Nielsen, Nielsen et al, and van Hooff and Blocken . The participants used both coupled and segregated solvers.…”

“…The use of secondor higher-order discretization schemes has been recommended by-among others-Casey and Wintergerste, 18 Chen and Srebric, 19 Nielsen, 21 Nielsen et al, 22 and van Hooff and Blocken. 34 The participants used both coupled and segregated solvers. In the segregated solvers, algorithms such as SIMPLE, SIMPLEC, SIMPLEST, and the PISO scheme were used for pressure-velocity coupling.…”

Mixing ventilation driven by two oppositely located supply jets with a time-periodic supply velocity: A numerical analysis using computational fluid dynamics

“…Numerous examples of previous studies on indoor airflows using CFD can be found in literature. [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] The largest disadvantage of CFD is the need for solution verification and validation [26][27][28][29][30][31] and the large sensitivity of results to the large amount of choices a user needs to make when performing CFD simulations. 32,33 In this paper, unsteady Reynolds-averaged Navier-Stokes (URANS) CFD simulations using the renormalization group (RNG) k-e turbulence model and a large-eddy simulation (LES) using the dynamic Smagorinksy subgrid-scale model were performed for a mixing ventilation case with time-periodic supply velocities, and the results were compared in terms of dimensionless time-averaged velocities and contaminant levels inside the enclosure.…”

“…9 Finally, a recent review paper 10 on advanced air distribution methods devoted one section to IAJSs, which stated that stagnation zones and draught issues could be reduced by time-periodic supply flows. Although all aforementioned publications indicated the possible positive effects of time-periodic (or intermittent) ventilation with respect to mixing, ventilation efficiency and thermal comfort, the vast majority of research papers on mixing ventilation flows focused on constant supply velocities 11–22 and a systematic study on the potential of time-periodic supply velocities for different mixing ventilation cases is currently lacking.…”

“…Numerous examples of previous studies on indoor airflows using CFD can be found in literature. 11–25 The largest disadvantage of CFD is the need for solution verification and validation 26–31 and the large sensitivity of results to the large amount of choices a user needs to make when performing CFD simulations. 32,33…”