Week 1- Mixing Tee
Title – Mixing Tee Effectiveness
Objective
To simulate the flow of air through mixing tee and understand the effect of length of pipe and momentum ratio of velocity for mixing of air by using two different types of pipe i.e. short pipe and long pipe.
- To clean the geometry and mesh it in pre-processing.
- To solve the model with best solver in Ansys solver.
- To generate contour and line plots and analyse it in postprocessor.
Introduction –
- Computational fluid dynamics is a branch of fluid mechanics, which uses mathematics, physics and computational software to visualize how fluid flows and affects its medium and its neighbours. In CFD mostly computers are used to predict the fluid flow phenomena based on some governing equations like conservation of momentum – Navier stokes equation, conservation of energy etc.
- In this study we are going to simulating effect of fluid using Ansys fluent software. Ansys fluent is a very important tool in CAE domain, which is allows you to perform all the three major steps of any simulation pre-processing, solving and post-processing. Ansys fluent facilitates for modelling flow, turbulence, heat transfer and chemical reactions. With Ansys we can accomplice more in less time and with less training.
About
In industrial process engineering, mixing is a unit operation that involves the manipulation of a heterogeneous physical system with the intent to make it more homogeneous. Mixing is performed to allow heat and/or mass transfer to occur between one or more streams, components or phases. Modern industrial processing almost always involves some form of mixing. Mixing tee is a type of mixing mechanism.
Mixing Tees utilizes a specifically engineered internal geometry to efficiently mix two fluid streams into one combined stream. Mixing tees are widely used in the petrochemical, HVAC industry etc. in which two fluid streams with different physical and/or chemical properties mix together.
Hot air flowing in the main pipe is mixed with cold air flowing through a tee. The standard deviation of temperature is computed to quantify the degree of mixing. The velocity and temperature fields are also computed. The effects of the mesh size, turbulence model on results were examined.
For this, we have created two versions of the mixing tee. One of them is longer than the other.
It is set up as steady-state simulations to compare the mixing effectiveness when hot inlet temperature is 360C & the Cold inlet is at 190C.
- 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.
Momentum ratio is defined as the ratio of velocity at the cold inlet to the velocity at the hot inlet.
Procedure -
There are three major steps to perform any type of simulation as follows pre-processor – it includes geometry clean up and generate best quality mesh. Solving includes applying boundary conditions, initialize and convergence. Last step is post processor which includes generate plots, contours, vectors to analyse results. In this study we are going to follow all three steps to find out effectiveness of mixing tee. The following steps to be followed –
1. Open new project, by clicking on Fluid flow (Fluent) in Ansys workbench.
2. Now click on geometry to import mixing tee model. It will redirect to Spaceclaim, Ansys’s geometry model manipulator.
3. We are to analyse the mixing of two fluid, so there is role of parts other than two pipes. To supress that we need to extract volume of tee. Go to prepare menu and click on volume extract. Switch to edge selection and select three outer edges and click on green check button.
4. Hide solid by unchecking it from structure menu to see volume, which we have extracted. Supress solid by clicking on option supress for physics.
5. Exit from Spaceclaim and click on mesh to open mesh menu.
6. Select outer face and press right click of mouse, it will open a menu. Click on option create named selection and write suitable name for face. Repeat for other faces.
7. Now click on mesh option and choose generate mesh. It will generate mesh according to some predefined criteria. Element size can be selected but it is going to affect simulation time. Section plane can be created to see inner portion of geometry.
8. Select the option element quality to see, how many elements are defying quality. Quality is measured on the scale of 0 to 1. In our graph most of the element are near to 1 so we can say it is good quality.
9. Exit mesh and open setup. First, Check mesh, it is throwing no error then only we can proceed further. We are going to do pressure based steady state simulation, so choose these options on task page.
10. Go to physics menu and select viscous model. It will open a menu, choose realizable k-epsilon model and close it.
11. Click on material and choose air as working fluid. Check working fluid by clicking on cell zone conditions option, if it is air then proceed.
12. Now go to boundaries option to set boundary conditions. Select inlet_x and click on option edit. It will open a menu, where all the parameter can be set whether it is thermal, radiation, momentum etc. Put the value of velocity as 3m/s and go to thermal parameter to set temperature as 36C, for hot fluid.
13. Repeat the same procedure for cold fluid coming, velocity –6 m/s, temperature – 19C.
14. We want to generate temperature report of outlet and we want standard deviation of temperature also. Go to solutions tab and select definition and select area weighted average. In the opened menu select temperature as field variable. Similarly select standard deviation in definitions menu.
15. Now click on initialization to calculate approximate values of velocity and pressure.
16. Change the number of iterations to 250 and click on calculate. If solution is not converged, then iterations can be increased.
17. Exit setup and double click on result to open CFD post. Check boxes to see walls and other parts. Click on walls and go to render set transparency of 0.4 to inside of wall. Colour can also be changed to white for clear visibility.
18. Now insert a XY plane in wall by clicking on plane in location option of insert.
Fig. Transparent Mixing Tee with XY plane
19. Go to colour of plane and change it by variable choose option temperature. Similarly, velocity contours can be created by just changing variable as velocity.
20. Right click on plane which we created and select streamline. Go to appearance and select temperature to see plot line on plane. Similar procedure can be repeated for velocity plot line.
Results –
Case 1 – In this short mixing tee is used, which was mixing two fluid with temperature 36C and 19C. Velocity of hot fluid was 3m/s and cold fluid was 6m/s. we used two different turbulent solvers for it first was realized k-epsilon and second was k-omega SST. (Using Momentum Ratio of 2).
In this case k-epsilon is giving better results because solution was converged after few iterations, whereas after using k-omega model solution was not converged. K-omega is preferred in studying fluid behaviour near wall, but here it is not the case. So, k-epsilon model is recommended to study of fluid far from wall i.e. flow region, in further cases.
MOMENTUM RATIO = 2,
Plot and contours from k-epsilon model
Fig - Obtained Mesh
Fig - Average temperature vs iterations
Fig - Standard Deviation of Temperature
Fig - Scaled Residuals
Fig - Temperature Contour
Fig - Velocity Contour
Fig - Temperature streamline of x - axis
Fig - Temperature streamline of y-axis
Fig - Velocity Streamline of x-axis
Fig - Velocity Streamline of y-axis
Plot and contours from k-omega model
Fig - Mesh
Fig - Average temperature vs Iterations
Fig - Deviation per Iterations
Fig - Scaled residuals
Fig - Temperature Contour
Fig - Velocity contour
Fig - Temperature streamline of x-axis
Fig - Temperature streamline of y - axis
Fig - Velocity Streamline of x-axis
Fig - Velocity streamline of y-axis
MOMENTUM RATIO = 4
PLOTS FROM K -epsilon Model
Fig - Mesh
Fig - Average Temperature vs Iterations
Fig - Deviations per Iterations
Fig - Residuals
Fig - Temperature Contour
Fig - Velocity contours
Fig - Temperature Streamline x-axis
Fig - Temperature streamline y-axis
Fig - Velocity Streamline x-axis
Fig - Velocity streamline y-axis
Using K-omega Model
Fig - Mesh
Fig - Average temperature vs iterations
Fig - Deviations per Iterations
Fig - Scaled residuals
Fig - Temperature Contour
Fig - Velocity contours
Fig - Temperature Streamline x-axis
Fig - Temperature Streamline Y-axis
Fig - Velocity streamline y-axis
LONG MIXING TEE MODEL
Momentum Ratio = 2,
K-epsilon Model
Plots and contours
Fig - Mesh
Fig - Area Average vs Iterations
Fig - Deviations per Iterations
Fig - Scaled Residuals
Fig - Temperature Plane
Fig - Velocity Plane
Fig - Temperature Streamline x -axis
Fig - Temperature streamline y-axis
Fig - Velocity Streamline x-axis
Fig - Velocity Streamline y-axis
k-omega model
Plots and contours
Fig - Average temperature vs iterations
Fig - Standard Deviations of temperature
Fig - Temperature Contours
Fig - Velocity Contours
Fig - Temperature Streamline x - axis
Fig - Temperature Streamline y-axis
Fig - Velocity Streamline x - axis
Fig - Velocity Streamline y-axis
Momentum Ratio = 4
k-epsilon model
plots and contours
Fig - Area Weighted Average vs Iterations
Fig - Standard Deviation of Temperature
Fig - Scaled residuals
Fig - Temperature Plane
Fig - Velocity Plane
Fig - Temperature Streamline x-axis
Fig - Temperature Streamline y-axis
Fig - Velocity Streamline x - axis
Fig - Velocity Streamline y-axis
Comparison table –
|
Parameters |
Cell Count |
Average outlet temperature |
Number of iterations to converge |
|
Case 1 |
12812 |
30.20C |
199 |
|
Case 2 |
15588 |
29C |
157 |
In the above contour chart, we can see the flow difference of k-epsilon Realizable and k-omega SST let just first understand why they are different
k-Epsilon
- as we saw in our first simulation, the K-epsilon turbulence model is the most common model used in CFD to simulate mean flow characteristics for turbulent flow conditions. It is a two-equation model that gives a description of turbulence by means of two transport equations (PDEs). the first transported variables are the turbulent kinetic energy (k) and the second transported variable is the rate of dissipation of turbulent kinetic energy.
k-epsilon focuses on the mechanism that affects the turbulent kinetic energy.
k-omega SST
The SST k-omega turbulence model is a two-equation eddy-viscosity model that is used for many aerodynamic applications. It is a hybrid model combining the Wilcox k-omega and the k-epsilon models. A blending function, F1, activates the Wilcox model near the wall and the k-epsilon model in the free stream. This ensures that the appropriate model is utilized through the flow field
Mesh Independent Study –
Mesh independent test allows mesh refinement without affecting results. After some tests we can be ensured by solution that mesh refinement won’t affect the result. The following graphs are showing the results after mesh refinement on short mixing tee, earlier element size is used as 0.0195 and now it got 0.001.
Conclusion –
Ansys fluent is high performance CFD software recognized for accuracy, robustness etc. it is not confined to only aerospace and mechanical domain, it has various application like to simulation of calculation of air flow on air craft wing, combustion in furnace, bubble columns in oil refineries, combustion in IC engine etc. In this study we performed simulation on simple geometry of mixing tee and got insights of various feature of fluent. we can conclude by analysing graph that If momentum ratio is increased, outlet temperature will decrease and there is no effect of velocity even though length is increased.