Week 6 - CHT Analysis on a Graphics card

Conjugate heat transfer analysis on a graphics card

• Problem statement:

Perform a steady-state conjugate heat transfer analysis on a model of a graphics card. You can use appropriate materials of your choice for the simulation. Make sure to properly define the correct solid and fluid zones. Refer to the video for further clarification and the model is provided below the video.

Run the simulation for the best possible mesh with a combination of coarse and refined mesh in different regions. Explain the reason for choosing the particular mesh settings.

• Objectives:

1. Run the simulation by varying the velocity from 1m/sec to 5m/sec for at least 3 velocities and discuss the results.
2. Find out the maximum temperature and heat transfer coefficient attained by the processor.
3. Prove that the simulation has achieved convergence with appropriate images and plots.
4. Identify potential hotspots 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 compression which in turn generates 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 modeling 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.

• Geometry setup:

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 setup:

1. Before generating a base mesh, let's name the different components of the graphic card so it is easier to find them while setting up fluent.
2. Base, processor, and fins are basic name selections that will help us in the simulation.
3. Click generate mesh to create a base mesh, body sizing can be applied for the 3 solid components.
4. after creating body sizing for the 3 components, generate mesh as that it is considered as a base mesh having more than 1 lakh elements which help us in finding accurate solutions.
5. After creating the mesh, exit Mesh setup to simulate in fluent.

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 a pressure-based steady-state solver
• Velocity formulation - Absolute

Model setup

• Energy
• Viscous - k-omega

Materials :

We are using different material for all the component

For fluid : Air

• Density = 1.225 kg/m^3

Viscosity = 1.7894e-05 kg/ms

for fins: copper

• Density = 8978 kg/m^3
• Specific heat = 381 j/kg-k
• Thermal conductivity = 387.6 w/m-k

For Processor : Silica

• Density = 2328 kg/m^3
• Specific heat = 710 j/kg-k
• Thermal conductivity = 150 w/m-k

For Base : steel

• Density = 8050 kg/m^3
• Specific heat = 502 j/kg-k
• Thermal conductivity = 16.27 w/m-k

Setting up the source terms :

For this case, we consider that our processor is a source term and it generating heat.

So, for setting up the source term go to Zones - Cell zones - and click on the processor for editing which is in the Solid.

After that select the material to gold and click on the source terms to enabled it and set the energy source to 1.

The calculation for the Energy source

Let the processor consume 6 W of power to work.

Dimension of the processor = 8*8*1 mm^3

So, the Energy produced by the processor is 93750000 w/m^3.

Inlet

• Air is used as a fluid
• Velocity = 1 m/s
• Temperature = 300 K

Outlet

• Pressure = Gauge pressure (0 pa)

• Results:

1. Base Mesh:

Inlet velocity= 3m/s

1. Mesh:

Main enclosure: 3.9mm

Fins size: 2mm

Processor: 1.5mm

Base: 2mm

base mesh images:

Results:

case1: inlet velocity 3m/s

bae mesh plots:

residuals:

local hotspots: global hot spots:

hotspots around the model back view : Global temperature contours:

wall heat transfer co-efficient: temperature streamlines:

vector plot:

case(2): plots for refined mesh:

velocity 3m/s

materials same as base mesh

Main enclosure: 3 mm

Fins size: 0.5 mm

Processor: 0.5 mm

Base: 1mm

mesh images and no of nodes and elements:

residuals:

local hotspots: Global hot spots:

hotspots around the model back view:

wall heat transfer co-efficient: temperature streamlines:

vector plot:

case3 :

velocity 1m/s

materials same as base mesh

Main enclosure: 3 mm

Fins size: 0.5 mm

Processor: 0.5 mm

Base: 1mm

local hotspots: Global hot spots:

hotspots around the processer: backside view:

global temperature plots: wall heat transfer coefficient:

streamlines:

case(4):

refined mesh with velocity 5 m/s:

materials same as base mesh

Main enclosure: 3 mm

Fins size: 0.5 mm

Processor: 0.5 mm

Base: 1mm

model mesh and no of nodes:

residuals:

local hotspots: Global hot spots:

hotspots around the processer: backside view:

global temperature plots: wall heat transfer coefficient:

streamlines: vector plot:

conclusion:

s.no	inlet velocity	max temperature attained in model
1	1 m/s	415K
2	3 m/s	358K
3	5 m/s	343K

A converged solution for the given sets of input was observed on the basis of repetitive plots in the Residual plot. The processor acted as a source of heat input and thus one of the hotspots for such a study

With an increase in velocity, we saw a considerable drop in temperature and a substantial rise in heat transfer coefficient which shows that high-velocity air can be an effective mode of heat dissipation in graphics card

From the temperature distribution, we can see hotspots in the fin and base near the surface that is in contact with the processor. Based on the thermal conductivity of the material this temperature is distributed accordingly amongst the component. We see a few high-temperature zones on fin away from processors depicting their high susceptibility to heat due to their geometry.

Also, the difference in baseline and refined mesh is considerable and thus we can conclude that more refined mesh provides a better solution and can be used in optimizing the design for such applications. From the residual plot, the change in the gradients is very small with which we can conclude that the solution has converged.

From the temperature plot of the coarse mesh, the high-temperature points are found at fins, processor, and base area near the processor. As the wall heat transfer coefficient is a wall phenomenon and fins dissipate a large amount of heat which results in higher heat transfer co-efficient but the coarse mesh heat transfer for fins and other components in the graphics card has the same values. Hence refinement of mesh is done for fins and base of the fin and base of the graphic card in order to calculate the wall heat transfer coefficient at the fins and base.

The high-temperature regions are found at the center of the fins, base, and processor walls. With further iterations, the temperature is distributed to other regions in the graphics card in the direction of the inlet airflow. Conduction occurs where there is a contact region between the solids in this case it is between processor and fins and processor and base. Convection occurs when the heat generated in the components of the graphic card is removed by the inlet airflow. The maximum temperature in the components is inversely proportional to the inlet airflow velocity. With the increase in the airflow velocity, the heat removal process by convection is faster. And also recirculation of air (vortex) at appropriate places will increase the heat transfer efficiencies hence greater amount of heat is removed by convection.