本文探討一具有三維入口流道及圓弧形側流道壁的微光流體分光器，它會在主流道內產生漸進式的折射率梯度，引導進入主流道的光線往兩側前進，達到分光的功能。本研究使用氯化鈣水溶液(A，核心流體)及去離子水(B，包覆流體)作為流道內的工作流體，將流體由左右對稱的六個入口注入流道內，其中外圍的四個入口注入的是去離子水，包圍由中央注入口注入的氯化鈣水溶液，在主流道匯流後包覆流體會包覆住核心流體，形成中間高兩邊低的折射率分佈，由於注入口為左右對稱，進入主流道的光線會朝兩側高折射率區域前進，達到分光的效果，同時可以由控制入口流率的方式，改變流道內的折射率分佈，使進入流道內的光線產生不同的分光角。本研究使用ANSYS Fluent數質模擬軟體模擬流道內的速度場和濃度場，再以自行寫作的C++程式進行光追跡，模擬光線穿過流道時的軌跡。接著對流道的幾何以及流率參數進行最佳化，選定的7個參數如下，流體A入口流道寬度 、深度 、後段主流道長度 及A、B流體匯流處與主流道圓弧形流道壁端點距離 、入口總流率 、B流體1號入口和B流體流率的流率比 及A流體流率和總流率的流率比 ，先以田口法評估各項參數對分光器分光角和傳輸效率的影響程度，在經過程式模擬後，發現流率參數對表現的影響較大，並且各項參數對於分光角和傳輸效率的影響為相反，因此接著以基因演算法進行多目標最佳化，尋求最佳的參數組合，在以模擬驗證基因演算法的結果後，確認基因演算法能夠取得優於田口法的最佳化結果。

In this work, we investigate a micro optofluidics beam splitter, which forms gradient refractive index (GRIN) inside main channel to split the incident beam into two beams. The beam splitter consists of inlet channel, main channel with curved wall and outlet channel. Calcium chloride solution ( ) and deionized water( ) are used as the core and the cladding liquid, respectively. They are pumped into the channel through inlet channels with unequal height, so the cladding liquid would wrap around the core liquid to form GRIN in the main channel, which will split and focus the incident beams into two beams. ANSYS Fluent is used to simulate the concentration field inside the channel and Self-developed ray tracing code is employed to simulate light propagation. Different geometry and flow parameters will affect the transmission efficiency and the split angle. Genetic algorithm and the Taguchi method are used to optimize these parameters to achieve big split angle with good transmission. Response surface method (RSM) and radial basis function (RBF) are used to create surrogate model for genetic algorithm optimization. We find the following trends from simulation results. (i) The inlet channel depth ratio has great influence on both split angle and transmission efficiency of the beam splitter. (ii) Flow rate parameters have greater influence on the performance of the beam splitter than geometry parameter. (iii) The RBF method combined with genetic algorithm generates better optimization result than taguchi method does.

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