Validation of Turbulence Models:
Since we have unresolvable term in turbulence flow, different turbulence models or "estimation" were created to resolve turbulence flow with different characteristic. To decide on the turbulence model to use, a flow over backstep was compared with the literature experimental data. Figure 1 shows the flow of Re = 48000 over the channel. In the middle of the channel, the flow separate due to the small step size of height h. The flow reattaches at about 7 times the step height further downstream. This flow properties is similar to the 180 degree bend in the flocculation tank where we have flow separation and reattachment downstream (Figure 2).
Figure 1: Flow over backstep in a open channel (Re = 48000, Reattachment length = 7h)
Figure 2: Flow over 180 degree turn in flocculation tank
We compared the back step flow using K-e, K-W SST, K-e realizable, K-e RNG, RSM turbulence models. We need to pinpoint the reattachment points of all the turbulence models so that we can compare the reattachment ration with the experimental data.
Plotting the derivative du/dy, the change in direction of velocity in x direction with respect to y at the wall, we will be able to accurately pinpoint the reattachment point. At the wall, separation flow will give negative du/dy and and the flow reattaches, the du/dy will reach zero and becomes positive. Figure 3 shows the derivative of du/dy vs x direction for different turbulence model.
Table 1 shows the comparison results of different turbulence models.
Turbulence Model |
K-e |
K-W SST |
K-e realizable |
K-e RNG |
RSM |
|---|---|---|---|---|---|
Reattachment Ratio |
3.54 |
4.94 |
5.47 |
4.58 |
4 |
Table 1: Reattachment ratio with different turbulence models
From table 1, we see that the K-e under-predict the reattachment length, as known by most literature. K-e realizable gives the most accurate representation of the back step flow with reattachment length of 5.47. Therefore, K-e realizable was chosen as an appropriate model for the flow in flocculation tank.
Figure 3: Flow over backstep using K-e realizable model
Now that we have finish validating all the appropriate step, we are fairly confident with the accuracy of our model. We can start optimizing the geometry to get the desired flow properties that will promote particles agglomeration. For analyzing different geometry, meshes with different dimension parameters will have to be created. It is time consuming to create a new mesh from scratch just to change some small parameters. Therefore a journal script was written to automate the mesh creation process.