Advanced Non-Destructive Testing of Blade Manufacturing Defects

叶片制造缺陷的先进无损检测

• 批准号: 2128243
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2128243
• 关键词: Advanced; Non; Destructive; Testing; Blade

Wind turbine blades are one of the most costly and complicated components of the wind turbine. Blades are now approaching 100m in length and are manufactured using carbon and/or glass fibre reinforced plastic composites. Non-destructive testing (NDT) methods are utilized to identify defects so to reduce operational maintenance costs and extend lifetimes.Ultrasonic testing techniques are the most suitable NDT method with the data/image analysis typically performed by trained NDT experts. The aerospace industry now employs improved, contactless scanning methods, coupled with detection algorithms and machine-learning techniques. The application of similar methods to large wind turbine blades will enable more efficient and cost-effective inspections resulting in overall benefits for clean energy production. A review of current ultrasonic NDT techniques for composite materials will be conducted and the most applicable for the wind industry identified. These will be tested on small blade samples, provided by industrial partner Siemens Gamesa Renewable Energy. Following on from this, processes are to be up-scaled with the aim of developing mature and fully automated inspection, and image analysis, techniques used in large-scale blade manufacturing.

风力机叶片是风力机最昂贵、最复杂的部件之一。叶片长度现在接近100米，使用碳和/或玻璃纤维增强塑料复合材料制造。无损检测(NDT)方法被用来识别缺陷，以降低运行维护成本和延长使用寿命。超声波检测技术是最合适的无损检测方法，通常由训练有素的无损检测专家进行数据/图像分析。航空航天工业现在采用改进的非接触式扫描方法，再加上检测算法和机器学习技术。将类似方法应用于大型风力涡轮机叶片，将使检查更有效、更具成本效益，从而为清洁能源生产带来整体效益。将对目前复合材料的超声无损检测技术进行回顾，并确定最适用于风电行业的技术。这些将在工业合作伙伴西门子Gamesa可再生能源提供的小叶片样本上进行测试。在此之后，将扩大工艺规模，目标是开发用于大规模叶片制造的成熟和完全自动化的检测和图像分析技术。