UFR 1-07 Evaluation
Unsteady Near-Field Plumes
Underlying Flow Regime 1-07
Comparison of DesJardin et al. [1] CFD Calculations with Experiments
Figure 11 shows a snapshot of the flow field predicted by the CFD model of DesJardin et al. [1]. With the coarse grid, the plume puffing frequency was found to be approximately 1.8 Hz, much higher than the frequency measured in the experiments of 1.37 Hz. The predictions improved as the grid was refined, with the fine grid producing a frequency of 1.5 Hz. A similar frequency was obtained with or without an SGS model. DesJardin et al. [1] also presented results from a simulation with no SGS model and a very coarse mesh (220k nodes in total and only 30 cells across the source diameter). This produced a puffing frequency of 1.7 Hz, which they considered to be an adequate estimate for engineering purposes, although the axial velocity in this case was overpredicted by nearly a factor of two.
Figure 12 shows the mean axial velocity predictions at three vertical
positions within the plume. The symbols are the experimental data
points with their uncertainty shown as vertical lines. The predictions
are overall in good agreement with the experiments. All of the results
are mostly within the experimental uncertainty bounds except for the
results obtained using the coarse 512k node mesh with an SGS model. For
this case, the peak velocity is overpredicted by 27 %, 61 % and 67 %
at the three downstream positions x = 0.2 m, 0.4 m and 0.6 m.
For the coarse mesh, mean axial velocity predictions are improved when
the SGS model is not used. DesJardin et al. suggested that the
relatively poor predictions with the coarse grid and SGS model were due
to there being a net upscale transport of turbulent energy near the
plume source, from small to large scales. They noted that the purely
dissipative Smagorinsky model was unable to account for this
phenomenon. Using finer meshes, a greater proportion of turbulence
energy was resolved. Alternatively, by removing the SGS model, the
damping from the turbulence model was reduced, which improved the
predictions.
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The radial mean velocity predictions (Figure 12) show reasonable
agreement with the experiments on the periphery of the plume but all of
the simulations overpredict the radial velocity near the plume
centreline. The best results are again achieved using the finer mesh.
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RMS axial velocity profiles are shown in Figure 13. The coarse{}-grid results without the SGS model and the results on the fine grid with or without the SGS model all overpredict the RMS velocities by up to 75\%. The best agreement is obtained with the coarse{}-grid using the SGS model. DesJardin \textit{et al}. suggested that the relatively good performance for this last case is purely fortuitous and is due to excessive damping of the turbulent fluctuations. The generally poor predictions of the RMS velocity was attributed to under{}-resolution of the turbulent production and destruction near the base of the plume, resulting in an overly{}-coherent puffing motion. Radial RMS velocities (not shown) were better predicted, with fine{}-grid simulations falling within the experimental uncertainty bounds.
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Figure 13 also shows the predicted and experimental mean helium mass fractions at the three downstream positions. The best predictions were obtained using the fine mesh without the SGS model, which were within the experimental uncertainty bounds for the two positions nearest the plume source. The worst results were obtained using the coarse{}-grid with the SGS model which overpredicted the experimental values by nearly a factor of two. The mean helium concentration decayed faster in the experiments than in the simulations, producing worsening agreement between experiments and simulations with increasing distance from the source.
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DesJardin \textit{et al}. [1] also presented predicted RMS concentration fluctuations which showed significant grid sensitivity and poor overall agreement with the experiments (errors of up to 200\%). This was attributed to the sensitivity of the concentration fluctuations to the small scales of motion that were not resolved by the LES. They suggested that the RMS velocity fluctuations did not show the same degree of sensitivity due to the smoothing effect of the pressure gradient in the momentum equation. The poor prediction of the concentration fluctuations has important implications for fire simulations, where the mixing of fuel and air determines the overall heat release rate.
Contributed by: Simon Gant — UK Health & Safety Laboratory
© copyright ERCOFTAC 2010