AIM: Mixing the effectiveness of tee under different circumstances in Ansys fluent.

AIM: Mixing the effectiveness of tee under different circumstances in Ansys fluent.

Methodology

• Basic of mixing tee
• turbulence modeling brief
• fluent steps.
• Required plots of cases
• Mesh independent study
• Effect of length and momentum
• analytical calculation of temperature and enthalpy
• Learning outcome

• what are mixing tee and its use!
• Tee joint is kind of tee shape having 2 outlets and 1 inlet(or 2 inlets and 1 outlet). it is used in various conditions under different circumstances, for instance, the oil and gas industry, automotive industry, pharma industry.
• to mixing the different fluid with different temperatures, to diversify the flow in another direction, tee joints are used.

In our case tee will have 2 inlets and 1 outlet. hot inlet temp is 36 degree and cold inlet is at 19 degrees.

• K-epsilon turbulence model:
• Its 2 equation model where k stands for the kinetic energy of turbulence and epsilon stands for the rate of dissipation of kinetic energy in turbulence.
• It is a low Reynolds number model, works well far from the wall.If a good initial guess is provided, it will be easy to converge faster. This model note down convection and diffusion of flow. It does perform well for complex geometry and external flows.
• K-omega sst model:
• It is 2 equation model where omega is for the rate of dissipation of kinetic energy in turbulence.K omega sst is an upgraded model of k epsilon and k omega model. It is a more nonlinear model than k epsilon, therefore convergence should take more iterations in compare to k epsilon model.
• This model works great with near walls.It Is great to capture the sub viscous layer effect.
• The selection of model depends on a specific problem, boundary conditions, wall functions, and y+.
• Here , the fluid is air.

• Case 1: short tee
• Short tee has an inlet velocity of 3 m/s, and the momentum ratio is 2, 4 therefore cold inlet velocity will be 6 &12m/s respectively

• SHORT TEE/K-ε model/momentum ratio 4

• The first step will be creating a new case study in Ansys fluent.
• Upload short tee geometry in space claim and extract out the volume for simulation purposes.

• The next step will be meshing, we are using tetrahedron mesh with default quality criteria.

• The quality criteria are in a given range, the next step is to define a boundary condition.

• There will be 2 inlets 1 outlet and the rest of it will be a wall.
• The next step is setting up physics for analysis purposes.

• Assuming the wall is adiabatic, and the flow is steady. we are concerned with temperature and velocity using the energy equation. we are using the k-ε realizable model.

• For the value of the boundary conditions at inlet x velocity 3m/s and temperature 36-degree Celcius
• At the inlet, y enter velocity 12 m/s and temperature 19-degree Celcius,
• Here we will generate a standard deviation of temperature and area-weighted avg of temperature.

• Initialize the process with a hybrid option. and run solution for 150 iteration.

Area weighted temperature graph

The standard deviation of temperature

• The solution is converged at 136 iteration
• Average outlet temp.

• Average enthalpy at outlet

Total cell count is 12583

• Temperature contour plots along the tee

• velocity contour plots along the tee

• Temperature contour plots across the tee

• velocity contour plots across the tee

• Temp linear plotting along and across tee

• velocity linear plotting along and across tee

• SHORT TEE/K-ε model/momentum ratio 2

For the rest of the cases, the procedure will be the same , here the post-processing results are shown

• Area weighted temperature graph

• standard deviation graph of temperature

Residuals

The solution is converged around 205th equation

• Average outlet temperature at the outlet

• Average enthalpy at outlet

• Temperature contour plots along with the tee

• velocity contour plots along with the tee

• velocity contour plots across the tee

• temperature contour plots across the tee

• velocity linear plotting along and across tee

• temp linear plotting along and across tee

line contour of velocity chart

• line contour of temp chart

case 3: short tee/k-w sst model /momentum ratio 4

• Area weighted temperature graph

• standard deviation graph of temperature

• Residuals

The solution is converged at 202th iteration

• Average outlet temperature

• Average enthalpy at outlet

• velocity contour plots along with the tee

• Temp.contour plots along with the tee

• velocity contour plots across with the tee

• Temp. contour plots across with the tee

• velocity linear plotting along and across the tee

• Temp. linear plotting along and across the tee

• line contour of velocity -chart

• line contour of temp. -chart

case 4: short tee/k-w sst model /momentum ratio 2

• Average outlet temperature

• Average enthalpy at outlet

• Area weighted temperature graph

• standard deviation graph of temperature

• Residuals

The solution is converged at 202th equation

• velocity contour plots along with the tee

• Temp. contour plots along with the tee

• Temp. contour plots across with the tee

• velocity. contour plots across with the tee

• Temp. linear plotting along and across the tee

• velocity. linear plotting along and across the tee

• line contour of temp. -chart

• line contour of velocity -chart

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• If we compare the standard deviation of the k-ε model and k-w sst model for the same momentum ratio, the standard deviation of the k-w sst model is more, therefore for this model, k-ε is more efficient.

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• For the long tee, we will compute for the k-ε model.

• long tee/k-ε model/momentum ratio 2

• standard deviation graph of temperature

• Area weighted temperature graph

• Residuals

Solution is converged at 204 equation.

• Average outlet temperature

• Average enthalpy at outlet

• Temp. contour plots along with the tee

• Velocity contour plots along with the tee

• Temp. contour plots across with the tee

• Velocity. contour plots across with the tee

• Temp. linear plotting along and across the tee

• line contour of temp chart

• line contour of velocity chart

• long tee/k-ε model/momentum ratio 4

• standard deviation graph of temperature

• Area weighted temperature graph

• Residuals

The solution is converged at 136th iteration.

• Outlet area-weighted Temp.

• Average enthalpy at outlet

• temp.contour plots along with the tee

• Velocity.contour plots along with the tee

• Velocity. contour plots across with the tee

• temp. contour plots across with the tee

• velocity linear plotting along and across the tee

• Temp linear plotting along and across the tee

• line contour of temp- chart

• line contour of velocity- chart

• Comparison of both cases in the table is given below

--------------------------------------------------------------------------------------------------------Mesh independent study

• Model: Long tee/K-ε/momentum ratio 4

Original element size:0.012m

Cell count : 15368

The solution is converged at 136 iterations

• New element size: 0.005m

The solution is converged at 127 iterations.

Cell count : 17771

• New element size: 1m

The solution is converged at 158 iterations.

Cell count : 2054

• From the experiment, we can conclude that the convergence takes lesser iteration to converge due to flux balancing.
• Coarser the mesh, more the diffusion for any property, as shown in the figure. The fine mesh will give you more accuracy as it is discretized in more parts.

• Effect of length
• From the table, we can conclude that the avg outlet temp is almost the same, therefore at the cost-cutting purpose and for faster manufacturing process short tee is recommended.
• Effect of momentum ratio
• By increasing the momentum ratio, we can decrease the temperature at outlet, It means at the cold inlet If the velocity will be increased, at the outlet temp will be decreased. We can change the diameter of the inlet and control the velocity.

comparing Results with analytical equations.

• Temperature calculation
• For momentum ratio 4

`m 1 = ρ . a . v 1 = 997 (k g) /m 3 . 0.0009 ( m 2 ) x 3 ( m/ s )`

`m 1 = 2.69 (k g )/s`

`m 2 = ρ . a . v 2 = 997 (k g) /m 3 . 0.0002 ( m 2 ) x 12 ( m/ s )``m2=2.71(kg)/s`

Use above formula and put values

mhot=2.69 kg/s , m cold 2.71kg/s

T hot=36 c , T cold=19 c

`T mixture=27.46 c`

• For momentum ratio 2

`m 2 = ρ . a . v 2 = 997 (k g) /m 3 . 0.0002 ( m 2 ) x 6 ( m/ s )`

`m2=1.35(kg)/s`

mhot=2.69kg/s , cold =1.35kg/s

thot=36 c t cold =19 c

`T mixture =30.31 c`

Both values are matching with our simulation.

• Enthalpy calculation
• For momentum ratio 4

  `m 1 = ρ . a . v 1 = 997 (k g) /m 3 . 0.0009 ( m 2 ) x 3 ( m/ s )`

  `m 1 = 2.69 (k g )/s`

  `m 2 = ρ . a . v 2 = 997 (k g) /m 3 . 0.0002 ( m 2 ) x 12 ( m/ s )``m2=2.71(kg)/s`

`h(hot)=m.Cp.delta t``h(hot)=1*1.005*(309.15-298.15)(T ref is 25 c)

h(hot)=11.055 (KJ)/Kg`

`h(cold)=m.Cp.delta t`

`h(cold)=1*1.005*(292.15-298.15)(T ref is 25 c)

h(cold)=-6.03 (KJ)/Kg`

Putting values in enthalpy equations

`h(mixture)=2.47(KJ)/kg`

`h(mixture)=2479(J)/kg`

• For momentum ratio 2

`m 2 = ρ . a . v 2 = 997 (k g) /m 3 . 0.0002 ( m 2 ) x 6 ( m/ s )`

`m2=1.35(kg)/s`

Putting values in enthalpy equations

`h(mixture)=5.344(KJ)/kg`

`h(mixture)=5344.05(J)/kg`

• Learning outcome
• simulation of mixing of fluid in a tee joint With some assumptions .
• produced velocity and temp contour and line graph.
• How mesh is directly affected to the accuracy.
• Temp and enthalpy calculation by formula.

Conclusion

• Here simulation of tee is done under given circumstances.