## HVAC SIMULATION INSIDE A MIXING TEE GEOMETRY USING ANSYS FLUENT

HVAC SIMULATION INSIDE A MIXING TEE GEOMETRY USING ANSYS FLUENT

I. OBJECTIVES

1) Simulate the flow of a fluid through a Mixing Tee geometry.

2) Analyse the mixing effectiveness for different case setups.

II. PROBLEM STATEMENT

1) Simulate the flow of a fluid through the given mixing tee geometry for the following cases -

• Case 1
• Short mixing tee with hot inlet velocity of 3 m/s
• Momentum ratio of 2
• Case 2
• Long mixing tee with hot inlet velocity of 3 m/s
• Momentum ratio of 2
• Case 3
• Short mixing with hot inlet velocity of 3 m/s
• Momentum ration of 4

2) Show the steady state convergence plot for all the different case setups. Also show the Temperature & Velocity distribution throughout the length of the geometry.

3) Calculate the average steady state temperature at the outlet of the geometry.

III. METHODOLOGY

STEP 1 - GEOMETRY

The geometry of the mixing tee is as shown below -

1. Solid Model  The initial model is a solid model. In this project, we are only interested in performing the flow simulation. Hence, we shall extract only the fluid model i.e. the parts of the solid in contact with the fluid & we shall suppress the physics of the remaining parts.

The geometry that shall be considered for flow simulation is as given below -

2. Fluid Model  3. Dimensions  For Case 1 & Case 3, we shall be using the fluid model of Short Mixing Tee.

For Case 2, we shall be using the fluid model of Long Mixing Tee, in which only the length of the cylinder is increased.

STEP 2 - NAMING THE BOUNDARIES

We first have to name the boundaries of the model -

• Cold_Inlet
• Hot_Inlet
• Outlet
• Walls

These named selections shall help us to set the initial conditions and also in analysing the output. STEP 3 - MESH

The selected mesh size = 0.002m for all the three cases.

The generated mesh is as shown below -

Case 1 & 3 - Short Mixing Tee

Number of Nodes = 21330

Number of Elements = 105751  Case 2 - Long Mixing Tee

Number of Nodes = 28370

Number of Elements = 140510  Mesh Quality

The element quality is an important parameter that helps us to determine the quality of the mesh. If the quality of the mesh is very low then we can get inaccurate results.

Case 1 & 3 - Short Mixing Tee Case 2 - Long Mixing Tee The lowest quality of the mesh in both the cases is above 5 percent, so we can proceed with the selected mesh size.

STEP 4 - PROBLEM SETUP

1. Solver

We need to now apply the appropriate solver to solve the given problem.

Here we shall be applying the pressure - based type steady state solver using the realizable k - epsilon turbulence model to solve the given problem.

2. Fluid Materials

We also need to update the fluid material & its properties. In this project, we shall be taking the fluid as air.

3. Boundaries

We need to apply the different boundary conditions for all the three cases as follows -

Case 1 - Case 2 - Case 3 - We shall define the temperature values at the two inlets as follows -

For Cold_Inlet, T = 10^oc

For Hot_inlet, T = 25^oc

4. Convergence Criteria

The simulation stops when the residuals falls below the convergence criteria. In this project, we shall assume the convergence criteria to be 1e-3.

5. Defining Reports & Plots

1) Area Weighted Average of Temperature at Outlet

In this project, we are interested in knowing as the changes in the temperature at the outlet for the different cases. Hence, we need to print the report & plot for the temperature using the area weighted average method at the outlet.

2) Standard Deviation of Temperature at Outlet

Another important parameter that is used for analysis is the standard deviation of the temperature at the outlet. It help us determine the effectiveness of mixing at the outlet. In order to ensure that mixing effiency is good & the temperature is uniform at the outlet, the standard deviation should be low.

6. Initializing & Computing the solution

Finally, we initialize the solution at t = 0. Once intialization is done, we set the Number of iterations as 500 and then calculate the solution for the given problem.

STEP 5 - RESULTS

The following plots are obtained -

Case 1 - Short Mixing Tee (Momentum Ratio = 2)

Figure 1 - Convergence/Residual Plot All the residuals falls below the convergence criteria & hence the solution convergence at 237 iterations.

Figure 2 - Plot of Outlet Temperature (Area Weighted Average) As can be seen in this plot, the temperature at the outlet is averaged to T = 20.1129^oc.

Figure 3 - Plot of Standard Deviation of Outlet Temperature The standard deviation of the temperature at the outlet is 1.6872. As it is very small, we can say that the mixing is efficient & the temperature is uniform at the outlet.

Case 2 - Long Mixing Tee (Momentum Ratio = 2)

Figure 1 - Convergence/Residual Plot In this case, the continuity residuals falls does not fall below the convergence criteria within 500 iterations. However, we can observe that the variations in the plot repeat itselves after a given number of iterations. Hence we may not get the solution with the required tolerance even after increasing the number of iterations.

However, the residuals are of very low order, hence we can say that the solutions are converged after 500 iterations.

Figure 2 - Plot of Outlet Temperature (Area Weighted Average) As can be seen in this plot, the temperature at the outlet is averaged to T = 20.2070^oc

Figure 3 - Plot of Standard Deviation of Outlet Temperature The standard deviation of the temperature at the outlet is 1.0833. As it is very small, we can say that the mixing is efficient & the temperature is uniform at the outlet.

Case 3 - Short Mixing Tee (Momentum Ratio = 4)

Figure 1 - Convergence/Residual Plot Once again in this case, the continuity residuals falls does not fall below the convergence criteria within 500 iterations. However, the residuals are of very low order, hence we can say that the solutions are converged after 500 iterations.

Figure 2 - Plot of Outlet Temperature (Area Weighted Average) As can be seen in this plot, the temperature at the outlet is averaged to T = 17.6936^oc

Figure 3 - Plot of Standard Deviation of Outlet Temperature The standard deviation of the temperature at the outlet is 0.9568. As it is very small, we can say that the mixing is efficient & the temperature is uniform at the outlet.

STEP 6 - POST PROCESSING

Finally we need to observe the velocity & temperature distribution throughout the length of the geometry. This will help us to understand the overall mixing effectiveness.

We shall use a section plane to cut the geometry in half along the z - axis and observe the temperature & velocity contours along this plane.

Case 1 - Short Mixing Tee (Momentum Ratio = 2)

Figure 1 - Temperature Contour Figure 2 - Velocity Contour Case 2 - Long Mixing Tee (Momentum Ratio = 2)

Figure 1 - Temperature Contour Figure 2 - Velocity Contour Case 3 - Short Mixing Tee (Momentum Ratio = 4)

Figure 1 - Temperature Contour Figure 2 - Velocity Contour IV. RESULTS

The important results obtained from the given problem are compared for all the three cases. Note: * -> The solution is assumed to have converged.

The following results were obtained from the table -

1) Increasing the length of the geometry doen not result in any significant change at the outlet.

2) Increasing the length of the geometry increases the number of cells which results in an increase in the computation time.

3) Increasing the momentum ratio results in the decrease of the average outlet temperature.

4) Increasing the momentum ratio results in an increase in the number of iterations required for convergence which furthur results in an increase in the computation time.

V. CONCLUSIONS

1) The shorter mixing tee is much more preferable than the longer mixing tee, as we can obtain the same output without increasing the number of cells in the geometry.

2) The momentum ratio is an important parameter that can be used to change the temperature at the output for a given geometry. Increasing the momentum ratio results in a decrease in the average temperature at the outlet & vice - versa.

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