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Flocculation is an important process used by AguaClara to treat water. The process involves particle collisions and agglomeration to form flocs. Computational Fluid Dynamics was used to better understand the fluid dynamics in the reactor. The standard K-ε model was used for every simulation model. The pressure coefficient drop over one baffle turn is 3.75, which agrees with literature estimates. After a clearance height of one baffle width or greater, the pressure coefficient drop and the maximum velocity become approximately constant. Most of the energy dissipation occurs in the region after the turn, over a distance of two baffle widths. An area of flow recirculation occurs near the center wall immediately after the turn. The pressure drop is not sensitive to the Reynolds number for a large range of inlet velocities. Better understanding of the flocculation dynamics will enable optimized particle agglomeration and break-up.
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Flocculation is the process by which particles collide and agglomerate. Past research has shown that shear gradients play an important role during flocculation. This process was simulated using Computational Fluid Dynamics (CFD). The main task of this research is to find the optimum strain rate in the reactor to influence particle collision. Gambit and FLUENT were utilized to model one baffle turn. Gambit was used to create the geometry of the flocculator, and to generate the mesh. FLUENT was used set up the boundary conditions and to obtain the results.
2. Methodology
The real life flocculation tank used by AguaClara involves 180 deg turns over few dozens baffles. To save computational effort, a simple only one 180 deg turn over two baffles was modeled. The first step was to set up the geometry of the turn. For To enable future comparison with the experimental data, the design parameters for geometry mimics the pilot plant were used. The modeling approach was to create the geometry, mesh it, set boundary conditions, and solve it using FLUENT.
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Height: 1 m
Clearance: 0.15 m
Baffle width: 0.1 m
Velocity inlet: 0.1 m/s
With the geometrythese parameters, the mesh for the model can then be set upbe built.
2.2 Setting up Mesh
Figure 2. Meshing Parameters (Click on the figure to see the original size)
Figure 2 shows the meshing parameters that were used. The boundary layers was were first established at all the wall surfaces. The boundary layer was They were set such that the solution would provide a result of y+ less than 5. After that, the mesh edges were set up such that they will meshed as to provide higher mesh resolution near the turn. With the initial meshing conditions set upIn the final step, all the faces were then meshed.
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Figure 3 shows the mesh of the flocculator model. As can be seen, the mesh is fine near the turn and at the walls. The final next step at this point was to set up the boundary conditions of the system.
2.3
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Boundary Conditions
Figure 4. Boundary Conditions
Figure 4 shows the boundary condition that was conditions used for modeling. For a flocculator, there is an one in-flow and one out-flow of the fluidsboundary conditions. Since inlet velocity inlet was known from the experimental data, the inlet was set to the Velocity Inlet type boundary condition. The outlet was set to Pressure Outlet boundary condition type, equal to the atmospheric pressure.
The 2-Dimensional mesh was then saved and exported to FLUENT for further obtaining solution and further analysis.
2.4 Solve using FLUENT
At this stage, the Standard k-ε turbulence model was set up. Water was defined as the working material from the FLUENT database. The discretization method for the momentum, turbulent kinetic energy, and turbulent dissipation rate were set to the 'Second Order Upwind' scheme to obtain a 'Second Order Accurate' solution. The boundary conditions were set according to the values shown in the table below. The solution was obtained by iterating until the residuals converged to 10e-6. Results were then analyzed and plotted.
TABLE IN with BCS TABLE 1
2.5 Mesh Sensitivity Analysis
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