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

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

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.

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

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)

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

Finite element analysis Governing Equations for heat transfer  

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

Finite element analysis Mesh Number 125594 elements 15279 elements 2086 elements

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

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

Temperature

62.5C 58C 55.1C 53C

61.045C 58.225C 57.11C 56.819C

Experiment Simulation

The result of initial step size[Beta 0.0001 ] SCGM Method The result of initial step size[Beta 0.0001 ] [Start point =h1=57.0000mm,h2=41.5000mm,w1=30.5000mm,w2=24.5000mm]

The result of initial starting point[beta 0.00003] SCGM Method The result of initial starting point[beta 0.00003] [Start point =h1 58.9000mm, h2= 43.2000mm,w1= 30.7800mm,w2= 24.7630mm]

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

[Generations 100 Population 100]

Comparison of the generation and population

SGCM GA Table.1Compare beta size and optimal variables of simulations w1 w2 h1 h2 Optimal J (T1) 0.0001 38 0.004 0.0005 0.0007 0.0257 64.7921704 0.0002 22 64.5920246 0.0003 14 64.5917845 0.00001 394 64.5915898 0.00002 198 64.5916952 0.00003 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 64.59156552 0.0002 8 64.59202468 0.0003 0.00001 51 64.59164774 0.00002 66 64.59233533 0.00003 71 64.59206346 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 0.004000 0.000484 0.000599 0.026701 64.367552 13: 42: 3.31 G100-P50 0.003992 0.000480 0.000596 0.026576 64.288230 15:4: 15.67 G100-P60 0.000496 0.000600 0.026519 64.237978 18:7: 38.20 G100-P70 0.026521 64.239281 21: 2: 27.33 G100-P85 0.003996 0.000491 0.000598 0.026517 64.240511 25: 41: 3.10 G100-P100 0.000498 64.240417 30: 4: 17.92 GA

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 64.7921704 0.0002 22 64.5920246 0.0003 14 64.5917845 0.00001 394 64.5915898 0.00002 198 64.5916952 0.00003 132 Table.2 Beta size IA Optimal J (T1) 0.0001 16 64.59156552 0.0002 8 64.59202468 0.0003 0.00001 51 64.59164774 0.00002 66 64.59233533 0.00003 71 64.59206346 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

TEMP 2 TEMP 1 TEMP 3

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

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.

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