由於能源短缺的問題日趨嚴重，因此再生能源的議題廣受到社會各界的重視，而在所有的再生能源當中，風能轉換器則受到了極大的關注。在本研究中，數值方法與基因演算法的結合被成功的運用在葉片的設計上，葉片的外形設計對於風能轉換器的效率存在著顯著的影響。因此，利用最佳化葉片斷面的外形來提升葉片對風車的輸出效能。另外，許多研究指出在眾多最佳化方法中，基因演算法是強而有力且全域最佳化的方法之一。實數型基因演算法可以大幅解決傳統二進位型編碼所造成染色體長度過長的缺陷，為了有效控制機翼外型的形狀，本研究運用PARSEC參數化方法的11個特徵參數來呈現機翼斷面外型。此外，直接施力沉浸邊界法為一有效模擬流固耦合運動的數值方法。本研究透過此方法來模擬流體流經旋轉的風車葉片，且運用光線投影法來捕捉隨著每個時間步旋轉的葉片位置，透過最佳化的演化結果得出，機翼外型的改變能夠提升整體輸出效能，且也顯示基因演算法與直接施力沉浸邊界法的成功結合能得到最佳化的目的。

Renewable energy is an important topic due to energy shortage. Especially wind energy converted by a wind turbine receives more attentions. In the present study, the blade design of the wind turbine using a couple method with computational fluid dynamics (CFD) and genetic algorithm (GA) is discussed. The blade shape is the most significant effective factor in the wind energy conversion. Hence, we dedicated to utilizing an optimal method for the cross-section of blade, i.e., an airfoil, in order to get the better efficiency for producing the higher lift and lower drag to drive the wind turbine. According to the previous study, the Genetic Algorithm (GA) is known to be the robust method in the optimal design area. The real-coded Genetic algorithm is considered since it is able to solve the defect of binary code. That is, the chromosomes length is too long to code. While the PARSEC parameterization method is used to represent the shape of airfoil through the eleven parameters as the control variables. Furthermore, a direct-forcing immersed boundary (DFIB) method is employed for simulations of interaction of rotating blades in a flow field at a moderate low Reynolds number. Numerical results reveal that the shape of airfoil can be optimized and the proposed DFIB model coupled with GA successfully simulates the moving blade in flow field for obtaining the high performance.

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