UFR 1-07 Evaluation

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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 11  An instantaneous snapshot of the DesJardin et al.‘s LES predictions showing an iso-contour of vorticity magnitude at 5% of the maximum coloured according to the magnitude of the gravitational torque.

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.

Figure 12  

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Contributed by: Simon Gant — UK Health & Safety Laboratory

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