本論文建構一套用於空冷散熱模組之熱管位置最佳化的設計輔助程式，其目的為於給定的設計規格下，藉由改變熱管於鰭片的位置來最小化散熱模組之熱阻，以降低散熱模組與晶片的接端溫度，使晶片能更穩定地運作。此設計輔助程式應用粒子群演算法處理最佳化問題，並整合有限差分法數值求解器及軸流扇流速模型以計算目標函數，即具不同熱管位置之散熱鰭片組的熱阻。關於數值方法的驗證，本研究提出的求解器對單一鰭片散熱問題，與商用軟體Ansys Fluent計算結果的最大差異為3.48%；而對於帶風扇鰭片組的散熱問題，在發熱率100W時與實驗值的誤差為-2.5%。此求解器簡化了鰭片的幾何形狀並使用二維計算網格，因此對同一散熱問題可較Fluent節省約80%的計算時間，使其更適合應用於需要評估大量設計參數的最佳化問題。在最佳化方法方面，於評比基因、粒子群以及生物地理等三種演算法後，得知粒子群演算法的極值搜尋能力為最佳。舉本研究探討的鰭片組為例，以規模60的族群執行粒子群演算法並迭代120次所得出之最佳熱管位置，相較於目前位置可使整體鰭片有效度由80.2%增加為86.0%，代表在不增加產品成本的情況下，鰭片組的熱阻得以降低7.6%，即晶片發熱率100W~150W時可降低其溫度1.2℃~1.8℃。

A new Particle Swarm Optimization (PSO) algorithm incorporated with a finite difference numerical solver was proposed to optimize the location of heatpipes in a stacked-fin heatsink, in order to decrease the thermal resistance of a fan-cooled fin array. With lower thermal resistance, the heatsink mounted on an electronic package can lead to lower junction temperature, increasing stability and life span of the electronics. The new coded numerical solver, which solves the simplified 2-D governing equations for the fins’ convective heat transfer rate, is applied to calculate the objective function of the optimization algorithm. To verify the numerical solver, its calculation results were first compared with these obtained from commercial CFD software Ansys Fluent, getting the maximum 3.48% difference for single fin cooling problems. Regarding a fin array cooling problem, its calculated outcome is merely 2.5% deviated from the experimental data at 100W heat load. This established solver benefits from solving mathematic model with simplified fin geometry and 2D structural mesh, which saves roughly 80% computation time against Fluent for the same fin cooling problem, and becomes a better choice for solving objective functions.
As for evaluating the optimization methods, PSO illustrates an outstanding performance comparing with Genetic Algorithm (GA) and Biogeography-Based Optimization (BBO) alogorithm. In the stacked-fin heatsink considered here, total fin efficiency is enhanced from 80.2% to 86.0% by optimizing the heatpipe location. Also, the thermal resistance of fin array is reduced by 7.6%, which enables the chip temperature to drop 1.2℃~1.8℃ between 100W~150W heat generation rate without extra product material cost. In conclusion, this research successfully combines the simplified finite-difference solver and the particle swarm optimization algorithm to construct an effective and time-saving tool for optimizing the thermal characteristics in the thermal management of elcectronic device.

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