当前位置: X-MOL 学术Wind Energy › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Machine learnt prediction method for rain erosion damage on wind turbine blades
Wind Energy ( IF 4.1 ) Pub Date : 2021-01-05 , DOI: 10.1002/we.2609
Alessio Castorrini 1 , Paolo Venturini 2 , Alessandro Corsini 2 , Franco Rispoli 2
Affiliation  

This paper proposes a paradigm shift in the numerical simulation approach to predict rain erosion damage on wind turbine blades, given the blade geometry, its coating material, and the atmospheric conditions (wind and rain) expected at the installation site. Contrary to what has been done so far, numerical simulations (flow field and particle tracking) are used not to study a specific (wind and rain) operating condition but to build a large database of possible operating conditions of the blade section. A machine learning algorithm, trained on this database, defines a prediction module that gives the feature of the impact pattern over the 2-D section, given the wind and rain flow. The advantage of this approach is that the prediction becomes much faster than using the standard simulations; thus, the study of a large set of variable operating conditions becomes possible. The module, coupled with an erosion model, is used to compute the erosion damage of the blade working on specific installation site. In this way, the variations of the flow conditions due to dynamic effects such as variable wind, wind turbulence, and turbine control can be also considered in the erosion computation. Here, we describe the method, the database creation, and the development of the prediction tool. Then, the method is applied to predict the erosion damage on a blade section of a reference wind turbine, after one year of operation in a rainy onshore site. Results are in good agreement with on field observations, showing the potential of the approach.

中文翻译:

风力机叶片雨蚀损伤的机器学习预测方法

本文提出了数值模拟方法的范式转变,以预测风力涡轮机叶片上的雨水侵蚀损坏,考虑到叶片几何形状、涂层材料以及安装地点预期的大气条件(风和雨)。与迄今为止所做的相反,数值模拟(流场和粒子跟踪)不是用来研究特定(风和雨)运行条件,而是用于构建叶片部分可能运行条件的大型数据库。在该数据库上训练的机器学习算法定义了一个预测模块,该模块给出了在给定风雨流的情况下二维截面上的撞击模式的特征。这种方法的优点是预测变得比使用标准模拟快得多;因此,研究大量可变操作条件成为可能。该模块与侵蚀模型相结合,用于计算叶片在特定安装地点工作的侵蚀损伤。这样,在侵蚀计算中也可以考虑由于变风、风湍流和涡轮机控制等动态效应引起的流动条件的变化。在这里,我们描述了预测工具的方法、数据库创建和开发。然后,在多雨的陆上场地运行一年后,该方法被应用于预测参考风力涡轮机叶片部分的侵蚀损坏。结果与实地观察非常吻合,显示了该方法的潜力。用于计算叶片在特定安装地点工作的侵蚀损伤。这样,在侵蚀计算中也可以考虑由于变风、风湍流和涡轮机控制等动态效应引起的流动条件的变化。在这里,我们描述了预测工具的方法、数据库创建和开发。然后,在多雨的陆上场地运行一年后,该方法被应用于预测参考风力涡轮机叶片部分的侵蚀损坏。结果与实地观察非常吻合,显示了该方法的潜力。用于计算叶片在特定安装地点工作的侵蚀损伤。这样,在侵蚀计算中也可以考虑由于变风、风湍流和涡轮机控制等动态效应引起的流动条件的变化。在这里,我们描述了预测工具的方法、数据库创建和开发。然后,在多雨的陆上场地运行一年后,该方法被应用于预测参考风力涡轮机叶片部分的侵蚀损坏。结果与实地观察非常吻合,显示了该方法的潜力。在这里,我们描述了预测工具的方法、数据库创建和开发。然后,在多雨的陆上场地运行一年后,该方法被应用于预测参考风力涡轮机叶片部分的侵蚀损坏。结果与实地观察非常吻合,显示了该方法的潜力。在这里,我们描述了预测工具的方法、数据库创建和开发。然后,在多雨的陆上场地运行一年后,该方法被应用于预测参考风力涡轮机叶片部分的侵蚀损坏。结果与实地观察非常吻合,显示了该方法的潜力。
更新日期:2021-01-05
down
wechat
bug