Week 1- Mixing Tee

Aim: To conduct a mixing Tee simultaion for 2 different lenth of pipe

Objective:

• Set up steady-state simulations
• Compare the mixing effectiveness when the hot inlet temperature is 36C, and the cold inlet is 19C.
• Try both the k-epsilon and k-omega SST models for the first case then use the most suitable model for further simulations.
• Simulate each case for the given velocity and momentum ratio 
  • Case 1 
    • Short mixing tee with a hot inlet velocity of 3m/s.
    • Momentum ratio of 2, 4.
  • Case 2 
    • Long mixing tee with a hot inlet velocity of 3m/s.
    • Momentum ratio of 2, 4. 
• Compare results from the 2 cases about cell count, average outlet temperature, and the number of iterations for convergence.
• Plot the velocity and temperature contour plots on the cut plane along and across the pipe.
• Also, plot the velocity and temperature line plots along and across the length of the pipe.
• Perform Mesh independent study for any one case.
• Moreover, discuss the effect of length and momentum ratio on results.
• Lastly, for each simulation, show the convergence plot.

Introduction:

Mixing tees are widely used in the petrochemical industry, in which two fluid streams with different physical and/or chemical properties mix together. When there is a large temperature difference between the two streams, large metal temperature fluctuations can occur in the region where the two streams meet if proper design steps are not taken. The temperature fluctuations in the metal may lead to thermal fatigue of the pressure boundary piping. If through-wall cracking occurs, the fluid may leak, possibly causing a fire and damage. It is therefore imperative to make sure that the temperature fluctuations are such that the piping system can withstand them. Options can include improving the mixing of the two streams to reduce the temperature fluctuations through redesign of the piping system such that the momentum ratio of the two fluid streams is favorable to rapid mixing, or to install devices that protect the pressure boundary piping.

A tee junction simulation is used here to evalute the change in temparature by mixing same fluid at different temparatures and with different inlet velocities through two inlet pipe which are perpendicular to each other.The simulation is done in two cases, for each cases only geometry varies (one is shorter than the other).Also for each cases we will be simulating for momentum ratio of 2 and 4.

The workflow in Ansys fluent is as follows

Gemoetry -> Mesh -> Setup -> Solution -> Results 

Given data

Hot inlet temperature = 36 c

Cold inlet temperature = 19 c

Hot inlet velocity = 3 m/s

since, Mometnum ratio = Velocity of cold inlet / Velocity of hot inlet

Therefore, 

For momentum ratio 2

Velocity of cold inlet = 6 m/s

For momentum ratio 4

Velocity of cold inlet = 12 m/s

 

Case 1 : SHORT MIXING TEE

Let us start with analyzing the best method to converge the solution for this particular case by studying two methods: k-epsilon and k-omega SST.

The aforementioned methods use simulation of turbulence modeling where k-epsilon models predict flow virtually far from the boundaries (wall), and the k-omega model predicts near the wall.

The selection of model depends on the specific problem; no turbulence model suit for every case. Fine-tuning requires the SST model that used good mesh at the boundary, and with wall treated usually works in the most case; it is a problem specific.

Our first study will set two setups, one for k-epsilon and another for k-omega SST. Here, we are going to use the same geometry and mesh for both cases of simulation. Hence, we are going to use a short mixing tee model and mesh it.

Start with dragging and dropping mesh setup to load the geometry and mesh it.

                                      

 

Then open space claim to load geometry in setup. Space claim is a beneficial CAD cleanup software to prepare CAD geometry for simulation setup; it could be for FEA or CFD.

Therefore, load the geometry and extract the fluid volume (working fluid volume).

 

Suppress the actual model, as we do not have to make any conduction simulation in this project. Lastly, close the space Claim.

Now, we can go for meshing. So, click on the mesh button, which will start Ansys meshing, where we can generate the mesh and provide names for all the surface of the geometry.

Next, create two fluent setups—one for k-epsilon and another for k-omega SST. Drag the exiting mesh into the Fluent analysis Setup cell. Then update the mesh (Right-click on mesh and use the update button) to convert and write the mesh for the FLUENT analysis.

 

We can check our meshing by cutting the fluid domain into the section as shown below to observe mesh flow. And we can also investigate the quality of mesh by clicking on the option called element quality in the quality section of the mesh panel.

Then, double click on the setup option in the FLUENT cell for trying the k-epsilon method. It will open the FLUENT interface.

                                      

 

In the Fluent window, we will first see the console window which displays text, and it also accepts TUI (Text User Interface) commands. In the beginning, we will see a lot of text about loading meshing cells, text about achromatic setting boundary condition in fluent as per name provided to surfaces.

After the complete loading of domain geometry, it will represent it in a graphical user interface very remarkable manner for the user to understand how imported boundary conditions will look.

Next, in the domain panel by using the check button we can check our mesh preferable for fluent analysis or not. And we can improve the quality using modify option.

After clicking on the check mesh quality button, it prints the following conformation in the console Window. The mesh check ensures that each cell is in a correct format and connected to other cells as expected. It is recommended to check every mesh immediately after reading it. Failure of any check indicates a badly formed or corrupted mesh that will need repairs prior to simulation.

Click ‘units’ to change the units of temperature. FLUENT store values in SI units. Most post-processing can be converted into other units.

Then we will set the physics of the domain. Keep the general parameter setting as it is. Solver type -> pressure-based; velocity formulation -> Absolute; solver time -> steady.

Next, activate models. First, Turn on the energy  Equation. Activating energy equation allows the temperature-dependent problem to be solved.

In the viscus model, select ‘k-epsilon’,’ Realizable’ for our first type of simulation. Turbulence modeling is a complex area. The choice of model depends on the application. Here, the Realizable k-epsilon model is used which is an improvement on the well-established standard k-epsilon model. Accept the remaining default settings.

In the cell zone define the material, select the working fluid.

In ‘Boundary condition’ click on the ‘Inlet-x’ and edit the inlet parameter set velocity as 3m/s and temperature as 36^oC. For inlet_y set velocity as 12m/s as we are using momentum ratio of 2 and temperature as 19^oC.

Set outlet pressure condition as atmospheric pressure value which is also zero gauge pressure. The simulation may predict that flow enters the model through part of the outlet. The backflow will bring turbulence and energy back into the model. However, the model can not predict how much (because the flow is coming from outside of the model). It is, therefore, necessary to specify backflow conditions. Ideally, the geometry should be selected such that flow enters the model only at well-defined inlets. The backflow setting then does not affect the final solution (although they may be used in intermediate iterations).

In ‘Reports’, press ‘new’ for the surface report monitor. Select area-weighted average.

here we want temperature average at outlet surface so select temperature. And check “report file” and “report Plot”.

And, similar way create a plot for average velocity plot at outlet boundary condition.

Before running the program our final step will be to initialize the problem setup.

Initialization creates the initial solution that the solver will iteratively improve. Generally, the same converged solution is reached whatever the initialization, though convergence is easier if they are similar. Basic initialization imposes the same values in all cells. You can improve on this in various ways - for example, by patching different values into different zones. Several features, including patching and post-processing, are not available until after initialization.

The hybrid Initialization method is an efficient method of initializing the solution based purely on the setup of the simulation with no extra information required. These methods produce a velocity field that conforms to complex domain geometries and a pressure field that conforms to complex domain geometries and a pressure field that smoothly connects high and low-pressure values.

after hitting initialize button we will get the following data printed in the console window that says initialization is done.

Now, we can run the simulation just enter the number of iteration for which we want to run the simulation and press ‘calculate’.

When the solution will fully converge it will show the following massage Calculation complete.

While calculating the solution we will also get our converge plot for the k-epsilon method. It also built up another plot report of temperature and velocity at the outlet that we set earlier.

For moment ratio 2

(This is for k-epsilon SST model)

Convergence plot

Area waited average of temprature at outlet for each iteration

 

Area waited average of velocity-mag at outlet for each iteration

Standard deviation of temprature at outlet for each iteration

Standard deviation of velocity-mag at outlet for each iteration

After this, we can close the fluent and post-process the results. Click on the results in the workbench and it will open CFD-Post.

CFD-Post initially displays the outline (wireframe) of the model which can be turned on by checking the surface shown in the Outline panel in the fluid section.

Viewer toolbar buttons allow you to manipulate the view.

Create a plane - in the location menu, select ‘Plane’ -> accept the default name ‘Plane 1’ -> set ‘Method’ to be ‘YZ Plane’, accept ‘X’ as 0.0, and press ‘Apply’

For temprature contour.                                                           

Now, for velocity contour.

Number of Iteration taken for covergence is

This time, we are going to use the same setting for the next simulation and solve the problem by the k-omega SST method. And compare all the results

Convergence plot

Area waited average of temprature at outlet for each iteration

 

Area waited average of velocity-mag at outlet for each iteration

Standard deviation of temprature at outlet for each iteration

Standard deviation of velocity-mag at outlet for each iteration

For temprature contour.                                                           

 

Now, for velocity contour.

Number of Iteration taken for covergence is

 

For moment ratio 4

For k-epsilon SST model

Convergence plot

Area waited average of temprature at outlet for each iteration

 

Area waited average of velocity-mag at outlet for each iteration

Standard deviation of temprature at outlet for each iteration

Standard deviation of velocity-mag at outlet for each iteration

For temprature contour.                                                           

 

Now, for velocity contour.

Number of Iteration taken for covergence is

For k-omega SST model

Convergence plot

Area waited average of temprature at outlet for each iteration

 

Area waited average of velocity-mag at outlet for each iteration

Standard deviation of temprature at outlet for each iteration

Standard deviation of velocity-mag at outlet for each iteration

For temprature contour.                                                           

 

Now, for velocity contour.

Number of Iteration taken for covergence is

For further calculation

The k- omega model is well suited for simulating flow in the viscous sub-layer. On the contrary, The k-epsilon model is ideal for predicting flow in the regions away from the wall

The SST model exhibit less sensitivity to free stream conditions (Flow outside the boundary layer) than many other turbulence models.

The shear stress limiter helps the k-omega model avoid a build-up of excessive turbulent kinetic energy near stagnation points.

If we observe our simulated figure, we can notice, in the k-omega model flow is more like laminar and not giving proper turbulent results, in contrast, simulation by k-epsilon, is giving proper mixing of the two air stream.

In this particular case we are not dealing with walls, therefore, we are going to use the k-epsilon method for further simulations.

 

Case 2 : LONG MIXING TEE

For moment ratio 2

Convergence plot

Area waited average of temprature at outlet for each iteration

 

Area waited average of velocity-mag at outlet for each iteration

Standard deviation of temprature at outlet for each iteration

Standard deviation of velocity-mag at outlet for each iteration

For temprature contour.                                                           

 

Now, for velocity contour.

Number of Iteration taken for covergence is

Velocity line plot along the length of the pipe.

 

Temperature line plot along the length of the pipe.

 

 

For moment ratio 4

Convergence plot

 

Area waited average of temprature at outlet for each iteration

 

Area waited average of velocity-mag at outlet for each iteration

 

Standard deviation of temprature at outlet for each iteration

 

Standard deviation of velocity-mag at outlet for each iteration

 

For temprature contour.                                                           

 

Now, for velocity contour.

 

Number of Iteration taken for covergence is

 

Velocity line plot along the length of the pipe.

Temperature line plot along the length of the pipe.

 

Mesh independent study for long tee of momentum ratio 4

Convergence plot

 

Area waited average of temprature at outlet for each iteration

 

Area waited average of velocity-mag at outlet for each iteration

 

Standard deviation of temprature at outlet for each iteration

 

Standard deviation of velocity-mag at outlet for each iteration

 

For temprature contour.                                                           

 

Now, for velocity contour.

 

 

Velocity line plot along the length of the pipe.

Temperature line plot along the length of the pipe.

 

Workflow

 

Effect of length and Momentum ratio

 

Momentum ratio	Pipe length	Outlet temperature
2	Short	302.159 k
4	Short	301.737 k
2	Long	302.654 k
4	Long	302.039 k
4	Long(inflamation)	302.221 k

 

 Conclusion:

• It can be observed that when the momentum ratio is 4 that is when cold inlet velocity is 12 m/s there may be slight drop in temperature when compared with momentum ratio of 2 , This is applicable for both the cases. This is due to the fact that when the cold inlet velocity is increased there is more turbulent mixing happening which further reduces the temperature
• So comparing the standard deviation of temperature of short and long pipe of momentum ratio of 4 it was observed that there was less deviation in long pipe (plots are in respective cases). So it is recommended to use long pipe of effective mixing