Collaborative Research: Integrated Wind Turbine Blade and Tower Health Monitoring and Failure Prognosis

本研究的总体目标是建立一种适用于风力机结构的概率结构健康监测和失效预测方法。具体地说，研究活动将验证用于复合材料损伤检测的现场传感技术，利用实验数据更新数值模型，并表征对风力涡轮机结构中关键部件故障预测的结构需求。这项研究将在纤维增强复合材料中嵌入能够进行空间应变传感的薄膜，以检测叶片结构中关键热点的局部损伤。仪表化比例风力涡轮机叶片将在实验室进行静态和动态负载配置测试。随后，将使用加州大学戴维斯分校现有的风力涡轮机试验台获得全尺寸现场数据。损伤估计将被用于更新基于有限元方法的结构的抗力模型。最后，失效预测将作为风险评估步骤，利用结构的整体振动来更新气动弹性分析模型，然后用于估计结构需求。这项研究将得出风险水平，为风力发电机组的维护提供合理的依据，提高结构安全性，减少停机时间，最终目标是降低风能成本。研究结果将有助于了解风力机的性能以及不同输入载荷对整个结构系统需求的影响。该方法可用于其他大型结构的失效预测。风力机是可持续能源生产的重要投资。大型和地理位置偏远的风电场设施需要有关单个涡轮机结构状况的可靠和可靠信息，以确保高效和安全的运行。该项目的成功完成将导致早期预警结构健康监测系统，当涡轮叶片的损坏构成结构故障风险时，该系统将向操作员发出警告，并以概率的形式量化故障风险。该项目整合并推进了复合材料结构、气动弹性结构相互作用理论、结构动力学和基于纳米技术的传感器应用等不同领域的研究。通过本项目的实施所获得的见解也将适用于其他工程系统在随机载荷作用下的失效预测。它将提供损害检测和风险分析之间的联系，为保护建筑物和公众免受危险的决策提供依据。通过将规模化风力涡轮机的设计和建造与本科机械工程Capstone设计课程相结合，还将产生更广泛的教育影响。也将从不同的校园团体中招募代表不足、女性和经济困难的学生，以参与并积极贡献这一项目。

The overarching goal of this research is to derive a probabilistic structural health monitoring and failure prognosis methodology that is applicable to wind turbine structures. Specifically, the research activities will validate an in situ sensing technology for damage detection in composite materials, utilize experimental data for updating numerical models, and characterize structural demand for failure prognosis of critical elements within wind turbine structures. The study will embed thin films capable of spatial strain sensing in fiber-reinforced composites for detecting localized damage at critical hotspots within the blade structure. Instrumented scaled wind turbine blades will be tested in the lab under static and dynamic load configurations. Subsequently, data from full-scale field will be obtained using an existing wind turbine test bed at the University of California-Davis campus. Damage estimates will be used to update the resistance model of the structure based on the finite element method. Finally, failure prognosis will be performed as a risk assessment step in which global vibrations of the structure are used to update aero-elastic analysis models and then used for estimating structural demand. This research will yield risk levels that will provide a rational basis for wind turbine maintenance, enhance structural safety, and reduce downtimes with ultimate goal of lowering cost of wind energy. The results will be useful for understanding wind turbine performance and the implications of varying input loads have on the demand on the entire structural system. The methodology developed can be applicable for failure prognosis of other large structures.Wind turbines represent an important investment in sustainable energy production. Large and geographically remote wind farm facilities require robust and reliable information regarding the condition of individual turbine structures to assure efficient and safe operation. Successful completion of this project will lead to early-warning structural health monitoring systems that will warn operators when damage to turbine blades poses a risk of structural failure, and quantifies failure risks in term of probabilities. This project integrates and advances disparate fields of composite structures, aero-elastic structure interaction theory, structural dynamics, and nanotechnology-based sensor application. Insights gained through the execution of this project will also be applicable for failure prognosis of other engineered systems subjected to random loadings. It will provide a link between damage detection and risk analysis that will provide a basis for decision making to protect structures and the public from danger. Educational broader impacts will also be achieved by integrating the design and construction of scaled wind turbines with the undergraduate mechanical engineering Capstone design courses. Underrepresented, female, and economically disadvantaged students will also be recruited from various campus groups for participating and actively contributing to this project.