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

本研究的首要目标是推导出适用于风力涡轮机结构的概率结构健康监测和故障预测方法。具体而言，研究活动将验证复合材料损伤检测的原位传感技术，利用实验数据更新数值模型，并表征风力涡轮机结构内关键元件故障预测的结构需求。该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，以检测叶片结构内关键热点处的局部损伤。 将在静态和动态载荷配置下，在实验室中对仪表化缩放风力涡轮机叶片进行测试。随后，将使用加利福尼亚大学戴维斯分校现有的风力涡轮机试验台获得全尺寸场的数据。损伤估计将用于基于有限元法更新结构的阻力模型。最后，故障预测将作为风险评估步骤进行，其中使用结构的全局振动来更新气动弹性分析模型，然后用于估计结构需求。本研究将得出风险等级，为风力涡轮机维护提供合理依据，提高结构安全性，减少停机时间，最终降低风能成本。研究结果将有助于理解风力涡轮机的性能和不同输入载荷对整个结构系统的影响。所开发的方法可以适用于其他大型结构的故障预测。风力涡轮机代表了可持续能源生产的重要投资。大型且地理位置偏远的风电场设施需要关于单个涡轮机结构的状况的鲁棒且可靠的信息，以确保高效且安全的操作。该项目的成功完成将导致早期预警结构健康监测系统，该系统将在涡轮机叶片损坏造成结构故障风险时警告操作员，并根据概率量化故障风险。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的不同领域。通过执行本项目获得的见解也将适用于其他工程系统的故障预测受到随机载荷。它将在损坏检测和风险分析之间建立联系，为保护结构和公众免受危险的决策提供依据。教育更广泛的影响也将通过整合设计和建设规模的风力涡轮机与本科机械工程顶点设计课程。代表性不足，女性和经济上处于不利地位的学生也将从各个校园团体招募参与并积极为这个项目做出贡献。

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.