Week 1- Mixing Tee

AIM: To stimulate flow of air through a mixing tee and study the mixing effectiveness of this in pipes of different length and different momentum ratio

Objective: In this challenge, the turbulent mixing of two streams of fluid is to be simulated and studied in a mixing-Tee geometry. The primary aim is to run different cases and come up with the effects of using a higher momentum ratio and longer Tee. The effectiveness of mixing is to be determined at various physical conditions.

Case Setup:

Understand K-epsilon and k- Omega SST by considering case-1

Case-1

short mixing tee

1. Hot inlet velocity -3m/s, Momentum ratio-4
2. Hot inlet velocity -3m/s, Momentum ratio-2

Case-2

Short mixing tee

1. Hot inlet velocity -3m/s, Momentum ratio-4
2. Hot inlet velocity -3m/s, Momentum ratio-2

We use K-epsilon and K-omega SST methods for this case to converge the solution. Where K-epsilon predicts the flow virtually far from boundaries(wall) and K-omega model predicts near the wall.

Our first study will set 2 setups , one for K-epsilon and another for K-omega SST. Here, we are going to use the same geometry and mesh for the both cases of simulation. Here we are using short mixing tee model and mesh it

Open Ansys, Double click on the Fluid Flow(fluent)

Step1: Geometry

Double click on Geometry then space claim will be open and drag the required CAD model( Short mixing tee)

Then go to prepare in space claim and select volume extract and then select the required edges for fluid flow and click on complete

Now in structure, We can see 2 designs one is the entire CAD model(Short mixing tee) and other one is extracted volume model for Simulating fluid flow.

Untick the Actual CAD model and supress it for physics

Now close the space claim

Step2: Mesh

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

Next, create two fluent steups-one for k-epsilon another for k-omega SST.

click ctrl+F(face) for face selection and name the faces for inlet and outlet

provide suitable element sizing and generate the mesh

After mesh generation close the mesh and open solution

Step3:Solution

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

Next, in the domain panel by using the check button we can check our mesh prefrable 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 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.

Then we will set the physics of the domain. Keep the general parameter settings 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 viscous 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 materials click' FLUENT Databse' to add material. In this case our working fluid is air so we keep the default material.

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

In B.C click on the inlet_x and edit the inlet parameter set velocity as 3m/s and temperature as 36°C. For the inlet_y set velocity as 6m/s as we are using momentum ratio of 2 and temperature as 19°C.

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 outlet. The backflow will bring turbulence and energy back into the model.

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'.

Similarly create a plot for average velocity at outlet B.C

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

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.

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 geometry and 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 intialization is done.

Now we can run the simulation just enters the number of iterations for which we want to run the simulation and press calculate

when the solution will fully converge it will show the following message

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

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 of the model which can be turned on by checking the surface shown in the outline panel in the fluid selection.

Viewer toolbar buttons allow you to manipulate the view.

Create a plane in the location menu. Select plane>accept the default name 'plane1'> set method to be 'YZ planne', accept 'X' as 0.0 and press Apply

Finally select a plane from the outline section and use color by temperature which will generate temperature contour on the selected plane.

K-EPSILON K-OMEGA(STT)

Fig 1: Residuals of governing and transport equations for K-EPSILON Fig 2: Residuals of governing and transport equations for K-OMEGA

Fig 3: Temperature contour for K-EPSILON (Realzible) method Fig 4: Temperature contour for K-OMEGA(STT) method

Fig 5: Velocity contour for K-EPSILON (Realzible) method Fig 6: Velocity contour for K-OMEGA(STT) method

Observations from above results:-

• By comparing the above contours we can say that K-omega(STT) model is extracting model turbulence effect compare with k-epsilon method.
• From the above figures we can say that for getting converge solutions k- omega(STT) model is taking more time compare with k-epsilon model.
• Finally, we can say that we can use k-omega(STT) turbulence model for further simulations.

Case1:- Short Mixing Tee

(1) Momentum Ratio:2

Fig (7)-Residuals of both governing and transport equations

Fig (8) shows Area- Weighted average temperature and Standard Deviation temperature respectively

Fig (9) shows Area- Weighted average velocityand Standard Deviation Velocity respectively

Fig(10) Temperature and velocity contour along the length of Tee

Fig(11) Temperature and velocity contour along the cross section of Tee

Fig(12) Temperature and velocity line plot along the length of Tee

Fig(13) Temperature and velocity line plot across the length of Tee

(2) Momentum Ratio:4

Fig (14)-Residuals of both governing and transport equations

Fig (15) shows Area- Weighted average temperature and Standard Deviation temperature respectively

Fig (16) shows Area- Weighted average velocity and Standard Deviation Velocity respectively

Fig(17) Temperature and velocity contour along the length of Tee

Fig(18) Temperature and velocity contour along the cross section of Tee

Fig(19) Temperature and velocity line plot along the length of Tee

Fig(20) Temperature and velocity line plot across the length of Tee

Case2:- Long Mixing Tee

(1) Momentum Ratio:2

Fig (21)-Residuals of both governing and transport equations

Fig (22) shows Area- Weighted average temperature and Standard Deviation temperature respectively

Fig (23) shows Area- Weighted average velocity and Standard Deviation Velocity respectively

Fig(24) Temperature and velocity contour along the length of Tee

Fig(25) Temperature and velocity contour along the cross section of Tee

Fig(26) Temperature and velocity line plot along the length of Tee

Fig(27) Temperature and velocity line plot across the length of Tee

(2) Momentum Ratio:4

Fig (28)-Residuals of both governing and transport equations

Fig (29) shows Area- Weighted average temperature and Standard Deviation temperature respectively

Fig (30) shows Area- Weighted average velocity and Standard Deviation Velocity respectively

Fig(31) Temperature and velocity contour along the length of Tee

Fig(32) Temperature and velocity contour along the cross section of Tee

Fig(33) Temperature and velocity line plot along the length of Tee

Fig(34) Temperature and velocity line plot across the length of Tee

Effect of length and momentum ratio on results:

• From the above results we can say that outlet temperature is independent of length and mixing tee.
• Momentum ratio effects the outlet temperature. By increasing the momentum ratio outlet temperature is dropping.

Comparision between two cases:

	Momentum ratio	No. of elements	Avg. outlet temp. in °C	No. of iterations for convergence
Short Tee	2	11693	30.25	213
Short Tee	4	11693	27.53	243
Long Tee	2	16230	30.34	262
Long Tee	4	16230	27.49	276

Mesh Independent Test(Grid Independent Test):

Case study is done for Short mixing tee with momentum ratio 2.

Grid size	No. of elements	Avg. outlet temp. in °C
0.003	13014	30.251
.0021	14453	30.209
0.0019	39951	30.275

Conclusion:

• k-epsilon models predicts flow virtually far from the boundaries(wall) and k-omega model predicts near the wall. As in this case we need a flow simulation turbulent model far from the wall, therefore k-epsilon method is used.
• Solving the first simulation on the fluid domain using coarse mesh gives an initial overview of the analysis which coverge results fast. As element number increases the convergence time also increases. Therfore, after obtaining the rough results using coarse mesh simulation successfully we can improve mesh quality and get to final accurate results doing this saves a lot of time which is also extremely helpful for mesh independent test.
• To get better mixing at outlet high velocity of flow is very effective. Therefore, a higher Momentum ratio gives us better mixing of hot and cold fluid stream at the outlet.
• As the length of the mixing tee increases because diffusion flow always gives slightly better mixing of two-fluid.
• Finally, in the above cases, it is very important to use a short mixing tee with a momentum ratio 4 to get precise results but one has to consider the diffusion of flow along the length of the mixing tee, a long mixing tee with momentum will be greater option.