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MIXING TEE CFD SIMULATION ABSTRACT: The given problem is focused in analyzing the mixing effectiveness of hot and cold fluid in a tee shaped pipe. A numerical study using CFD approach is appliedto predict the outlet temperature and velocity of the pipe at a steady state condition. The computational geometry is modeled…
Amol Anandrao Kumbhar
updated on 16 Dec 2021
MIXING TEE CFD SIMULATION
ABSTRACT:
The given problem is focused in analyzing the mixing effectiveness of hot and cold fluid in a tee shaped pipe. A numerical study using CFD approach is appliedto predict the outlet temperature and velocity of the pipe at a steady state condition. The computational geometry is modeled using URANS based solver namely k-epsilon and k-omega model. With different momentum ratio, physical parameters like no of iterations, cell count, area weighted average, standarddeviation of temperature at the outlet is predicted. Finally, with different grading of mesh size a grid independence test is conducted to justify the effectivenessof mixing fluids and to measure out the best model for the problem.
INTRODUCTION:
Mixing problems, such as the design and scale-up of a mixer and quantification of mixing, have been traditionally tackled by developing empirical designequations mainly due to the complexity of the fluid dynamics of mixing. Although this approach has proven to be satisfactory for many applications, it is rather limited because it neglects the complexity of flow in most mixing applications. Applications where pipeline mixing with tees is used include low viscosity mixing such as wastewater treatment and blending of some oils (injection of additives) and petrochemical products. Other applications include blending of fuel gas,and mixing of feed streams for catalytic reactors. A tee is formed by two pipe sections joined at a right angle to each other. One stream passes straight throughthe tee while the other enters perpendicularly at one side as shown in
Fig 1
This flow arrangement is known as the side-tee. However other flow arrangements may be used, such as having the two opposing streams entering co-axially and leave through a pipe, which is perpendicular to the entering direction. This isknown as the opposed-tee.
Tee pipes are commonly used in piping systems and multi-channel network in various industries like electronic, petrochemical, nuclear plant etc. the mixing ofhot fluid and cold fluid give rise to temperature fluctuation in the vicinity of mixing zone. The flow pattern of fluid gives necessary information such as area,where thermal zones are high. Using different momentum ratio, the efficiency of the model and its effectiveness is checked.
PROBLEM STATEMENT:
To compare the mixing effectiveness of tee shaped pipe using k-epsilon and k-omega model at momentum ratio factor 2, 4. The inlet temperature and velocity conditions are given as 36degreeC at inlet x, 19degreeC at inlet y and 3m/s at inlet x.
OBJECTIVE:
1. Run simulation for small tee and long tee geometry based upon RANS model
2. Compare cell count, no of iteration and outlet temperature between two tee pipes.
3. Perform grid independence study.
4. Thermal and velocity profile plot on cut plane and across the geometry.
PREPROCESSING SETUP
1.GEOMETRY OF THE MODEL:
The geometry of the mixing tee pipe is attached to the ANSYS space claim geometry using the step file format.
Next, we extract the fluid volume region by selecting the edge command from the volume extraction tool at prepare physics section. The solid model is suppressed from the fluid model to create mesh at the region of interest.
Fluid Volume
2.MESH GENERATION:
Fluid geometry is imported to the workbench, where mesh size of 10mm is defined around the entire body.
Clicking on the statistics option, the no of elements and nodes are highlighted. The mesh metric shows the no of triangular, pyramidal, trapezoidal members required at a certain percentage to optimize the mesh model.
Using named selection option define the inlet x, inlet y, outlet and wall.
After clicking on mesh generation, click on update to update the mesh model.
Mesh Matix - Quality check of elements
3.SET UP OF PHYSICS SOLVER:
The FLUENT solver setting is activated, where the console and the graphics pane appear.
From General tab, we select the pressure-based solver having absolute velocity formation and within a steady state condition.
The energy parameter is activated and then the viscous model is selected, which is k-epsilon Realizable or K-omega SST.
Specify the material as air.
Define the boundary condition for inlet x, inlet y, wall by specifying the inlet temperature and velocity.
Cell Zone Conditions
Cold Inlet Velocity
Cold Inlet Temperature
Hot Inlet Velocity
Hot Inlet Temperature
Solution Method
Report Defination
The monitor setting is specified to two parameter area weighted average and standard deviation.
Residual Monitors
Convergenace Conditions
Solution Initialization
Initialize the solver with hybrid schemes.
Set up the no of iterations. Click on calculate option.
POST-PROCESSING RESULTS
STEPS
1.After the computation ends check for graphical visualization for the interation convergence rate, area weighted temperature and standard deviation plotat the outlet section.
2.Go to POST CFD and activate the inlet, outlet and wall section to view the geometry.
3.Select planes along and across the geometry of pipe at different cross section.
4.Highlight the planes with velocity and temperature variables.
5.Use line plot along and across the pipe and measyre the temperature and velocity variation in a chart.
The give section below demostrates the post processing results.
Results
1. Temperature Distribution along Length
2. Temperature Distribution along Cross Section
3. Temperature Vs Length Graph
4. Velocity Vs Length Graph
CONCLUSION
1.The k-e model is seen to be effective in capturing the physics of thermal and flow behaviour starting from entry to the outlet.
2.The mesh grid independency test shows the effect of meshing parameter on the average outlet temperature distribution.
3.The long tee is suitable for better mixing of fluid effectiveness and numerical prediction of temperature and velocity variation.
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