CFD Fall 2008 Midterm Progress Report: (By Yong Sheng Khoo and Jesse Prager)

Overviews:

In modeling flocculation tank, we want to model the tank in the way that accurately represent the physical properties. In order to achieve that, couple of validation process are used.

1. The modeling data should not deviate from the measureble experimental data (for instance, pressure coefficient drop)

2. Mesh sensitivity analysis was carried out to see that the modelings result does not deviate much from using different mesh density.

3. Effect of boundary layer is taken into account. We need to create a mesh near the wall such that the cell next to the wall need to be at the y+ of smaller than 5 so that we can resolve the viscous sublayer.

4. Appropriate turbulence model is chosen for accurately representing the flow properties in flocculation tank. (This is done by comparing the difference turbulence model with the experimental results that have the same flow properties in the 180 degree turn in flocculator. Flow over a backstep was modeled)

Step one to three above has been verified over Spring 2008. Step four was validated at the beginning of the semester.

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. Table 1 shows the comparison results of different turbulence models.

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.
 

Automation of Mesh Creation Process