DESIGN PROJECT ON THERMAL ANALYSIS OF HEAT SINK

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ABSTRACT

In this paper, we compare thermal performances of two types of heat sinks commonly used in the electronic equipment industry: plate-fin and pin-fin heat sinks. In particular, heat sinks subject to an impingingflow are considered. For comparison of the heat sinks, experimental investigations are performed for various flow rates and channel widths. From experimental data, we suggest a model based on the volumeaveraging approach for predicting the pressure drop and the thermal resistance. By using the model, thermal resistances of the optimized plate-fin and pin-fin heat sinks are compared. Finally, a contour map,which depicts the ratio of the thermal resistances of the optimized plate-fin and pin-fin heat sinks as a function of dimensionless pumping power and dimensionless length, is presented. The contour map indicatesthat optimized pin-fin heat sinks possess lower thermal resistances than optimized plate-fin heat sinks when dimensionless pumping power is small and the dimensionless length of heat sinks is large.On the contrary, the optimized plate-fin heat sinks have smaller thermal resistances when dimensionless pumping power is large and the dimensionless length of heat sinks is small.

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CHAPTER NO TABLE OF CONTENTS PAGE NO

ABSTRACT LIST OF ABBRIVATIONS

LIST OF FIGURES

1 INTRODUCTION 6 2 OBJECTIVE 7

3 INNOVATION IN WORK 8

4 LITERATURE SURVEY 9

5 DATA COLLECTION 11

5.1 COMPUTATIONAL FLUID DYNAMICS 11 5.1.1 ADVANTAGES 12 5.1.2 APPLICATIONS 12 5.2 NTU 13

6 FIN 15

6.1 FIN INTRODUCTION 15

6.2 PLATE FIN HEAT SINK 16

6.3 PIN FIN HEAT SINK 16 7 PROBLEM DEFINITION 18

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7.1 PROBLEM DEFINITION 18 7.2 PRE-PROCESSOR 19 7.3 SOLVER 20 7.3.1 FINITE DIFFERENT METHOD 21 7.3.2 FINITE VOLUME METHOD 21 7.3.3 FINITE ELEMENT METHOD 21 7.4 POST PROCESSOR 22 8 INITIAL CAD DESIGN USING SOLID WORKS 24

9 THERMAL ANALYSIS OF CFD USING ANSYS 25 10 ANSYS SIMULATION 26 10.1 PLATE FIN APPARATUS 26 11 ANSYS SIMULATION 27 11.1 PIN FIN APPARATUS 27 12 FORMULA AND CALCULATION 29

13 ANSYS SIMULATION ON EACH FIN 31

14 RESULT AND FINAL DISCUSSION 45

15 CONCLUSION 46

16 REFERENCE 47

LIST OF ABBRIVATIONS

CFD COMPITATIONAL FLUID DYNAMICS

NTU NUMBER OF TRANSFER UNIT

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LIST OF FIGURES PAGE NO

1 PLATE FIN HEAT SINK 16

2 PIN FIN HEAT SINK 17

3 INITIAL CAD DESIGN USING SOLID WORKS 3.1 PLATE FIN 24 3.2 PIN FIN 24

4 ANSYS SIMULATION 4.1 PLATE FIN 26 4.2 PIN FIN 27

5 ANSYS SIMULATION ON EACH FIN 29-44

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CHAPTER 1

INTRODUCTION

A heat sink is a passive heat exchanger component that cools a device by dissipating heat into the surrounding air.

Used in electronic systems wherever the heat dissipation ability of basic device package is insufficient to control its temperature.

In this project we will be looking in detail about the temperature distribution in a heat fin which is produced by an electronic circuit to which the fin is attached.

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CHAPTER 2

OBJECTIVE

The main objectives of this project are –

To determine the nodal temperature in the component.

To determine the maximum value of temperature in the component.

To come up with better fin arrangement comparing the plate fin and pin fin.

The project is to be done with the help of ANSYS software which is available in CAD lab.

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CHAPTER 3

INNOVATION IN THE WORK

AIM- improves heat dissipating capacity of heat sink without changing its available area.

After analysis, an appropriate and suitable material to be suggested for the manufacturing of heat sinks.

Also the arrangement and shape of the fins in the heat sinks to be altered.

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CHAPTER 4

LITERATURE SURVEY

Wuhan Yuan (2012) has analyse the factors of the computational fluid dynamic software FLUENT is used in assessing the electronics cooling potential of a plate pin fin heat sink (PPFHS), including the conjugate effect. The simulation results are validated with reported experimental data. The simulation shows that pin height and air velocity have significant influences on the thermal hydraulic performances of PPFHS while the influences of in-line/staggered Array and neighbour pin flow-directional center distance (NPFDCD) of the PPFHS are less notable. In applying the present design to the cooling of a desktop PC CPU at a heat flux of 2.20 W/cm2, the temperature can be kept at less than 358 K with an air velocity over 6.5 m/s.

Xiaoling Yu (2004) has conducted an experimental study based on plate fin heat sinks (PFHSs), a new type of plate-pin fin heat sink (PPFHS) is constructed, which is composed of a PFHS and some columnar pins staggered between plate fins. Numerical simulations and some experiments were performed to compare thermal performances of these two types of heat sinks. The simulation results showed that thermal resistance of a PPFHS was about 30% lower than that of a

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PFHS used to construct the PPFHS under the condition of equal wind velocity. Another obvious advantage of PPFHSs is that users can change an existing unsuitable PFHS into a required PPFHS by themselves to achieve better air-cooling results

Hung-Yi Li (2009) His investigation assesses the performance of plate-fin heat sinks in a cross flow. The effects of the Reynolds number of the cooling air the fin height and the fin width on the thermal resistance and the pressure drop of heat sinks are considered. Experimental results indicate that increasing the Reynolds number can reduce the thermal resistance of the heat sink. However, the reduction of the thermal resistance tends to become smaller as the Reynolds number increases. Additionally, enhancement of heat transfer by the heat sink is limited when the Reynolds number reaches a particular value. Therefore, a preferred Reynolds number can be chosen to reduce the pumping power. For a given fin width, the thermal performance of the heat sink with the highest fins exceeds that of the others, because the former has the largest heat transfer area. For a given fin height, the optimal fin width in terms of thermal performance increases with Reynolds number. As the fins become wider, the flow passages in the heat sink become constricted .As the fins become narrower, the heat transfer area of the heat sink declines. Both conditions reduce the heat transfer of the heat sink. Furthermore, different fin widths are required at different Reynolds numbers to minimize the thermal resistance.

Dong-Kwon Kim (2009) compares thermal performances of two types of heat sinks commonly used in the electronic equipment industry: plate-fin and pin-fin heat sinks. In particular, heat sinks subject to an impinging flow are considered. For comparison of the heat sinks, experimental investigations are performed for various flow rates and channel widths. From experimental data, we suggest a model based on the volume averaging approach for predicting the pressure drop and the thermal resistance. By using the model, thermal resistances of the optimized plate-fin and pin-fin heat sinks are compared. Finally, a contour map, which depicts the ratio of the thermal resistances of the optimized plate-fin and pin-fin heat sinks as a function of dimensionless pumping power and dimensionless length, is presented. The contour map indicates that optimized pin-fin heat sinks possess lower thermal resistances than optimized plate-fin heat sinks when dimensionless pumping power is small and the dimensionless length

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of heat sinks is large .On the contrary, the optimized plate-fin heat sinks have smaller thermal resistances when dimensionless pumping power is large and the dimensionless length of heat sinks is small.

CHAPTER 5

5.1 Computational Fluid Dynamics :

Computational Fluid Dynamics or CFD as it is popularly known is used

to generate flow simulations with the help of computers. CFD involves the

solution of the governing laws of fluid dynamics numerically. The complex sets

of partial differential equations are solved on in geometrical domain divided into

small volumes, commonly known as a mesh (or grid).

CFD enables analysts to simulate and understand fluid flows without the

help of instruments for measuring various flow variables at desired locations.

The development CFD analysis leads to a considerable reduction of

investment and operating costs by optimized design, by an increased availability

of the system. This can be achieved by an appropriate design of the manifold.

CFD analysis helps to evaluate and avoid velocity and temperature peaks in

manifold sections which are of special relevance regarding material stress and

deposit formation. CFD analysis helps to predict the temperature, velocity and

pressure distribution in the system.

Traditionally this has provided a cost effective alternative to full scale

measurement. However, in the design of equipment that depends critically on

the flow behavior, for example the aerodynamic design of an aircraft, full scale

measurement as part of the design process is economically impractical. This

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situation has led to an increasing interest in the development of a numerical

wind tunnel.

The development of more powerful computers has furthered the advances

being made in the field of computational fluid dynamics. Consequently CFD is

now the preferred means of testing alternative designs in many engineering

companies before final, if any, experimental testing takes place.

5.1.1 ADVANTAGES :

> CFD allows numerical simulation of fluid flows, results for which are

available for study even after the analysis is over. This is a big advantage

over, say, wind tunnel testing where analysts have a shorter duration to

perform flow measurements.

> CFD allows observation of flow properties without disturbing the flow

itself, which is not always possible with conventional measuring

instruments.

> CFD allows observation of flow properties at locations which may not be

accessible to (or harmful for) measuring instruments. For example, inside

a combustion chamber, or between turbine blades.

> CFD can be used as a qualitative tool for discarding (or narrowing down

the choices between), various designs. Designers and analysts can study

prototypes numerically, and then test by experimentation only those which

show promise.

5.1.2 Applications:

Biomedical:

Flow modeling with computational fluid dynamics (CFD) software lets

you visualize and predict physical phenomena related to the flow of any

substance. It is widely used in medical, pharmaceutical, and biomedical

applications to analyze.

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Electronics:

Ansys provides a full spectrum of problem solving products for the

electronics industry.

The Ansys flagship CFD software, FLUENT, as well as the electronics

industry custom-designed Icepak suite, offer high-performance electronics

cooling solutions covering a wide range of real life problems on any level.

Industrial:

To meet the vast fluid flow modeling needs of a broad spectrum of

industries around the world, Fluent has been at the forefront of developing and

driving computational fluid dynamics (CFD) for more than two decades.

Diverse modeling capabilities allow Fluent's software products to tackle

problems from most major industry sectors.

Environmental :

Protecting and improving the quality of our environment today requires

innovative design solutions that establish compliance with ever-expanding and

more stringent regulations. Flow modeling with Fluent's computational fluid

dynamics (CFD) software helps you tackle your environmental flow problems in

the most efficient and cost-effective way.

Civil :

Within the built environment, it is critical to assess a number of important

building characteristics at the design stage, including the ability to improve the

energy efficiency of a building, quantify solar radiation effects, analyze wind

flow effects, study possible fire and smoke hazard scenarios, and predict

occupant comfort.

5.2 NTU :

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The NTU, or Number of Transfer Units, is a dimensionless parameter that relates the heat transfer convective resistances to the coolant flow heat capacity. While the details are beyond the scope of this short column, a typical heat sink (or cold plate) can be described with the following equations (assuming that the simplifying assumption of one surface temperature is reasonable).

1 Actual heat transfer = Ccool*(Tcool-out – Tcool-in);

  (Ccool is the mass flow times the heat capacity for the coolant)

2 Maximum possible heat transfer = Ccool*(Tsurf – Tcool-in)

3 Effectiveness = E = (Tcool-out – Tcool-in)/ (Tsurf – Tcool-in)

4 Effectiveness = 1 – exp (-NTU), where NTU = hA/Ccool

One way to increase the NTU term is to decrease the coolant heat capacity but while our effectiveness increased, the resulting temperature for the surface may not be acceptable. The other way is to increase the hA term which means either larger area or a higher effective heat transfer coefficient. The engineering challenge is to minimize the decrease in effectiveness as coolant flow rates increase. Note that the limit is when the surface temperature and the coolant exit are at the same temperature,

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CHAPTER 6

FIN

6.1 FIN INTRODUCTION

A fin is a surface used to produce lift and thrust or to steer while traveling

in water, air, or other fluid media. Fins improve heat transfer in two ways. One

way is by creating turbulent flow through fin geometry, which reduces the

thermal resistance through the nearly stagnant film that forms when a fluid

flows parallel to a solid surface. A second way is by increasing the fin density,

which increases the heat transfer area that comes in contact with the fluid.

The plate fin heat exchanger is one of the most efficient designs used to

transfer heat from one fluid to another. The best way to increase the efficiency

of one of these devices is to alter the inner fin layout of the design. The fins are

very important to the overall design because they add a large amount of surface

area of contact between the fluid and the plate, which supplies heat. Altering the

fin's geometry and other traits can drastically change the way a heat exchanger

performs. Changing certain properties of a fin can have diminishing returns

while others can have very beneficial effects. Effective ways to change a fin

would be to change its height, length, the angle at which it is oriented with

respect to the flow, and the amount of fins per unit length.

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The fin angle is also an important factor to consider. Introducing different

fin angles will create more vortices in the flow but will also create a lot more

turbulence.

Vortices are good to have because the mixing swirling fluid is more efficient at

transferring heat than a laminar boundary region attached to the surface. As the

fluid hits the fin and passes over it, the wake region behind the fin sees almost

no fluid velocity or mixing and has a large pressure drop.

6.2 PLATE FIN HEAT SINK :

The problem under consideration is an impinging flow througha plate-fin heat sink as shown in Fig. 1(a) and (b). The bottom surface is kept constant at a high temperature. Air impinges on the heat sink along the y-axis and then flows parallel to x-axis .Air, employed as a coolant, passes through the heat sink, thus removing the heat generated by the component attached at thebottom of the heat sink substrate. In analyzing this problem, the flow is assumed to be steady and laminar. In addition, all thermal properties are evaluated at the film temperature of fluid. In addition, it is assumed that the aspect ratio of the channel is higher than 1 and the solid conductivity is higher than the fluid conductivity

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6.3 PIN FIN HEAT SINK : The fluid flow in the pin-fin heat sink is axisymmetric in naturerather than two-dimensional. In the present study, the pin-fin heatsink is modeled as an equivalent porous cylinder which has thesame porosity, wetted surface, and base area. The top view forthe equivalent porous cylinder is shown in Fig. 2(b).

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CHAPTER 7

7.1 PROBLEM DESCRIPTION:

In early days the rectangular ducts are made up with straight array of

plates. This type of rectangular duct has low surface conduct with air flowing

inside the rectangular duct. This results in the reduced total pressure drop across

the rectangular duct and also reduced heat transfer rate. Due to straight array of

plates air passing in the straight direction there is no mixing between the airs

flowing across the rectangular duct and there is no secondary flow formation.

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CFD codes are structured around the numerical algorithms that can tackle

fluid flow problems. In order to provide easy access to their solving power all

commercial CFD packages include sophisticated user interfaces to input

problem parameters and to examine the results. Hence all codes contain three

main elements.

> Pre-processor

> Solver

> Post-processor

We briefly examine the function of each of these elements within the

context of a CFD code.

7.2 PRE-PROCESSOR:

'Pre processing consists of the input of a flow problem by means of an

operator friendly interface and the subsequent transformation of this input into a

form suitable for use by the solver. The user activity at the pre-processing stage

involves,

> Definition of the geometry of the region of the interest: the

computational domain.

> Grid generation: the sub-division of the domain into a number of

the smaller, non-overlapping sub-domains: a grid (or mesh) of cells

(or control volumes or elements).

> Selection of the physical and chemical phenomena that need to be

modeled.

> Definition of fluid properties.

> Specification of appropriate boundary conditions at cells, which

coincide with or touch the domain boundary.

The solution to a flow problem (velocity, pressure, temperature etc) is

defined at nodes inside each cell. The accuracy of a CFD solution is governed

by the number of cells the better the solution accuracy. Both the accuracy of a

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solution and its cost in terms of necessary computer hardware and calculation

time are dependent on the fineness of the grid. Optimal meshes are often non-

uniform: finer in areas where large variations occur from point and coarser in

regions with relatively little change. Efforts are under way to develop CFD

codes with an adaptive meshing capability. Ultimately such programs will

automatically refine the grid in area of rapid variations. A substantial amount of

basic development work still needs to be done before these techniques are

robust enough to be incorporated into commercial CFD codes. At present it is

still up to the skills of the CFD user to design a grid that is a suitable

compromise between desired accuracy and solution cost.

7.3 SOLVER:

There are three distinct streams of numerical solution techniques: finite

difference, finite element and spectral methods. In outline the numerical

methods that form the basis of the solver perform the following steps,

> Approximation of the unknown flow variables by means of simple

functions.

> Diseretizations by substitution of the approximations into the

governing flow equations and subsequent mathematical

manipulations.

> Solution of the algebraic equations.

The main differences between the three separate streams are associated

with the way in which the flow variables are approximated and with the

diseretizations processes.

7.3.1 Finite Difference Method:

Finite difference methods describe the unknown's of the flow problem by

means of point samples at the node points of a grid co-ordinate lines. Truncated

Taylor series expansions are often used to generate finite difference

approximations of derivatives of F in terms of the point samples O at each grid

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point and its immediate neighbors. Those derivatives appearing in the governing

equations are replaced by finite differences yielding an algebraic equation for

the values of at each grid point.

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7.3.2 Finite Element Method:

Finite element methods use simple piecewise functions (e.g. linear or

quadratic) valid on elements to describe the local variations of unknown flow

variables. The governing equation is precisely satisfied by the exact solution. If

the piecewise approximating functions for are minimized in some sense by

multiplying them by equations for the unknown coefficients of the

approximating functions. The theory of finite elements has developed initially

for structural analys

7.3.3 Finite Volume Method:

The finite volume method was originally developed as a special finite

difference formulation. It is central to four of the five main commercially

available CFD codes: PHOENICS, FLUENT, FLOW3D and STAR-CD. The

numerical algorithm consists of the following steps,

> Formal integration of the governing equations of fluid flow over all

the (finite) control volumes of the solution domain.

> Diseretizations involves the substitution of a variety of finite

difference type approximations for the terms in the integrated

equation representing flow processes such as convection, diffusion

and sources. This converts the integral equations into a system of

algebraic equations.

> Solution of the algebraic equations by an iterative method.

The first step, the control volume integration, distinguishes the finite

volume method from all other techniques. The resulting statements express the

(exact) conversation of relevant properties for each finite size cell. This clear

relationship between the numerical algorithm and the underlying physical

conservation principle forms one of the main attractions of the finite volume

method and makes its concepts much simple to understand by engineers than

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finite volume method and spectral methods. The conservation of a general flow

variable, for example a velocity component or enthalpy, within a finite control

volume can be expressed as a balance between the various processes tending to

increase or decrease it.

CFD codes contain diseretizations techniques suitable for the treatment

of the key transport phenomena, convection (transport due to fluid flow) and

diffusion (transport due to variations of O from point to point) as well as for the

source terms (associated with the creation of destruction of O) and the rate of

change with respect to time. The underlying physical phenomena are complex

and non-linear so an iterative solution approach is required. The most popular

solution procedures are the TDMA line-by-line solver of the algebraic

equations and the simple algorithm to ensure correct linkage between pressure

and velocity.

7.4 POST PROCESSOR:

As in pre-processing a huge amount of development work has recently

taken place in the post-processing field. Owing to the increased popularity of

engineering workstations, many of which have outstanding graphics

capabilities, the leading CFD packages are now equipped with versatile data

visualization tools. These include,

> Domain geometry and grid display

> Vector plots

> Line and shaded contour plots

> 2D and 3D surface plots

> Particle tracking

> View manipulation (translation, rotation, scaling etc)

> Color postscript output

More recently these facilities may also include animation for dynamic

result display and in addition to graphics all codes produce trusty alphanumeric

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output and have data export facilities for further manipulation external to the

code. As in many other branches of CAE the graphics output capabilities of

CFD codes have revolutionized the communication of ideas to non-specialist

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CHAPTER 8

INITIAL CAD DESIGN USING SOLID WORKS

PLATE FIN :

PIN FIN :

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CHAPTER 9

THERMAL ANLYSIS OF CFD USING ANSYS

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CHAPTER 10

ANSYS SIMULATION

PLATE FIN ARRANGEMENT :

FRONT VIEW - TEMPERATURE DISTRIBUTION

ISOMETRIC VIEW – TEMPERATURE DISTRIBUTION

ANSYS SIMULATION – MODIFIED

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CHAPTER 11

ANSYS SIMULATION

PIN FIN ARRANGEMENT

FRONT VIEW - TEMPERATURE DISTRIBUTION

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ANSYS SIMULATION – MODIFIED DESIGN

ISOMETRIC VIEW – TEMPERATURE DISTRIBUTION

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CHAPTER 12

FORMULAS AND CALCULATION :

θl=(Tl-T∞)/(Tb- T∞)

θl=Temperature distribution at specified end temperature

Tl= Specified end temperature

Tb=temperature of the base

T∞=ambient temperature

 

For plate fin

θl=(84-20)/(100-20)=0.8

For pin fin

θ l=(78-20)/(100-20)=0.725

FORMULAES FOR THEORETICAL CALCULATIONS

Q=hA∆T

Q à heat dissipation rate W

h à heat convection coefficient W/m2K

A à area of fins m2

∆T à temperature difference K

For Plate Fin

A = 0.0106 m2

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h = 50 W/m2K

∆T = 100-84 = 16 K

Q = 50*0.0111*16=8.9 W

For Pin Fin

A = 0.0106 m2

h = 50 W/m2K

∆T = 100-78 = 22 K

Q=50*0.0106*22=11.66 W

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CHAPTER 13

ANSYS SIMULAION OF EACH FIN

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CHAPTER 14

RESULT AND FINAL DISCUSSION :

Ansys simulation have been made on each pin fin using computational fluid dynamics and dissipation of heat at various region of fin has been calculated.

From the chart it has been identified that heat dissipation is high in pin fins due to large number of air passages between fins. whereas in a plate fin heat dissipation rate is low when compared to pin fin due to less number of air passage.

So usage of pin fin will be more effective to provide cooling condition to system instead of plate fin

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CHAPTER 15

CONCLUSION

Thus the analysis have been done on the rate of heat dissipation of plate fin and pin fin using ansys simulation software . we found that the rate of heat dissipated is high in pin fin when compared to plate fin. so usage of pin fin in electronic components is more effective as it dissipates a lot of heat generated . Also usage of material is less in pin fin when compared to plate fin

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CHAPTER 16

REFERENCES :

1. Optimal design of plate-and-frame heat exchangers for efficient heat recovery in process industries. Olga P. Arsenyeva b, et al(2011),pp. 4588-4598.

2. J. A. W. Gut, J. M. Pinto, Modelling of plate heat exchangers with generalized configurations, International journal of heat and mass transfer,, 2003, pp. 2571-2585.

3. R. K. Shah, W. W. Focke, Plate heat exchangers and their designtheory, Heat transfer Equipment Design, Hemisphere, New York, 1988,pp. 227-254.

4. T. Zaleski, K. Klepack Approximate method of solving equations for plate heat exchangers, International journal of heat and mass transfer, vol. 35, n°5 , pp. 1125-1130.

5. Numerical Analysis of Plate Heat Exchanger Performance in Co-Current Fluid Flow Configuration. H. Dardour, S. Mazouz, and A. Bellagi (2009).

6. Development of structural design procedure of plate-fin heat exchanger for HTGR. Yorikata Mizokamia, Toshihide Igari b, Fumiko Kawashimae, et al.(2011),pp. 248– 262..

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RESULTS

PLATE FIN

The maximum temperature is observed to be 100.55 degree Celsius.

The minimum temperature is observed to be 84.55 degree Celsius

PIN FIN

The maximum temperature is observed to be 100.55 degree Celsius.

The minimum temperature is observed to be 78.689 degree Celsius.

RESULTS

PLATE FIN Q =8.9 W

PIN FIN Q=11.7W

The dissipation rate is higher for pin-fin arrangement and hence it is more efficient in cooling the circuit.

LEARNING OUTCOMES

Understood the basic working of Heat Sinks and understood the importance of their usage

Understood how heat is dissipated through Heat Sinks.

Learnt how different designs can alter the rate of heat dissipation.

REFERNCE

http://en.wikipedia.org/wiki/Fin_(extended_surface)

http://en.wikipedia.org/wiki/Heat_sink

http://www.howstuffworks.com/heat-sink.htm

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