Abstract Since the underwater impurity and cavitation could have a significant impact on the aerodynamic performance of tidal turbine blade airfoil, it is necessary to consider both roughness and cavitation in the process of optimization design. In the present study, a novel multi-objective evolution algorithm based on NSGA‐II is proposed for the design of tidal turbine blade airfoil, to overcome the adverse effects of cavitation and roughness. Non-uniform rational B-spline (NURBS) representation is adopted in the process of optimization; a new performance evaluation index of airfoil under rough condition is formed; multi-objective optimization was conducted for NACA2415 airfoil. The minimum pressure coefficient is the criterion used for the identification of cavitation on tidal turbine blade airfoil. The CFD and XFOIL are employed to calculate the lift drag coefficient and pressure coefficient of airfoil. The transition position of airfoil is predicted by the improved transition model. A comprehensive experimental study was conducted to evaluate the aerodynamic performance of optimized airfoil, revealing flow patterns such as flow separation over the initial airfoil and optimized airfoil through smoke flow experiment. In order to simulate the rough condition, sandpaper was pasted on the leading edge surface of airfoil and experiments indicated that location and formation of laminar separation bubble were affected . Numerical simulation and experimental results show that the optimized airfoil enjoys a better aerodynamic performance than the original ones under the roughness condition, in terms of cavitation inhibition. Within the range of design conditions, the lift drag ratio of optimized airfoils increased by an average of 20% and the minimum pressure coefficient peak decreased by about 17.2% on average. Thus,the proposed method can effectively guide the design and technical reserve of the tidal turbine.

中文翻译：

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考虑空化和粗糙度的潮汐涡轮叶片翼型性能优化与流动现象研究

摘要 由于水下杂质和气穴现象对潮汐涡轮叶片翼型的气动性能有显着影响，因此在优化设计过程中需要同时考虑粗糙度和气穴现象。在本研究中，提出了一种基于 NSGA-II 的新型多目标进化算法，用于潮汐涡轮叶片翼型的设计，以克服空化和粗糙度的不利影响。优化过程中采用非均匀有理B样条(NURBS)表示；形成了新的翼型在粗糙状态下的性能评价指标；NACA2415翼型进行了多目标优化。最小压力系数是用于识别潮汐涡轮叶片翼型空化的标准。采用CFD和XFOIL计算翼型升阻系数和压力系数。通过改进的过渡模型预测翼型的过渡位置。进行了全面的实验研究以评估优化翼型的空气动力学性能，通过烟流实验揭示了初始翼型上的流动分离和优化翼型等流动模式。为了模拟粗糙情况，在翼型前缘表面粘贴砂纸，实验表明层流分离气泡的位置和形成受到影响。数值模拟和实验结果表明，优化后的翼型在粗糙度条件下比原始翼型在气蚀抑制方面具有更好的气动性能。在设计工况范围内，优化翼型升阻比平均提高20%，最小压力系数峰值平均降低约17.2%。因此,所提出的方法可以有效地指导潮汐涡轮机的设计和技术储备。