Identification methods for offshore wind turbine structures and foundations

海上风机结构及基础识别方法

• 批准号: 2760401
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2760401
• 关键词: Identification; methods; offshore; wind; turbine

There is a lot of uncertainty around modelling the foundations of offshore wind turbines under complex loading regimes. As a result, the design of offshore wind turbine foundations are often conservative leading to excess costs. However, relatively mature methods from the field of structural health monitoring and machine learning offer the ability to provide accurate feedback on the efficacy and shortcomings of offshore wind foundation and structural modelling, leading to cost reduction in design, operation and life extension. Research is therefore required to deliver and validate data driven methods for OWF structural property estimation, which can inform and be embedded within industrial foundation and structural design processes.The key research questions are: How can system identification methods be applied to deliver accurate estimation of design critical offshore wind foundation and structural properties? How are these methods, including the algorithms and sensing systems, best applied to field monitored data? How can the estimated properties be utilised to deliver feedback to the design, maintenance and lifetime reassessment of offshore windfarms? How might sensor systems be optimally designed to provide data for structural assessment or design method development.

在复杂的载荷条件下，对海上风力涡轮机的基础进行建模存在很多不确定性。因此，海上风力涡轮机基础的设计通常是保守的，导致成本过高。然而，来自结构健康监测和机器学习领域的相对成熟的方法提供了对海上风力基础和结构建模的功效和缺点提供准确反馈的能力，从而降低了设计，运营和寿命延长的成本。因此，需要研究提供和验证数据驱动的方法OWF结构性能估计，这可以通知和嵌入在工业基础和结构设计流程。关键的研究问题是：如何系统识别方法可以应用于提供设计关键海上风电基础和结构性能的准确估计？这些方法，包括算法和传感系统，如何最好地应用于现场监测数据？如何利用估算的特性为海上风电场的设计、维护和寿命重新评估提供反馈？如何优化设计传感器系统，为结构评估或设计方法开发提供数据。