應用各種最佳化技術於完全限制狀況下高性能散熱座之散熱最佳化研究

簡易檢索 / 詳目顯示

回結果列表

研究生：	洪振聰 JENN-TSONG HORNG
論文名稱：	應用各種最佳化技術於完全限制狀況下高性能散熱座之散熱最佳化研究 Thermal Optimal Design for Fully-Confined Compact Heat Sinks by Using Various Optimization Methods
指導教授：	洪英輝 YING-HUEI HUNG 傅建中 CHIEN-CHUNG FU
口試委員:
學位類別：	博士 Doctor
系所名稱：	工學院 - 動力機械工程學系 Department of Power Mechanical Engineering
論文出版年：	2008
畢業學年度：	96
語文別：	中文
論文頁數：	309
中文關鍵詞：	最佳化設計、高性能散熱座、完全限制、類神經網路、基因演算法
外文關鍵詞：	optimal design, compact heat sink, fully-confined, ANN, GA
相關次數：	點閱：2 下載：0
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

本論文研究針對在水平渠道內完全限制狀況下高性能散熱座之流體流阻與熱傳特性作一系列的實驗研究與理論分析。依據實驗資料與理論分析結果，發現局部有效紐賽數在展向(Spanwise)具對稱性，而沿著流向(Streamwise)而遞減。在探討之影響參數中格拉雪夫數(GrH )、散熱座基板高度與整體散熱座高度比值( Hb/H)或鰭片與基座(Fin/Base)材料不同組合對局部有效紐賽數的影響不顯著；但發現此紐賽數會隨著雷諾數(ReD)的增加而增加。上述的參數對於平均紐賽數、局部與平均外部熱導，亦呈現相同的影響趨勢。根據實驗結果，本研究進一步地提出包含有效摩擦因子、平均外部熱阻與平均總熱阻之熱流特性經驗公式。
在進行最佳化研究之前，本研究有效地運用反應曲面法(RSM) 獲得的二階函數與類神經網路(ANN)推論獲得的三階函數兩種模式精確地預測總熱阻、壓降與質量的值。以RSM獲得的二階函數模式預測值與實驗值相比，在總熱阻、壓降與質量預測上之最大誤差分別為7.3%、1.7%與6.3%；利用ANN方法產生的三階函數模式預測值與實驗值相比，在總熱阻、壓降與質量預測上之最大誤差分別為3.2%、3.8%與6.3%。另外，本研究應用倒傳遞(Back-propagation)訓練方法，建立輸出之性能與輸入之設計參數間更精確的隱含性ANN函數；以此函數預測值與實驗值作比較，在總熱阻、壓降與質量預測上之最大誤差分別為2.6%、2.8%與4.3%。
最後，本研究針對水平渠道內完全限制狀況下高性能散熱座之熱傳特性最佳化的探討上，分別使用RSM-SQP、ANN-GA、RSM-GA與ANN-SQP等四種不同的最佳化方法，成功地完成在壓力，質量與空間等多重限制狀況下散熱最佳化的評估設計。經由上述四種不同最佳化方法的比較，可以發現RSM-SQP方法與ANN-GA方法兩者較RSM-GA方法與ANN-SQP方法兩者優異。當與ANN-GA方法之最佳化結果作比較，發現使用RSM-SQP方法在Case I 與Case II時，由於最佳化的設計參數均遠離可操作的區域，故其最佳化的結果產生了顯著的誤差；相對的，在Case III 與Case IV 因為最佳化的設計參數位於可操作的區域內，因此RSM-SQ方法亦可獲得滿意的結果。總而言之，使用RSM-SQP方法的優點是在本研究中僅需77組測試資料，它遠少於ANN-GA方法所需的320 組測試資料，故操作上顯著地節省電腦計算時間。如最佳化的設計參數在可操作的範圍內(如Case III 與Case IV)，即可得到滿意的結果。至於使用ANN-GA方法的優點，由於ANN網路的訓練樣本是選取在整個求解的領域內均勻分佈的樣本中隨機選取，其最佳化的解可涵蓋整體有興趣的區域，而非僅止於可操作的區域，因此ANN-GA方法可以求得精確的整體最佳化解，這種結論可由在本研究中Case (I)至Case (IV)最佳化的結果獲得印證。

In the present study, a series of experimental investigations and theoretical analyses on the fluid flow friction and heat transfer behavior for fully-confined compact heat sinks in a ducted flow have been performed. From the experimental data and theoretical results, the distribution of local effective Nusselt number is symmetric along the spanwise direction; and the local effective Nusselt number decreases along the streamwise direction. The local effective Nusselt number is insignificantly affected by GrH, Hb/H or Fin/Base material; while, it increases with increasing ReD. Similar trends can be found for the effects of relavant influencing parameters on other heat transfer characteristics such as average effective Nusselt number, local and average external thermal conductance. New correlations for fluid flow friction and thermal performance, including the effective friction factor, average effective Nusselt number, average external thermal resistance and overall thermal resistance, in terms of relevant influencing parameters have been presented.
Prior to the execution of thermal optimization, two numerical models such as the RSM with a quadratic explicit formula and the ANN with a third-order explicit formula are effectively employed to accurately fit the data of overall thermal resistance, pressure drop and mass. As compared with the actual experimental data and theoretical results, the maximum deviations of the predictions for overall thermal resistance, pressure drop and mass by using the RSM method with a quadratic explicit formula are 7.3%, 1.7%, and 6.3%; those by using the ANN method with a third-order formula are 3.2%, 3.8% and 5.2%, respectively. In addition, with an effective back-propagation training algorithm, a more accurate implicit correlation between the performance outputs and design variables has been obtained. As compared with the actual experimental data and theoretical results, the maximum deviations of the predictions by this implicit correlation for overall thermal resistance, pressure drop and mass are 2.6%, 2.8%, and 3.4%.
Furthermore, four types of optimization technique such as RSM-SQP, ANN-GA, RSM-GA and ANN-SQP methods have been successfully employed for the optimal evaluation on the thermal performance of fully-confined compact heat sinks in a ducted flow under multi-constraints such as pressure drop, mass, and space limitations. Among these four optimization methods, the superiority of using either the RSM-SQP or ANN-GA method can be found as compared with using the ANN-SQP or RSM-GA method. As compared with the optimal results obtained by the ANN-GA method, a significant deviation evaluated by using the RSM-SQP method for Cases I and II is found because the optimal values of the design variables are located beyond the region of operability. In contrast, a satisfactory agreement is achieved by the RSM-SQP method for Cases III and IV because the optimal values of the design variables are located within the region of operability. In summary, the advantage of using the RSM-SQP method with a smaller number of test cases, say 77 instead of 320 in the ANN-GA method, can be significantly found in the saving of computation time for some design cases with multi-constraints in the region of operability, e.g. Cases III and IV. As for the advantage of using the ANN-GA method, although more test cases are needed for the ANN-GA method as compared to that for the RSM-SQP method, the ANN-GA method which has randomly uniform-distributed training patterns in the whole solving domain can be applied to the global region of interest, not just in the region of operability; a globally precise optimal solution can be achieved with the ANN-GA method for all the cases (i.e., Cases I through IV) explored in the present study.

ABSTRACT           i
ACKNOWLEDGMENTS      iii
LIST OF TABLES        xi
LIST OF FIGURES     xiii
NOMENCLATURE       xxiii

CHAPTER 1  INTRODUCTION AND BACKGROUND    1

1.1    RATIONALE    1

1.2    FUNDAMENTAL MECHANISMS OF HEAT SINKS    3
1.2.1     Fluid Flow Characteristics     3
1.2.2     Heat Transfer Characteristics    7

1.3    LITERATURE SURVEY    7
1.3.1     Fluid Flow Characteristics    8
1.3.2     Heat Transfer Characteristics    9
1.3.3     Design Optimization Techniques    13

1.4    RESEARCH TOPICS AND OBJECTIVES    17
1.4.1  Fluid Flow Friction anf Heat Transfer for Fully-Confined
     Compact Heat Sinks    18
1.4.2  Thermal Optimal Design for Fully-Confined Compact Heat
Sinks with Various Optimal Methods     19

1.5    THESIS ORGANIZATION    19

CHAPTER 2   FLUID FLOW AND HEAT TRANSFER FOR FULLY-
CONFINED COMPACT HEAT SINKS    21

2.1    EXPERIMENTAL INVESTIGATION    21
2.1.1  Description of Experimental Facilities    22
(A) Air Supply System         22
(B) Pressure Load Unit    22
(C) Test Section and Test Assembly          22
(D) Type of Test Assembly    24
(E) Apparatus and Instrumentation    24
2.1.2  Data Acquisition and Control    26
2.1.3  Experimental Procedure    27
(A) Start-up Procedure and Operating Procedure    27
(B) Shutdown Procedure    28
2.1.4  Data Reduction    29
(A) Local heat transfer characteristics    30
(B) Average local transfer characteristics    32
(C) Local Thermal Resistances     33
(D) Average Thermal Resistances     34
2.1.5  Test Matrix and Test Cases    35
    2.1.6  Uncertainty Analysis    35
    2.1.7  Sensitivity Analysis    36

2.2  THEORETICAL EVALUATION WITH EMPIRICAL CORRELATIONS    38
2.2.1  Pressure Drop Evaluation    38
2.2.2  Overall Effective Heat Transfer Performance    39

2.3  NUMERICAL ANALYSIS    41
2.3.1  Theoretical Model    41
(A) Generalized Governing Equations    42
(B) Turbulence Model     42
(C) Boundary Conditions    44
2.3.2  Numerical Scheme    44
(A) Staggered Grid System    45
(B) Grid-independent Test     45
(C) Discretization Equations    46
(D) Pressure and Velocity Corrections    47
(E) Convergence Criteria    49
(F) Solution Procedure of SIMPLE Algorithm    50
(G) FLOTHERM Software    51
(H) Numerical Tests     51
(I) Test Matrix and Test Cases    51

CHAPTER 3  OPTIMAL DESIGN METHODOLOGY     109

3.1    DESIGN OF EXPERIMENTS    109

3.2    RESPONSE SURFACE METHODOLOGY    110
3.2.1  Central Composite Design     112
3.2.2  Least Squares Regression Analysis     113
3.2.3  Accuracy and Adequacy of Regression Model     116

3.3  SEQUENTIAL QUADRATIC PROGRAMMING    119
3.3.1  Fundamental Concept    119
3.3.2  Globally Optimal Solution    121

3.4  ARTIFICIAL NEURAL NETWORK    122
3.4.1  Introduction to Artificial Neural Network    122
3.4.2  Backpropagation for a Multilayer Neural Network    126
3.4.3  Neural Learning Using Backpropagation    127

3.5    GENETIC ALGORITHMS    129
3.5.1  The Basic Genetic Algorithm Operations    129
3.5.2  Genetic Algorithms Work with Real Numbers    131
3.5.3  Genetic Algorithms Procedure    132

3.6  OPTIMIZATION TECHNIQUES    133
3.6.1  RSM-SQP Method    133
3.6.2  ANN-GA Method    133
3.6.3  RSM-GA Method    134
3.6.4  ANN-SQP Method    134

CHAPTER 4  FLUID FLOW FRICTION AND HEAT TRANSFER FOR FULLY-CONFINED COMPACT HEAT SINKS    144

4.1    FLOW FRICTION BEHAVIOR    145
4.1.1  Total Pressure Drop of Non-Compact Heat Sink    145
4.1.2  Total Pressure Drop of Compact Heat Sink    146

4.2    HEAT TRANSFER CHARACTERISTICS    147
4.2.1  Definitions of Heat Transfer Parameters    148
4.2.2  Temperature Distribution on Heat Sink Base    149
4.2.3  Local Heat Transfer Performance     150
4.2.4  Average Heat Transfer Performance    154
4.2.5  Correlations of Average Heat Transfer Characteristics    155
4.2.6  External Thermal Resistance    157
4.2.7  Overall Thermal Resistance    164

CHAPTER 5  THERMAL OPTIMAL DESIGN FOR FULLY-CONFINED COMPACT HEAT SINKS    198

5.1  THERMAL OPTIMAL DESIGN WITH RSM-SQP METHOD    198
5.1.1  Design Parameters for Compact Heat Sink    199
5.1.2  DOE and Response Surface Methodology    199
5.1.3  Model Adequacy Checking    199
5.1.4  Numerical Optimization    200

5.2    THERMAL OPTIMAL DESIGN WITH ANN-GA METHOD     203
5.2.1  Design Variables of PPF Heat Sinks    203
5.2.2  Artificial Neural Network of Compact Heat Sinks    204
5.2.3  Optimization Technique Using ANN-GA Method for Compact Heat Sink    205
5.2.4  Thermal Optimal Design under Multi-Constraints    208

5.3    THERMAL OPTIMAL DESIGN WITH RSM-GA METHOD    211

5.4    THERMAL OPTIMAL DESIGN WITH ANN-SQP METHOD    214

5.5     COMPARISONS OF THERMAL OPTIMIZATION WITH VARIOUS OPTIMAL METHODS    218
5.5.1  Case (I) Constraints on 岛P and M in the Region of Interest or Design Variables     219
5.5.2  Case (II) Constraint with 岛P≦200 Pa in the Region of
Interest for Design Variables     219
5.5.3  Case (III) Constraint with M≦ 250 g in the Region of
Interest for Design Variables     220
5.5.4  Case (IV) Multi-Constraints of 岛P≦200 Pa and M≦250 g in the Region of Interest for Design Variables     221

CHAPTER 6  CONCLUSIONS AND ECOMMENDATIONS    265

6.1  CONCLUSIONS    265
6.1.1  Fluid Flow Friction and Heat Transfer for Fully-
Confined Compact Heat Sinks    265
6.1.2  Thermal Optimal Design for Fully-confined Compact Heat
Sinks    267

6.2  RECOMMENDATIONS    268

REFERENCES    270
APPENDIX A    CALIBRATION OF AIR VELOCITY    282
APPENDIX B    EMPIRICAL CORRELATIONS FOR AIR PROPERTIES     287
APPENDIX C    RADIATIVE HEAT LOSSES FROM HEAT SINK SURFACE TO SURROUNDINGS        293
APPENDIX D    THICKNESS OF THERMAL GREASE LAYERS     296
APPENDIX E    UNCERTAINTY ANALYSIS     299
VITA    308
LIST OF PUBLICATIONS    309

                                

[1] Viswanath, R., Wakharkar, V., Watwe, A., and Lebonheur, V., 2000, "Thermal Performance Challenges from Silicon to Systems," Intel Technology Journal, Q3, pp.1-16.
[2] Reill, M., 1994, "Iterative Direct Solution Method for Thermal Analysis of Electronic Equipment,” IEEE Electronic Components and Technology Conference, pp.644-652.
[3] Kraus, A. D., and Bar-Cohen, A., 1983, Thermal Analysis and Control of Electronic Equipment, Hemisphere Publishing Corporation, Washington, D.C., USA.
[4] Ellison, G. N., 1989, Thermal Computations for Electronic Equipment, 2nd Edition, Van Nostrand Reinhold Corporation, New York, USA.
[5] Ishizuka, M., Yokono, Y., and Hisano, K., 1999, “Experimental Study on the Performance of Compact Heat Sink for LSI Packages,” ASME Advances in Electronic Packaging, 26(1), pp.713-718.
[6] Copeland, D., 2000, “Optimization of Parallel Plate Heat sinks for Forced Convection”, 16th IEEE SEMI-THERMTM Symposium, San Jose, CA, USA, March 21-23, pp.266-272.
[7] Chen, H. T., Chen, P. L., Horng, J. T., and Hung, Y. H., 2005, “Design Optimization for Pin-Fin Heat Sinks,” ASME Journal of Electronic Packaging, 127(4), pp.397-406.
[8] Park, K., Oh, P. K., and Lim, H. J., 2006, “The Application of the CFD and Kriging Method to an Optimization of Heat Sink,” International Journal of Heat and Mass Transfer, 49, pp.3439 –3447.
[9] Chen, H. T., Chen, P. L., Horng, J. T., Chang, S. F., Wu, T. Y., and Hung, Y. H., 2006, ”Thermal Design Optimization for Strip-Fin Heat Sinks with a Ducted Air Flow,” Itherm 2006, San Diego, CA, USA, May 30-June 2.
[10] Peng, C. H., Wu, M. C., Lee , C. Y., Horng, J. T., Fang, C. J., and Hung, Y. H., 2006, ”Optimal Approach with Genetic Algorithm for Thermal Performance of Heat Sink/TEC Assembly,” Itherm 2006, San Diego, CA, USA, May 30-June 2.
[11] Wu, T. Y., Wu, M. C., Horng, J. T., Chang, S. F., Chen, P. L., and Hung, Y. H., 2007,”Thermal Optimal Design for Heat Sinks or Heat Sink/TEC Assemblies in a Ducted Flow,” InterPACK 2007, Vancouver, Canada, July 8-12.
[12] Wu, M. C., Wu, T. Y., Horng, J. T., Chang, S. F., Chen, P. L., and Hung, Y. H., 2007, “A Semi-Empirically Thermal Optimization for Heat Sink/TEC Assemblies with Various External Thermal Resistances,” InterPACK 2007, Vancouver, Canada, July 8-12.
[13] Mills, A. F., 1999, Basic Heat and Mass Transfer, Prentice-Hall, Inc., New Jersey, USA.
[14] Matsushima, H., Yanagida, T., and Kondo, Y., 1992, “Algorithm for Predicting the Thermal Resistance of Finned LSI Packages Mounted on a Circuit Board,” Heat Transfer Japanese Research, 21(5), pp.504-517.
[15] Sparrow, E. M., and Beckley, T. J., 1981, “Pressure Drop Characteristics for a Shrouded Longitudinal Fin Array with Tip Clearance,” ASME Journal of Heat Transfer, 103, pp.393-395.
[16] Sparrow, E. M., Niethammer, J. E., and Chaboki, A., 1982, “Heat Transfer and Pressure Drop Characteristics of Arrays of Rectangular Modules Encountered in Electronic Equipment,” International Journal of Heat and Mass Transfer, 25(7), pp.961-973.
[17] Sparrow, E. M., and Grannis, V. B., 1990, “Pressure Drop Characteristics of Heat Exchangers Consisting of Arrays of Diamond-Shaped Pin Fins,” ASME Journal of Heat Transfer, 34(3), pp.589-600.
[18] Jonsson, H., and Moshfegh, B., 2001, “Modeling of the Thermal and Hydraulic Performance of Plate Fin, Strip Fin, and Pin Fin Heat Sinks—Influence of Flow Bypass,” IEEE Transactions on Components and Packaging Technologies, 24(2), pp.142-149.
[19] Narasimhan, S., Bar-Cohen, A., and Nair, R., 2003, “Flow and Pressure Field Characteristics in the Porous Block Compact Modeling of Parallel Plate Heat Sinks,” IEEE Transactions on Components and Packaging Technologies, 26(1).
[20] Jeng, T. M., Wang, M. P., and Hung, Y. H., 2003, “Performance Prediction for Partially-Confined Heat Sinks,” Proceedings of INTERPACK, IPACK2003-35021, Maui, Hawaii, USA, July 6-11, 2003.
[21] Lei, N. and Ortega, A. 2004, “Experimental Hydraulic Characterization of Pin Fin Heat Sinks with Top and Side Bypass,” 9th Inter Society Conference on Thermal Phenomena, Las Vegas, USA, June 1-4, pp.418-428.
[22] Wang, M. P., Wu, T. Y., Horng, J. T., and Hung, Y. H., 2005, “Fluid Flow Characteristics for Partially-Confined Compact Plain-Plate-Fin Heat Sinks,” 2005 ASME Proceedings of Heat Transfer, HT2005-72225, San Francisco, CA, July 17-22.
[23] Sparrow, E. M., Baliga, B. R., and Patankar, S. V., 1978, "Forced Convection Heat Transfer from a Shrouded Fin Array with and without Tip Clearance," ASME Journal of Heat Transfer, 100, pp.572-579.
[24] Ashiwake, N., Nakayama, W., Daikoku, T., and Kobaiashi, F., 1983, “Forced Convection Heat Transfer from LSI Packages in an Air-cooled Wiring Card Array,” ASME Heat Transfer in Electronic Equipment, HTD- 28, p.35.
[25] Matsushima, H., Yanagida, T., and Kondo, Y., 1992, “Algorithm for Predicting the Thermal Resistance of Finned LSI Packages Mounted on a Circuit Board,” Heat Transfer Japanese Research, 21(5), pp.504-517.
[26] Wirtz, R. A., Chen, W. M., and Zhou, R. H., 1994, “Effect of Flow Bypass on the Performance of Longitudinal Fin Heat Sinks,” ASME J. Electronic Packaging, 116, pp.206-211.
[27] You, H. I., and Chang, C. H., 1997, “Numerical Prediction of Heat Transfer Coefficent for a Pin–Fin Channel Flow,” ASME Journal of Heat Transfer, 119, pp.840-843.
[28] Kim, S. Y., Koo, J.M., and Kuznetsov, A.V., 2001, “Effect of Anisotropy in Permeability and Effective Thermal Conductivity on Thermal Performance of an Aluminum Foam Heat Sink”, Numerical Heat Transfer Part A-Applications, 40, pp.21-36.
[29] Ma, H. B. and Peterson, G. P, 2002,“The Influence of the Thermal Conductivity on the Heat Transfer Performance in a Heat Sink,” Journal of Electronic Packaging, 124(3), pp.164-169.
[30] Chrysler, G., Mahajan, R. and Prasher, R., 2002, “Spreading in the Heat Sink Base: Phase Change Systems or Solid Metals,” Components, Packaging and Manufacturing Technology, Part A: Packaging Technologies, 25, pp.621-628.
[31] Tzeng, S. C., and Ma, W. P., 2004, “Experimental Investigation of Heat Transfer in Sintered Porous Heat Sink,” International Communications in Heat and Mass Transfer, 31, pp.827-836.
[32] Wu, T. Y., Wang, M. P., Horng, J. T., Chang, S. F., and Hung, Y. H., 2005, “Forced Convective Heat Transfer for Partially-Confined Compact PPF Heat Sinks with Top-Bypass Effect,” Proceedings of INTERPACK, IPACK2005-73121, San Francisco, CA, USA, July 17-22.
[33] Chen, H. T., 2005, “Thermal Optimal Design for Generalized Heat Sinks in Electronics Cooling Applications,” Ph.D. Thesis, Department of Power Mechanical Engineering, National Tsing Hua University, Taiwan, R.O.C.
[34] Yakut, K., Alemdaroglu, N. and Kotcioglu, I., 2006, “Experimental Investigation of Thermal Resistance of a Heat Sink with Hexagonal Fins,” Applied Thermal Engineering, 26, pp.2262-2271.
[35] Torii, S., and Yang, W. J., 2007, “Thermal-fluid Transport Phenomena over Slot-perforated Flat Fins with Heat Sink in Forced Convection Environment,” Journal of Enhanced Heat Transfer, 14, pp.123-134 .
[36] Park, K., Choi, D. H., and Lee, K. S., 2004, "Optimum Design of Plate Heat Exchanger with Staggered Pin Arrays," Numer. Heat Transfer, Part A, pp.347-361.
[37] Park, K., Choi, D. H., and Lee, K. S., 2004, "Numerical Shape Optimization for High Performance of a Heat Sink with Pin-fins," Numer. Heat Transfer, Part A, pp.909-927.
[38] Chang, J. Y., Yu, C. W., and Webb, R. L., 2001, “Identification of Minimum Air Flow Design for a Desktop Computer Using CFD Modeling,” Journal of Electronic Packaging, 123, pp.225-231.
[39] Chiang, J. S., Chuang, S. H., Wu, Y. K., and Lee, H. J., 2005, “Numerical Simulation of Heat Transfer in a Desktop Computer with Heat-Generating Components,” International Communications In Heat And Mass Transfer, 32, pp.184-191.
[40] Lee, T. Y., and Mahalingam, M., 1994, “Application of a CFD Tool for System-level Thermal Simulation,” IEEE Trans. Comp., Hybrids. Manufact. Technol., 17, pp.564-572.
[41] Wheaton, C., 1993, “Thermal Design of the Silicon Graphics Indigo 2 Workstation Using Physical and Computational Modeling,” Proc. 2nd Flothem Int. User Conf., Flomerics Limited, 1993.
[42] Rurgos, J., Manno, V. P., and Azar, K., 1995, “Achieving Accurate Thermal Characterization Using a CFD Code - A Case Study of Plastic Packages,” IEEE Trans. Comp., Hybrids. Manufact. Technol., 18, pp.732-738.
[43] Lee, T. Y., and Mahalingam M., 1997, “Thermal Limits of Flip Chip Package - Experimentally Validated. CFD Supported Case Studies,” IEEE Trans. Comp., Hybrids. Manufact. Technol., 20, pp.94-103.
[44] Chambers, B., and Lee, T. Y., 1997, “A Numerical Study of Local and Average Natural Convection Nusselt Numbers for Simultaneous Convection Above and Below a Uniformly Heated Horizontal Thin Plate,” Journal of Hear Transfer., 119, pp.102-108.
[45] Zahn, B. A., Stout, R. P., and Billings, D. T., 1996, “A Thermal Comparative Study of A Ceramic Dual In-Line Pressed Microelectronics Package Using Both Computational Fluid Dynamics and Solid Modeling Techniques on the ANSYS ‘ Finite Element Analysis System’,” Proc. 7th Int. ANSYS Conf. and Exhib., pp.2371 -2380.
[46] Zahn, B. A. and Stout, R. P., 1997, ”Evaluation of Isothermal and Isoflux Natural Convection Coefficient Correlations for Utilization in Electronic Package Level Thermal Analysis,” Proc. 13th IEEE Semi-Therm Symp., pp.24-31.
[47] Dallago, E., and Venchi, G., 2002, “Thermal Characterization of Compact Electronic Systems: A Portable PC as a Study Case,” IEEE Transactions on Power Electronics, 17(2), pp.187-195.
[48] Ragunathan, S., Goering, D.J. and Karulkar, P.C., 2007, ”Heat Transfer in a Three Dimensional Stacked Chip Scale Package (CSP) Module,” IEEE 23rd Annual, Semiconductor Thermal Measurement and Management Symposium, SEMI-THERM 2007, pp.244-247.
[49] Landram, C. S., 1991, “Computational Model for Optimizing Longitudinal Fin Heat Transfer in Laminar Internal Flows,” Heat Transfer in Electron. Equipment, 171, pp.127-134.
[50] Knight, R. W., and Gooding, J. S., 1991, "Optimal Thermal Design of Forced Convection Heat Sinks-Analytical," Journal of Electronic Packaging, 113, pp.313-321.
[51] Knight, R. W., Goodling, J. S., and Gross, B. E., 1992, "Optimal Thermal Design of Air Cooled Forced Convection Finned Heat Sinks—Experimental Verification," IEEE Trans. Compon., Hybrids, Manuf. Technol., 15(5), pp.754–760.
[52] Kraus, A. D., and Bar-Cohen, A., 1995, Design and Analysis of Heat Sinks, John Wiley and Sons, Inc., New York, USA.
[53] Lee, S., 1995, “Optimum Design and Selection of Heat Sinks,” IEEE Transactions on Components, Hybrids, and Manufacturing Technology, 18(4), pp.812-817.
[54] Box, G. E. P., and Wilson, K. B., 1951, “On the Experimental Attainment of Optimum Conditions,” J. Royal Statistics Society, Series B, No. 13, pp.1-45.
[55] Hou, X. J., and Wei, C., 2008, “Optimization of Extraction Process of Crude Polysaccharides From Wild Edible BaChu Mushroom by Response Surface Methodology,” Carbohydrate Polymers, 72, pp.67-74.
[56] Cubas, C., Lobo, M. G., and Gonzalez, M., 2008, ”Optimization of the Extraction of Chlorophylls in Green Beans (Phaseolus Vulgaris L.) by N,N-dimethylformamide Using Response Surface Methodology,” Journal of Food Composition And Analysis, 21, pp.125-133.
[57] Iervolino, I., Fabbrocino, G. and Manfredi, G., 2004, “Fragility of Standard Industrial Structures by a Response Surface Based Method,” Journal of Earthquake Engineering, 8, pp.927-945.
[58] Wei, D. L., Cui, Z. S., and Chen, J., 2007, “Optimization and Tolerance Prediction of Sheet Metal Forming Process Using Response Surface Model,” Computational Material Materials Science, 42, pp.228-233.
[59] Kim, Y. K., Hong, J. P., and Hur, J., 2003, “Torque Characteristic Analysis Considering the Manufacturing Tolerance for Electric Machine by Stochastic Response Surface Method,” IEEE Transactions on Industry Applications, 39, pp.713-719.
[60] Pinheiro, R. C., Soares, C. M. F. , de Castro H. F., Gisella, F. F. M. and Zanin, G. M. 2008, “Response Surface Methodology as an Approach to Determine Optimal Activities of Lipase Entrapped in Sol-gel Matrix Using Different Vegetable Oils,”, Applied Biochemistry and Biotechnology, 146, pp.203-214.
[61] Box, G. E. P., and Behnken, D. W., 1978, “Some New Three Level Designs for the Study of Quantitative Variables,” Technometrics, 2, pp.455-475.
[62] Box, G. E. P., and Draper, N. R., 1987, Empirical Model-Building and Response Surfaces, John Wiley & Sons, New York, USA.
[63] Monthomery, D. C., 2001, Design and Analysis of Experiments, 5th Ed., John Wiley & Sons, New York, USA.
[64] Khuri, A. I., and Cornell, J. A., 1996, Response surfaces: designs and analyses, 2nd Edition, Marcel Dekker.
[65] Myers, G. N., and Montgomery, D. C., 2002, Response Surface Methodology, 2nd Ed., John Wiley & Sons, New York, USA.
[66] Chen, H. T., Horng, J. T., Chen, P. L., and Hung, Y. H., 2004, “Optimal Design for PPF Heat Sinks in Electronics Cooling Applications,” ASME Journal of Electronic Packaging, 126, pp.410-422.
[67] Wang, M. P., Chen, H. T., Horng, J. T., Wu, T. Y., Chen, P. L., and Hung, Y. H., 2005, ”Thermal Optimal Design for Partially-Confined Compact Heat Sinks,” Proceedings of InterPACK’05, IPACK2005-73118, San Francisco, CA, USA, July 17-22.
[68] Rumelhart, D. E., Hinton, G. E., and Williams, R. J., 1986, “Learning Representation by Backpropagaion Error, ” Nature, 323(9), pp.533-536.
[69] Chen, S. S., Billings, A., and Grant, P. M., 1990, “Non-linear System Identification Using Neural Network,” Int. J. Control, 1(6), pp.1191-1214.
[70] Ilonen, J., Kamarainen, J. K. and Lampinen, J., 2003, “Differential Evolution Training Algorithm for Feed-forward Neural Networks,” Neural Processing Letters, 17(1), pp.93-105.
[71] Ilumoka, A. A., 1997, “Optimal Transistor Sizing for CMOS VLSI Circuits Using Modular Artificial Neural Networks,” Proc. IEEE 29th Southeastern Symposium on System Theory, Cookeville, Tennessee, USA, March 9-11, pp.310-314.
[72] Idir, K., Chang, L., and Dai, H., 1998, “Improved Neural Network Model for Induction Motor Design,” IEEE Trans. Magn., 34(5), pp.2948–2951.
[73] Han, S. Y., and Maeng, J. S., 2003, “Shape Optimization of Cut-off in a Multi-blade Fan/Scroll System Using Neural Network,” Int. J. Heat Mass Transfer, 46(5), pp.2833–2839.
[74] Hadim, H., and Suwa, T., 2005, “A Multidisciplinary Design and Optimization Methodology for Ball Grid Array Packages Using Artificial Neural Networks,” ASME Journal of Electronic Packaging, 127(3), pp.306-313.
[75] Jambunathan, K., Hartle, S. L., Ashforth-Frost, S., and Fontama, V. N., 1996. “Evaluating Convective Heat Transfer Coefficients Using Neural Networks,” Internal Journal of Heat and Mass Transfer, 39(11), pp.2329-2332.
[76] Pacheco-Vega, A., Diaz, G., Sen, M., Yang, K. T., and McClain, R. L., 2001, “Heat Rate Predictions in Humid Air-Water Heat Exchangers Using Correlations and Neural Networks,” ASME Journal of Heat Transfer, 123(2), pp.348-354.
[77] Goldberg, D. E., 1989, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA, USA.
[78] Holland, J. H., 1992, “Genetic algorithms,” Sci. Am., pp.44-50.
[79] Krishnakumar, K., and Goldberg, D. E., 1992, “Control System Optimization Using Genetic Algorithms,” J. Guidance Control Synamics, 15(3), pp.735-740.
[80] Hashinmot, Y., 1997, ”Introduction and Applications of Artificial Neural Networks and Genetic Algorithms to Agricultural System”, Computers and electronics I agriculture, 18, pp.71-72.
[81] Arifovic, J., and Gencay, R. 2001, “Using Genetic Algorithms to Select Architecture of a Feedforward Articial Neural Network”, Physica A, 289, pp.574-594.
[82] Song, R. G., and Zhang, Q. Z., 2001, “Heat Treatment Technique Optimization for 7175 Aluminum Alloy by an Artificial Neural Network and a Genetic Algorithm," J. Materials Processing Technology, 117, pp.84-88.
[83] Su, C. T., Su, T. L., and Chiang, T. L., 2002, ”Optimal Design for a Ball Grid Array Wire Bonding Process Using a Neuro-genetic Approach,” IEEE Transactions on Electronics Packaging Manufacturing, 25(1), pp.13-18.
[84] Niculescu, S. P., 2003, “Artificial Neural Networks and Genetic Algorithms in QSAR,” J. Molecular Structure, 622, pp.71-83.
[85] Ziver, A. K., and Pain, C. C., 2004, "Genetic Algorithms and Artificial Neural Networks for Loading Pattern Optimization of Advanced Gas-cooled Reactors," Annals of Nuclear Energy, 31, pp.431 –457.
[86] Malik, Z., and Rashid, K., 2000, “Comparison of Optimization by Response Surface Methodology with Neurofuzzy Methods,” IEEE Transactions on Magnetics, 36(1), pp.241-257.
[87] Yovanovich, M. M., Muzychka, Y. S. and Culham, J. R., 1999, “Spreading Resistance of Isoflux Rectangules and Strips on Compound Flux Channels,” Journal of Thermophysics and Heat transfer, 13(4), pp.495-500.
[88] Kline, S. J., and McClintock, F. A., 1953, "Describing the Uncertainties in Single Sample Experiments," Mechanical Engineering, 75, pp.3-8
[89] Moffat, R. J., 1988, “Describing the Uncertainties in Experimental Results,” Expl. Thermal Fluid Sci., 1, pp.3-17.
[90] White, F. M., 1991, Viscous Fluid Flow, 2nd Edition, McGraw-Hill, Singapore.
[91] Patankar, S. V. 1980, Numerical Heat Transfer and Fluid Flow, Hemisphere Publishing Corporation.
[92] Flomerics, 1993, How to Use FLOTHERM - Lecture Course, Flomerics, England.
[93] Goldstein, S., 1965, “Modern Developments in Fluid Dynamics,” Dover Publications Inc.
[94] Launder, B. E., and Spalding, D. B., 1974, “The Numerical Computation of Turbulent Flows,” Comput. Meth. Appl. Mech. Eng., 3, pp.269-289.
[95] Wang, M. J., 2000, “Numerical Investigation on the Thermal Characteristics of a Flip-Chip BGA Package,” Master’s Thesis, Department of Power Mechanical Engineering, National Tsing Hua University, Taiwan, R.O.C.
[96] Paul, G. T., 1997, “CFD Applied to Electronic Systems: A Review,” IEEE Trans. on Components, Packaging, and Manufacturing Technology, Part A, 20(4), pp. 518-529.
[97] Vanderplaats, G. N., 1993, Numerical Optimization Techniques for Engineering Design, McGraw-Hill, Singapore.
[98] Wesley, H. J., 1997, Fuzzy and Neural Approaches in Engineering MATLAB Supplement, John Wiley and Sons, Inc., New York, USA.
[99] Dayhoff, J. E., 1990, Neural Networks Architecture, Van Nostrand Reinhold, New York.
[100] Hagan, M., Demuth H., and Beale M., 1996, Neural Network Design, PWS PublishingCompany, Boston, USA.
[101] Kays, W. M., and Crawford, M.E., 1993, Convective Heat and Mass Transfer, 3rd edition, McGraw-Hill, New York, USA.

全文公開日期本全文未授權公開 (校內網路)
全文公開日期本全文未授權公開 (校外網路)

簡易檢索 / 詳目顯示

相關論文