臺灣博碩士論文加值系統

本論文主旨在於應用拉凡格氏法(Levenberg-Marquardt Method)搭配套裝熱流模擬軟體CFD-ACE+與程式語言Fortran，探討三維反算問題於雪花形散熱鰭片(snowflake-shaped fins)外形最佳化設計之研究，期望在不改變鰭片體積的情況下，獲得最低的無因次最高熱阻(dimensionless maximum thermal resistance)。
雪花形散熱鰭片可藉由將舵形散熱鰭片(helm-shaped fin)之扇形突出區域以固定長度L沿著中線左右向外分開獲得。向外分開後鰭片上將形成孔洞，進而達到降低溫度的功效。本論文之目標為預測出雪花形鰭片的最佳設計參數，並以文獻[23]作為基礎，探討雪花形散熱鰭片相較於舵形散熱鰭片(helm-shaped fin)之優劣，同時針對單一熱源與多熱源兩種案例進行反算，其中多熱源案例又包括雙熱源問題與四熱源問題，並將結果數據詳細表列及繪製成圖。結果指出，單熱源問題之無因次最高熱阻的下降百分比隨著鰭片厚度 增加呈現上升的趨勢，最高可達11.553%。多熱源問題之無因次最高熱阻的下降百分比隨著對流熱傳係數(convective heat transfer parameter)a減少呈現上升的趨勢，在雙熱源問題中最高可達14.358%；在四熱源問題中最高可達17.631%。

A three-dimensional shape design problem is considered in this thesis to determine the optimal snowflake-shaped fins (SSF), based on the minimization of maximum domain temperature of fin under fixed fin volume constraint. The Levenberg-Marquardt method (LMM) coupled with software package CFD-ACE+ are used as the design tools. The SSF can be obtained by modifying the helm-shaped fins (HSF), the fine surfaces of HSF can be increased by splitting central part of the extended bodies from the center line outward and perforations can therefore be formed, and thus the maximum temperature of the fin can be reduced. The objective of this work is to estimate the optimal design variables of SSF such that they can minimize the maximum fin temperature. The validity of CFD-ACE+ is first performed to verify the accuracy of the numerical solutions, and two categories of test cases. i.e. single internal heat source (IHS) problems and multiple IHS problems, in determining the optimal SSF are examined in the present study. The estimated optimal SSF are then compared with the HSF given by Feng et al [23]. Results indicate that the optimal SSF can achieve lower maximum fin temperature than HSF.

摘要 I
英文延伸摘要 II
致謝 VI
目錄 VII
表目錄 IX
圖目錄 X
符號說明 XII
第一章 緒論 1
1-1 研究背景與目的 1
1-2 研究方法 2
1-3 文獻回顧 5
第二章 單熱源雪花形鰭片之最佳形狀設計 9
2-1 直接解問題 9
2-2 鰭片設計問題：獲得最小熱源中心溫度(或熱阻) 12
2-3 拉凡格氏法之極小化過程 14
2-4 數值計算流程 16
2-5 結果與討論 17
第三章 多熱源雪花形鰭片之最佳形狀設計 31
3-1 直接解問題 31
3-2 鰭片設計問題：獲得最小熱源中心溫度(或熱阻) 34
3-3 拉凡格氏法之極小化過程 35
3-4 數值計算流程 38
3-5 結果與討論 39
第四章 結論 55
參考文獻 57

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