現今人類社會使用的能源如電力，大部分都是來自化石燃料。 而這些不可再生能源除了在未來有可能會耗盡以外， 其伴隨使用時所產生的環境汙染， 使國際社會上意識到可再生能源的重要性。 渦流引致振動能源轉換器是海洋發電領域的一種技術。 在本研究中，基因演算法結合數值方法被成功地運用在決定渦流引致振動能源轉換器的位置，能源轉換器的擺放位置對於效率存在不可忽略的影響(例如:風場中的風機排列)。 因此，本研究利用基因演算法找出最佳擺放位置來提升能源轉換器陣列的效率。 然而，在各領域的最佳化研究中， 基因演算法被認為是強大且其優化結果能逼近全域最佳化的方法之一。 此外，本研究使用直接施力沉浸邊界法模擬流固耦合，此數值方法能有效模擬流體與固體之間的交互運動。 本研究應用此方法來模擬海洋中流體流經渦流引致振動能源轉換器陣列，透過基因演算法的演化結果得出最佳位置，渦流引致振動能源轉換器的排列能夠提升能源轉換器陣列輸出的效能，並且成功結合直接施力沉浸邊界法與基因演算法達到最佳化的目標

The energy used by human society today, such as electricity, most of it comes from fossil fuels. In addition to these non-renewable energy sources will be exhausted in the future, the environmental pollution generated when producing electricity, so the international community aware of the importance of renewable energy. Vortex-induced vibration (VIV) energy converter is a technology in ocean energy harvesting. In this study, genetic algorithm (GA) coupled with computational fluid dynamics (CFD) were successfully developed and utilized to determine the location of the vortex-induced vibration (VIV) energy converter, the location of the energy converter has a non-negligible effect on efficiency, such as the arrangement of wind turbines in the wind farm. Therefore, this study uses genetic algorithm to find the optimal location to improve the efficiency of the energy converter arrangement. However, in the optimization research in various fields, the genetic algorithm is considered to be one of the powerful and its optimization result can approach the global optimization. Moreover, this study uses the direct-forcing immersed boundary (DFIB) method to simulate fluid-solid interaction, this numerical method performs effectively for simulating the interaction between the fluid and solid. This study applied the coupled method to simulate the flow past through vortex-induced vibration (VIV) energy converter array, the optimal location was obtained through the evolution of genetic algorithm and successfully coupled the direct-forcing immersed boundary (DFIB) method and genetic algorithm to achieve the goal of optimization.

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