1. Design and Optimization of the LED Lamp Holder
Student: Siwapong kingkaew
Adviser. David T.W. Lin
Co-Adviser. Jui-Ching Hsieh
National University of Tainan
System Optimization Lab

2. Outline 1 Introduction 2 Design and Optimization 3
Results and Discussion 4
Conclusion

3. In recent years, light emitting diode (LED) has begun to play more and important role in many applications including back lighting for cell phones, LCD displays, interior and exterior automotive lighting.
In the present study, we combine SCGM,GA and FEM method to design heat sink and obtain minimum temperature on MCPCB for high power LED array.

4. Simulation and optimization
2.1 Concept
Real Model 1
Simulation and optimization
New Prototype
Initial
Geometry
SCGM 4
New Design
(Initial )
Optimal 3
optimization
Experiment
Data
Simulation
Data 2
Comparison
(Initial )
Optimal GA

5. 2.1 The concept of design and optimization
Start
Initial Geometry
New Design(Simulation)
Create Model
Simulation(Prototype)
Experiment (Prototype)
Compare Experiment and simulation
No.
Compare
Simulation and Experiment
No.
Optimization (SCGM)
Yes.
Yes.
Optimization (SCGM,GA)
End
Optimization (GA)

6. Dimensional of Heat sink
Content Layouts
variables
Max(mm)
Min(mm) h1 61 57 h2 45
41.5 w1
31.7
30.5 w2
26.5
24.5

7. Finite element analysis
Governing Equations for heat transfer

8. Thermal conductivity (W/mK)
Finite element analysis
Boundary and Initial Conditions
Thermal conductivity (W/mK)
Surface emissivity
Aluminum
200
0.9
Chip
65.6
0.82
Copper
400
0.74
Lens
0.22
0.94
Housing white body
0.2 h1 h2 h2 h1
External Temperature(Tinf)=25C
Ambient Temperature(Tamb)=25C
Heat transfer coefficient(h1)=5
Heat transfer coefficient(h2)=0
Q=10W h2 h1

9. Finite element analysis
Mesh Number
elements
15279 elements
2086 elements

10. 2.3 Optimization process (SCGM)
Start
2.3 Optimization process (SCGM)
Optimization Run
With SCGM
COMSOL script
Input File
Change Initial
Parameter
Simulation Run
With COMSOL
Check For Convergence
Local Optimum? No
Yes
Optimum Design

11. 2.3 Optimization process (GA)
Cost function, variables and initial values
Population, Generation
COMSOL
Matlab
Design
Chromosome Cost
Equations
Pair Selection
Geometry
Results
Cross
Subdomain Settings
Mutation
Boundary Settings
Convergence
Solving Equations
End

12. Temperature

13. 62.5C
58C
55.1C
53C

14. 61.045C
58.225C
57.11C
56.819C

15. Experiment
Simulation

16. The result of initial step size[Beta 0.0001 ]
SCGM Method
The result of initial step size[Beta ]
[Start point =h1= mm,h2= mm,w1= mm,w2= mm]

17. The result of initial starting point[beta 0.00003]
SCGM Method
The result of initial starting point[beta ]
[Start point =h mm, h2= mm,w1= mm,w2= mm]

18. GA Method
[Generations 100 Population 100]
[100 x100]

19. [Generations 100 Population 100]

20. Comparison of the generation and population

21. SGCM GA Table.1Compare beta size and optimal variables of simulationsw1 w2 h1 h2
Optimal J
(T1)
0.0001 38
0.004
0.0005
0.0007
0.0257
0.0002 22
0.0003 14
394
198
132
SGCM
Table.2Compare beta size and optimal variables of simulations
Beta size IA
Optimal w1 w2 h1 h2
Optimal J
(T1)
0.0001 16
0.004
0.0005
0.0007
0.0257
0.0002 8
0.0003 51 66 71
Table.3 Compare generation, population size and CPU-Time at final Generation
Case
Optimal w1 w2 h1 h2
Optimal J
(T1)
CPU-Time
G100-P45
13: 42: 3.31
G100-P50
15:4: 15.67
G100-P60
18:7: 38.20
G100-P70
21: 2: 27.33
G100-P85
25: 41: 3.10
G100-P100
30: 4: 17.92 GA

22. Compare beta size[SCGM] and bit number[GA]
Table.1
Compare beta size[SCGM] and bit number[GA]
Beta size IA
Optimal J
(T1)
0.0001 38
0.0002 22
0.0003 14
394
198
132
Table.2
Beta size IA
Optimal J
(T1)
0.0001 16
0.0002 8
0.0003 51 66 71 GA
Table.3
Bit number
Optimal J
(T1)
CPU-Time 5
64.252
21:21:55.05 6
64.241
20: 39:30.64 7
64.268
19: 14: 58.96 8
64.250
20: 52: 64.83

24. TEMP 2
TEMP 1
TEMP 3

25. Comparison Experiment & Simulation(New prototype)
Room temperature 24.7 [C]
24.7
Experiments value(℃)
Simulation
value(℃)
∆Temperature
(℃)
TEMP1
56.000
55.710
0.2900
TEMP2
50.000
51.950
1.9500
TEMP3
48.000
50.420
2.4200

26. Design and optimize the LEDs lamp holder is an important problem for the temperature concentration. In order to obtain good quality, it present a SCGM,GA method for model heat sink of LEDs lamp in this study.
This study present on three dimensional finite element analysis model of the LEDs light bulb by using the SCGM,GA combined with COMSOL multi-physics software to optimize the size of the heat sink of LEDs light bulb for transported the heat more effectively to heat sink .
This heat sink design method can be used the other electronics cooling system.

27. Thank You!