*** This version of Confluence is for testing only and contains a copy of content from June 29th 2026. No changes will be preserved. ***
...
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
