Week 6 - CHT Analysis on a Graphics card

                                              CHT ANALYSIS ON A GRAPHICS CARD

AIM: Perform a steady-state conjugate heat transfer analysis on a model of a graphics card. 

Objectives:-

• Import graphics card model in the space claim and active share topology.
• Mesh the model using proper inflation layer and face sizing.
• Open fluent setup and perform steady-state conjugated heat transfer simulation on meshed model.
• Prove that the simulation has achieved convergence with appropriate images and plots.
• Check the mesh independence of the model by performing the same simulation with different densities of mesh.
• Try simulating solution using different velocity between range 1m/s to 5m/s
• Find out the maximum temperature and heat transfer coefficient attained by the processor.
• Identify potential hot spots on the model.

Theory:

• Conjugate heat transfer:

The Conjugate Heat Transfer (CHT) analysis type allows for the simulation of heat transfer between solid and fluid domains by exchanging thermal energy at the interfaces between them. Typical applications of this analysis type exist as, but are not limited to, the simulation of heat exchangers, cooling of electronic equipment, and general-purpose cooling and heating systems.

Examples:

HVAC, Heat Exchanger, Tank, Internal Combustion Engine, Heat Sink, Boiler, Reactor, Heat Pipe, Turbocharger.

• Heat Transfer in a Solid - Conduction: Diffusion of heat due to temperature gradients in solids. A measure of the amount of conduction for a given gradient is heat conductivity. Conduction is described by Fourier’s law defining the conductive heat flux, q, proportional to the temperature gradient.
• Heat Transfer in a Fluid - Convection: When heat is carried away by moving fluid. The flow can either be caused by external influences, forced convection or by buoyancy forces, natural convection. 

Due to the fluid motion, there are three contributions to the heat transfer:

1. Convective contribution: Depending on the properties of the fluid and its region of flow, the domination of conduction and convection varies.

2. Viscous effects: Due to the fluid flow, the viscosity creates heat. This effect is often neglected due to its negligible value but should be taken into account for high-velocity cases.

3. Density effects: When the density becomes related to the temperature, the domain will have different densities at different locations based on the temperature distribution. This variation in density can cause the compression which in-turn generates the heat.

The contemporary conjugate convective heat transfer model was developed after computers 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. This model, based on a strictly mathematically stated problem, describes the heat transfer between a body and a fluid flowing over or inside it as a result of the interaction of two objects. The physical processes and solutions of the governing equations are considered separately for each object in two subdomains. Matching conditions for these solutions at the interface provide the distributions of temperature and heat flux along the body–flow interface, eliminating the need for a heat transfer coefficient. Moreover, it may be calculated using these data.

Applications:

Starting from simple examples in the 1960s, the conjugate heat transfer methods have become a more powerful tool for modelling and investigating natural phenomena and engineering systems in different areas ranging from aerospace and nuclear reactors to thermal goods treatment and food processing, from complex procedures in medicine to atmosphere/ocean thermal interaction in meteorology, and from relatively simple units to multistage, nonlinear processes. A detailed review of more than 100 examples of conjugate modelling 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.

SIMULATIONS AND PROCESS:

This project is divided into 2 parts based on the Mesh ( with a baseline and an upgraded mesh )

Again in Part 1, we run 3 different simulations with 3 different velocities 1m/s to 5m/s(using updraged mesh).

Case 1:

Geometry:

1. Use Spaceclaim to edit the geometry of the downloaded model of the graphics card which is in the enclosure.

2. Click on share prep to view any edges in interference so that conformal mesh can be achieved between different components.

3. Share topology has to be enabled between the different components so that necessary information can be shared between different zones of the mesh.

Mesh settings:

Global mesh setting of element size = 10 mm

1) PCB body sizing
element size = 1 mm
capture curvature - yes, 18 deg

2) Processor body sizing
element size = 1 mm
capture curvature - no

3) fins body sizing
element size = 1 mm
capture curvature - no

Global number of elements = 201920

Fluent setup:

Setting up the physics :

Setting up the physics for CHT analysis on a graphics card in ANSYS fluent.

• For this case we are using pressure-based steady state solver
• Velocity formulation - Absolute

Model setup

• Energy
• Viscous - k-omega

 

Inlet velocity = 2 m/s

Heat generation rate = 2.3437e7 W/m^3

Side walls --> symmetry

Number of iterations = 500

The solution converged around 325 iterations.

Results:

 Temperature contour - Global:

The max temperature reached by system is 53.175 Deg C. The PCB doesn't conduct the heat to a great extent so it remains around 33 Deg C. Also, there appears to be a local hotspot in centre of the fin base.

Temperature contour - Local:

It can be seen that fins at the front are cooler than the ones behind. This happens because the cold air first hits the front fins, it gets heated up and goes to next fin. Hence, the next fins can't give it enough heat as difference in temperature is lower in between solid and fluid.

 Temperature volume rendering:

 Temperature contour on a plane at y = 0

 Velocity contour:

The flow has a dead (wake) region behind the fins. It should be seen correctly through a vector plot.

 Velocity vector plot:

There are 3 recirculation regions fromed and their velocity is less than 0.3 m/s. So, it is important to look at flow over this region using streamlines.

Heat flux plot:

• The heat flux seems to be more from front end. This is due to higher temperature difference in solid and fluid. As the air moves forward, it takes less heat, so the region appears blue.
• The heat flux distribution over a fin is interesting. The corners and centres have a higher contact with air, so they give up more heat.

Wall heat transfer Coefficient (WHTC):

The distribution seems to averaged to a light blue region but there are local high spots at fin ends and processor slot. This error might be due to mesh at that location. To find average coefficient, we use function calculator:

Reason for the high heat transfer coefficient:

A good mesh is said to have element quality near 1. Here, the fins and fin ends have quality < 0.4 and in some places <0.2. This must have caused the high WHTC values.

Also, a good mesh must have skewness near to 0 and must have average skewness around 0.33.

In current case, average is near to 0.5 and fins have higher than 0.6. In fin ends the skewness is near to 0.9. So the mesh needs to be more fine.

Reasons to refine the mesh:

• The recirculation region is not clearly visible.
• The WHTC has high values at some locations.
• The element quality and skewness is not good.

Case 2:

Mesh:

Global mesh setting of element size = 5 mm

1) PCB body sizing
element size = 1 mm
capture curvature - yes, 18 deg

2) Proc body sizing
element size = 0.5 mm
capture curvature - no

3) Fins body sizing
element size = 0.5 mm
capture curvature - no

Global number of elements = 422289

 Results:

 Residuals:

The simulation appears converged near 375 iterations.

 Temperature plots:

Velocity Plots:

 Heat flux:

 Wall heat transfer coefficient:

The Average value is increased owing to factors like mesh quality, skewness, Orthogonality.

Even though the mesh is refined, the zone still has low quality. This means some other technique is to be used to remove such issues with quality.

Reason to further refine the mesh:

• The mesh quality, skewness, etc are not upto the mark.

Case 3:

Geometry:

In order to refine the mesh and keep the element numbers to a minimum, an enclosure is made surrounding the graphics card. An important step while doing this is selecting all three bodies(fins, processor, PCB) and then applying enclosure feature in ANSYS Fluent. The outer enclosure is deleted and then created again by selecting inner enclosure. The above given image gives information of dimensions needed to make the enclosures. The dimensions are used based on the lengths of volume surrounding graphics card where there is a gradient in temperature.

 Meshing:

Global controls:
     Element size = 5 mm
     Capture proximity = yes
     Proximity size = 0.1 mm

Element number = 1852356

The pinch control is applied on the edge where the WHTC increases suddenly. It acts like a virtual topology. Even after we apply pinch control, the geometry doesn't change but the mesh changes. The reason for using this:

The gap between fin and base's side is 0.1 mm. In order to resolve the length that small using element size, we would need element size = 0.05 mm. Doing so would increase the element number greater than 10 million. Other option is to locally change the size using sphere of influence method, but after doing so the high WHTC phenomenon spread to whole region of influence (tried before). So it became important to ignore the gap.

Ignoring the gap might seem a bit wrong but it isn't because the temperature, temperature gradient, heat flux, velocity plots were uniform in that region without even using pinch control.

For verifying whether the pinch control works correct, we have applied pinch control only on the top surface. The bottom surface has some gaps along the edges where the processor fits. There no pinch control is applied.

 Results:

Residuals:

The solution converged at around 425 iterations.

Temperature plots:

Here we can observe 5 distinct recirculation zones with their temperature. The largest one has temperature of 40 Deg C.

 Velocity plots:

Wall Heat Transfer Coefficient:

As expected, the pinch control worked and we got uniformity on top surface but the bottom surface didn't have a smooth transition.

Final Comparison:

The trend can be observed using values from the table:

Sr No.       Max. Velocity   Max. tEMP.   Max.Wall Heat Transfer          Average wall Heat Transfet                   Number Of elements

Case 1           2.875             53.17                  331.65                                      133.98                                  201920

Case 2           2.522             52.01                  468.25                                      256.82                                  422289

Case 3           2.368             52.29                  714.44                                      351.97                                  1852356

 CONCLUSION:

• As the number of elements increases the accurate results of temperature distribution is obtained.
• From the above table, it is fairly notable that, the net temperature of components of graphics cards decreases with an increase in inlet velocity.
• Hence it can be inferred that a higher inlet velocity results in better cooling and lower the average temperature thus keeping the components safe from damage.
• Furthermore, it can be observed that the temperature of components increases as the inlet velocity decreases from 5 m/s to 1m/s, also proving that higher velocity results in better cooling.