EAPSI: Damage detection and performance assessment of wind turbine blades

EAPSI：风力涡轮机叶片的损伤检测和性能评估

• 批准号: 1415095
• 负责人: Yingjun Zhao
• 金额: $ 0.51万
• 依托单位: Zhao Yingjun
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1415095&HistoricalAwards=false
• 关键词: EAPSI; Damage; detection; performance; assessment

Current global competition for fossil fuels has motivated the prosperity of sustainable energy infrastructures such as wind turbine mills. Wind turbine blades, usually assembled by composite materials, are one of the most vulnerable structures due to their growing self-weight and harsh operational environment. Skyscraping maintenance cost spent on condition assessment through a turbine's service life has caused wind energy to be less cost-effective comparing to conventional resources. Reliable approaches are currently sought to assess turbine blade's structural performance without high expenditure. Considering that most structural failures are initiated from early material deteriorations such as cracks or corrosion, an in situ structural health monitoring system may be employed to alert early signs of material worn-outs, preventing catastrophic structural failures possibly taking place in the future. In collaboration with Dr. Yuan-Sen Yang at the National Taipei University of Technology in Taiwan, this projects aims to employ emerging damage sensing technologies to perform damage assessment of lab-scaled wind turbine blades. Dr. Yang's lab will provide unique dynamic testing facility for lab-scaled turbine blades, and expertise on dynamic characterization of turbine blade performance.A self-assembled, carbon nanotube-based nanocomposite material is employed as the strain sensor, whose damage sensing capability has been verified on composite material coupons. It will be customized to perform spatial damage detection for lab-scaled wind turbine blades. Structural dynamic responses of the same blades under rotational motion will be taken via the digital image correlation technique - a non-contact motion tracking method - through utilization of a high-speed video camera. A computational model based on both measurements will be established to simulate multidimensional damage aspects of a turbine blade under different operational conditions, thereby providing all-around performance assessment and predictions of the wind turbine's structural conditions. This NSF EAPSI award is funded in collaboration with the National Science Council of Taiwan.

当前全球对化石燃料的竞争已经推动了诸如风力涡轮机米尔斯之类的可持续能源基础设施的繁荣。风力涡轮机叶片通常由复合材料组装而成，由于其自重的增加和恶劣的运行环境，叶片是最脆弱的结构之一。在涡轮机的使用寿命期间，用于状态评估的高昂维护成本已经导致风能与常规资源相比成本效益较低。目前正在寻求可靠的方法来评估涡轮机叶片的结构性能，而不需要很高的费用。考虑到大多数结构失效是由早期材料劣化（如裂纹或腐蚀）引发的，可以采用原位结构健康监测系统来预警材料磨损的早期迹象，以防止将来可能发生的灾难性结构失效。该项目与台湾国立台北科技大学杨元森博士合作，旨在采用新兴的损伤传感技术对实验室规模的风力涡轮机叶片进行损伤评估。杨博士的实验室将为实验室规模的涡轮机叶片提供独特的动态测试设备，并在涡轮机叶片性能的动态表征方面提供专业知识。应变传感器采用自组装的碳纳米管基纳米复合材料，其损伤传感能力已在复合材料试样上得到验证。它将被定制为实验室规模的风力涡轮机叶片进行空间损伤检测。旋转运动下相同叶片的结构动态响应将通过数字图像相关技术-一种非接触式运动跟踪方法-通过利用高速摄像机进行拍摄。将建立基于这两种测量的计算模型，以模拟涡轮机叶片在不同运行条件下的多维损伤方面，从而提供风力涡轮机的结构条件的全面性能评估和预测。这个NSF EAPSI奖是与台湾国家科学理事会合作资助的。