基于径向基神经网络-遗传算法的海流能水轮机叶片翼型优化

如何提高海流能水轮机的能量捕获效率是海洋能开发领域的重点研究课题,而提高海流能水轮机能量性能的关键在于其叶片几何的构造基础--水力翼型的性能提升。该文提出了一种水力翼型的多工况优化设计方法,该方法采用Bezier曲线参数化技术建立翼型的参数化表征方法,然后利用拉丁超立方试验设计技术在设计空间获取训练径向基(radial basis function,RBF)神经网络的样本点,通过计算流体动力学的方法获得每个翼型样本的性能参数后开展神经网络的学习训练,最后采用RBF神经网络与NSGA-II遗传算法相结合的现代优化技术数值求解水力翼型的多工况优化问题。基于上述优化方法对NACA63-815翼型进行...

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Bibliographic Details
Published in农业工程学报 Vol. 30; no. 8; pp. 65 - 73
Main Author 朱国俊 冯建军 郭鹏程 罗兴锜
Format Journal Article
LanguageChinese
Published 西安理工大学水利水电学院,西安,710048 2014
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Summary:如何提高海流能水轮机的能量捕获效率是海洋能开发领域的重点研究课题,而提高海流能水轮机能量性能的关键在于其叶片几何的构造基础--水力翼型的性能提升。该文提出了一种水力翼型的多工况优化设计方法,该方法采用Bezier曲线参数化技术建立翼型的参数化表征方法,然后利用拉丁超立方试验设计技术在设计空间获取训练径向基(radial basis function,RBF)神经网络的样本点,通过计算流体动力学的方法获得每个翼型样本的性能参数后开展神经网络的学习训练,最后采用RBF神经网络与NSGA-II遗传算法相结合的现代优化技术数值求解水力翼型的多工况优化问题。基于上述优化方法对NACA63-815翼型进行了优化改进,重点研究了该翼型在3个攻角工况下(0,6°和12°)的优化问题及求解方法。优化结果表明,优化后的翼型在3个工况点下都具有更好的升阻比性能,同时也能更好地抑制失速现象的产生,验证了该优化方法的理论正确性和可行性。
Bibliography:neural networks;turbines;genetic algorithms;optimization;hydrofoil of turbine blade;multi-point optimization
11-2047/S
Zhu Guojun, Feng Jianjun, Guo Pengcheng, Luo Xingqi (School of Water Resources and Hydro-electric Engineering, Xi 'an University of Technology, Xi 'an 710048, China)
In order to reduce the current dependence on fossil and nuclear-fueled power plants to cope with the growing demand of electrical energy, the ocean energy technologies must be improved to develop more energy. There are several types of ocean energy that can be feasible to exploit:wave energy, marine-current energy, tidal barrages, ocean thermal energy and so on. But the most promising in the short term may be wave and marine-current energy. Marine-current energy can be exploited by a marine current turbine. So how to improve the efficiency of mariner current turbine is the key research subject in ocean energy development. The key to efficiency improvement is the performance improvement of hydrofoil, which is used to establish the tu
ISSN:1002-6819
DOI:10.3969/j.issn.1002-6819.2014.08.008