Week 4 - CHT Analysis on Exhaust port

1. Give a brief description of why and where a CHT analysis is used.
2. Maintain the y+ value according to the turbulence model and justify the results.
3. Calculate the wall/surface heat transfer coefficient on the internal solid surface & show the velocity & temperature contours in appropriate areas.
4. How would you verify if the HTC predictions from the simulations are right? On what factors does the accuracy of the prediction depend on?

1. What is Conjugate heat transfer (CHT):

The Conjugate Heat Transfer (CHT) is the analysis type which allows for the simulation of heat transfer between solid and fluid domains by exchanging thermal energy at the interfaces between them. Main applications of this analysis is cooling of electronic equipment, and general-purpose cooling and heating systems.

The main contemporary conjugate convective heat transfer came into existance when computer was developed and came into wide use in order to substitute the empirical relation of proportionality of heat flux to temperature difference with heat transfer coefficient which was the only tool in theoretical heat convection since the times of Newton.

2. Why CHT analysis is used:

Mainly in the Computational Fluid Dynamics (CFD) and more specifically conjugate heat transfer (CHT) analysis can accurately predict heat transfer by simultaneously solving all the relevant solid and flow field heat transfer processes.

Well CHT approach has an advantage over FE thermal analyses in that wall heat transfer coefficients and their local variations on surfaces are directly calculated within the model rather than based on simplified empirical calculations.

3. Where is CHT analysis used:

the conjugate heat transfer methodsare more powerful tool for modeling and investigating natural phenomena and engineering systems in different phases or ares from aerospace a to thermal goods treatment and food processing, from complex procedures in medicine to atmosphere/ocean thermal interaction in meteorology.

A detailed review of more than 100 examples of conjugate modeling selected from a list of 200 early and modern publications shows that conjugate methods are now used extensively in a wide range of applications. That also is confirmed by numerous results published after this book appearance (2009) that one may see, for example, at the Web of Science. The applications in specific areas of conjugate heat transfer at periodic boundary conditions and in exchanger ducts are considered in two recent books.

Applications of the CHT:

• Effective heat transfer:

Efficiently combining heat transfer in fluids and solids is the key to designing effective coolers, heaters, or heat exchangers.

The fluid usually plays the role of energy carrier on large distances. Forced convection is the most common way to achieve a high heat transfer rate. In some applications, the performances are further improved by combining convection with phase change (for example liquid water to vapor phase change).

• Energy savings:

WELL THE Heat transfer in fluids and solids can also be combined to REDUCES the heat losses in various devices. Because most gases (especially at low pressure) have small thermal conductivities, they can be used as thermal insulators… provided they are not in motion. In many situations, gas is preferred to other materials due to its low weight. In any case, it is important to limit the heat transfer by convection, in particular by reducing the natural convection effects. Judicious positioning of walls and the use of small cavities helps to control the natural convection.

4. Heat transfer co-efficient:

The heat transfer coefficient or film coefficient, or film effectiveness, in thermodynamics and in mechanics is the proportionality constant between the heat flux and the thermodynamic driving force for the flow of heat (i.e., the temperature difference, ΔT):

The overall heat transfer rate for combined modes is usually expressed in terms of an overall conductance or heat transfer coefficient, U. In that case, the heat transfer rate is:

where:

: the surface area where the heat transfer takes place, m²

: temperature of the surrounding fluid, K

: temperature of the solid surface, K.

The general definition of the heat transfer coefficient is:

where:

q: heat flux, W/m²; i.e., thermal power per unit area, q = dQ'/dA

h: heat transfer coefficient, W/(m²•K)

ΔT: difference in temperature between the solid surface and surrounding fluid area, K

It is used in calculating the heat transfer, typically by convection or phase transition between a fluid and a solid. The heat transfer coefficient has SI units in watts per squared meter kelvin: W/(m²K).

The heat transfer coefficient is the reciprocal of thermal insurance. This is used for building materials (R-value) and for clothing insulation.

There are numerous methods for calculating the heat transfer coefficient in different heat transfer modes, different fluids, flow regimes, and under different thermohydraulic conditions. Often it can be estimated by dividing the thermal conductivity of the convection fluid by a length scale. The heat transfer coefficient is often calculated from the Nusselt number (a dimensionless number). There are also online calculators available specifically for Heat-transfer fluid applications. Experimental assessment of the heat transfer coefficient poses some challenges especially when small fluxes are to be measured.

Geometry setup for this challenge:

• Load the model into the SpaceClaim and change the units to meters remove the duplicate edges in the model
• Then extract the volume in the model and name the inner volume as fluid and outer volume as solid volume and remove the empty components

• Enable Share topology of the main component so that the mesh and physics information can be shared and conformal mesh can be achieved.

• Lastly, to achieve conjugate heat transfer between the fluid and wall we have to choose share prep under the workbench tool so that the heat transfer is captured throughout the wall from inlet to outlet.

  

Mesh setup for yhis challenge:

• After the geometry setup is done in the SpaceClaim open the mesh parameter. select the faces and name them accordingly
• Create a name selection for fluid volume so that the inflation layer can be achieved on that body since the conjugate heat transfer is between the fluid volume to the solid wall.
• Create a body sizing on the outer wall convection region to achieve mesh refinement and accurate results can be captured at such regions.

Fluent setup for this challenge:

• After updating the mesh open fluent setup, select a pressure-based solver using the steady-state time simulation and absolute velocity formulation under the general settings.
• Under physics choose the energy option since we are dealing with temperature properties.
• Input the Boundary values at the inlet, 5 m/s as inlet velocity at 700k temperature. Set the outer wall convection boundary to convection type and input a constant value for heat transfer coefficient as 20
• Under materials, properties choose air as the fluid material and aluminum as the solid material
• Enter the reference values as per the geometry and inlet boundary conditions accordingly.
• Initialize by using the hybrid initialization, and the simulations for 400 iterations as it is optimum to achieve a converged solution.

CFD post:

• Then calculate the wall heat transfer coefficient by inserting a plane along xy plane
• Similarly, Vector and streamline contours can be created by using the insert vector or streamline option.

Task 1: Baseline simulation

element size =150mm

sizing element size=50mm

no. of elements=247571

viscous model=k-epsilon

inlet velocity =5m/s

inlet temperature=700k

HT co-efficient=20W/m^2-k

Mesh details:

Temparature distribution for this challenge:

Residual plot

surface heat transfer co-efficient

wall heat transfer coefficient

Temperature and velocity contours

velocity streamlined plot

Task 2 Refined mesh

element size =100mm

sizing element size=20mm

no. of elements=336493

viscous model=k-epsilon

inlet velocity =5m/s

inlet temperature=700k

HT co-efficient=20W/m^2-k

Mesh details:

Temparature distribution:

Residual plot

surface heat transfer co-efficient

wall heat transfer coefficient

Temperature and velocity contours

velocity streamlined plot

Task 3 Refined mesh

element size =100mm

sizing element size=20mm

no. of elements=362257

viscous model=k-omega

inlet velocity =5m/s

inlet temperature=700k

HT co-efficient=20W/m^2-k

Temparature distribution:

Residual plot

surface heat transfer co-efficient

wall heat transfer coefficient

Temperature and velocity contours

velocity streamlined plot

conclusion:

cases	viscous model	no. of elements	surface HT coefficient in(W/m^2-K)	wall HT coefficient in(W/m^2-K)
1	k-epsilon	247571	31.674	211
2	k-epsilon(refined)	336493	28.58	191.9
3	k-omega	362257	27.20	245.8

By solving the 3 cases using the k omega model we mostly can get more accurate because we are dealing with temperature

The Wall heat co-efficient is more in the k-omega and there is no change in the temperature for all cases.

Y plus should be calculated depending upon the solvers.

How would you verify if the HTC predictions from the simulations are right? On what factors does the accuracy of the prediction depend on?

According to Bernoulli's mass conservation principle mass is conserved in our model which is clearly explained below.

As mass is neither created nor destroyed,

Q1+Q2+Q3+Q4=Q5Q1+Q2+Q3+Q4=Q5 -------(1)

we have Q=ρ.A.VQ=ρ.A.V -------(2)

now the equation 1 becomes

A1.V1+A2.V2+A3.V3+A4.V4==A5.V5

as it is an incompressible flow to compensate for the mass flow, velocity is increased, and the overall mass flow rate is conserved.

As velocities are high, the Reynolds number at the outlet manifold will be high.

Nu=f(Re, pr) for forced convection applications,

Nu=hD/k

from the above equations

Nu∝Re,

Nu∝h,

hence, Re∝h

From our simulations, we have observed velocities are high at bend section of outlet manifold hence higher Reynolds number which has high Heat transfer coefficients.

Therefore predictions from our simulations can be considered correct.

Factors influence accuracy of prediction:

• Mesh quality greatly impacts the accuracy of predicted value from our simulations which can be observed from the above contours and maximum values of related parameters like velocity and heat transfer coefficient.
• Appropriate y+ value helps in calculating total layer thickness from the wall for the better capturing of effects near walls, which is HTC in our case.
• An appropriate turbulence model has to be used in accordance with the y+ value which is very important
• Appropriate solvers are one of the most important factors which lead all the calculations in solving the equations
• Inflation layers and boundary conditions