Abstract Wave energy harvesting systems mostly have low power production capability because of unoptimized design of the system components. A bidirectional flow impulse-turbine used in such a system has efficiency less than 40%, and it is required to design the turbine for a higher efficiency. Present work finds an optimal design and shows design-variable sensitivity to the turbine efficiency. The problem is solved using numerical analysis technique. The flow through the turbine was analyzed by solving the Reynolds-averaged Navier-Stokes equations (RANSE). The design variables; namely number of rotor blades and number of guide vane, guide vane angle and guide vane profile were modified to maximize the turbine efficiency. Using the Latin hypercube sampling technique, sample points were selected from a design space defined by lower and upper limits of the variables. Then, several surrogates were constructed using the RANSE calculated results, and the turbine performance was optimized. The results show that guide vane angle is the most sensitive parameter, while the guide vane profile has negligible effect on efficiency. A hybrid genetic algorithm searched the optimal design point. The relative mean efficiency enhancement over a wide range of flow coefficient was approximately 24%, while it was 28% at maximum efficiency point.

中文翻译：

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一种用于振荡水柱收集波浪能的脉冲涡轮机的高效设计

摘要 波能收集系统大多由于系统组件的设计未优化而具有低功率生产能力。这种系统中使用的双向流动脉冲涡轮机的效率低于40%，需要设计更高效率的涡轮机。目前的工作找到了最佳设计，并显示了对涡轮效率的设计变量敏感性。该问题使用数值分析技术解决。通过求解雷诺平均 Navier-Stokes 方程 (RANSE) 来分析通过涡轮机的流量。设计变量；即转子叶片的数量和导向叶片的数量、导向叶片角度和导向叶片轮廓被修改以最大化涡轮效率。使用拉丁超立方体采样技术，样本点是从由变量的下限和上限定义的设计空间中选择的。然后，使用 RANSE 计算结果构建了几个代理，并对涡轮机性能进行了优化。结果表明，导叶角度是最敏感的参数，而导叶轮廓对效率的影响可以忽略不计。混合遗传算法搜索最优设计点。在较宽的流量系数范围内，相对平均效率提高约为 24%，而在最大效率点为 28%。混合遗传算法搜索最优设计点。在较宽的流量系数范围内，相对平均效率提高约为 24%，而在最大效率点为 28%。混合遗传算法搜索最优设计点。在较宽的流量系数范围内，相对平均效率提高约为 24%，而在最大效率点为 28%。