在本研究中，開發了一種用於提高垂直式風力渦輪機（VAWT）性能的導流板，並使用田口方法和神經網絡來預測參數的最佳組合。另外，也討論了不穩定風速下對VAWT的影響。因此，本研究分為兩個部分，如下所述。
在本研究中的第一部分，設計了一個導流板，通過田口方法結合改進的加法模型（MAM）來提高VAWT的性能。分別考慮兩個相對位置的導流板，即上導流板和下導流板。方差分析（ANOVA）用於評估設計因子對性能的影響，並透過計算流體力學分析VAWT葉片周圍的空氣動力學特性。模擬結果顯示，沒有導流板的VAWT的平均功率係數（(C_p ) ̅）為0.46。使用MAM的上導流板設計，最高的平均功率係數可以提升到0.55。使用MAM的下導流板設計，最高的平均功率係數可以提升到0.54。與沒有導流板的VAWT相比，平均功率係數分別提升了20%和17%。MAM的分析結果顯示，VAWT中心到上導流板底部的距離是在上導流板設計中影響平均功率係數最重要的因子。VAWT中心到下導流板底部的距離是在下導流板設計中影響平均功率係數最重要的因子。其中，要考慮因子之間的相互作用以提供準確的最佳化結果。方差分析的結果與田口方法的結果一致，並確認正確的安裝導流板可以有效地提升VAWT的性能。
在本研究中的第二部分，非穩態風條件下的最佳葉尖速比（TSR）對於提高VAWT的功率係數非常重要。在本研究中，利用CFD模擬結果的數據結合高擬合的神經網絡(NN)模型來預測VAWT的最佳運行條件（平均TSR）。其中平均入口速度的幅度和頻率波動用於描述非穩態風速風條件。結果表明，在非穩態風速風條件下會降低VAWT的平均功率係數。通過田口方法和神經網絡對非穩態風速風條件影響的分析，發現平均TSR是運行條件中影響平均功率係數最重要的因子。結果顯示，由NN模型評估的平均TSR有利於VAWT在非穩態風條件下的運行。

In this research, a deflector to enhance the performance of a three-bladed vertical axis wind turbine (VAWT) was developed, and then using the Taguchi method and neural network to predict the best combination of parameters. In addition, the influence of unsteady wind conditions on VAWT was also discussed. Therefore, this study is divided into two parts, as described below.
In the first part of this study, designed a deflector to enhance the performance of a three-bladed vertical axis wind turbine (VAWT) through the Taguchi method in association with a modified additive model (MAM). Two types of the deflector, namely, an upper deflector and a lower deflector, are individually considered. Analysis of variance (ANOVA) is applied to evaluate the influences of design factors on the performance, and the aerodynamic characteristics around the turbine blades are explored. The prediction suggests that the average power coefficients ((C_p ) ̅) of the VAWT without deflector is 0.46. For the upper deflector design, the (C_p ) ̅ value using MAM can be raised to the highest value of 0.55, while the (C_p ) ̅ value for the lower deflector design is lifted up to 0.54, accounting for 20% and 17% improvements compared with the VAWT without a deflector. The application of MAM shows that the distances from the center of the turbine to the bottom of the upper deflector and the top of the lower deflector are the most important factors to influence (C_p ) ̅, and the interaction between the factors should be considered to provide accurate optimization results. The results of the ANOVA analysis are coincident with those of the Taguchi approach and confirm that a proper installation of a deflector can efficiently intensify the VAWT’s performance.
In the second part of this study, an optimal tip speed ratio (TSR) under unsteady wind condition is very important to improve the power coefficient of VAWT. In the present study, a high-fitting neural network (NN) model based on computational fluid dynamics (CFD) data is adopted to predict the optimal mean TSR for VAWT operation. The amplitude and frequency fluctuations of the mean inlet velocity are used to specify the unsteady wind conditions. The results show that the imposed unsteady wind exhibits to reduce the average power coefficient (C_p ) ̅ of VAWT. By perofrming Taguchi method and NN analyses for the impact of unsteady wind conditions, it is found that the mean TSR, indicated by TSRmean, is the factor which produces the greatest impact on (C_p ) ̅. The optimal TSRmean is evaluated by the NN model and the results benefit the operation of VAWT under unsteady wind conditions.

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