Collaborative Research: A retrospective assessment and future projection of thunderstorm impacts on the field performance of wind turbines

合作研究：雷暴对风力涡轮机现场性能影响的回顾性评估和未来预测

• 批准号: 1336304
• 负责人: Sukanta Basu
• 金额: $ 19.98万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-15 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1336304&HistoricalAwards=false
• 关键词: Collaborative; Research; retrospective; assessment; future

PI: Manuel, Lance / Basu, SukantaProposal Number: 1336760 / 1336304Institution: University of Texas at Austin / North Carolina State UniversityTitle: Collaborative Research: A retrospective assessment and future projection of thunderstorm impacts on the field performance of wind turbinesIn wind resource-rich regions such as the U.S. Great Plains, wind energy development in recent years has increased at a brisk rate in recent years. Climatologically, this region is also where severe weather events such as thunderstorms are common; failures of wind turbines in recent events suggest a need for retrospective analysis and re-evaluation of design for associated transient inflow conditions. To meet the nation?s goals of 20% wind energy by 2030 more wind turbines will likely be sited in the Great Plains where failures in extreme weather events are expected; suggested climate change influences on the changing frequency and severity of such events exacerbates concerns about today?s turbine designs.This project seeks to develop advanced mesoscale and large-eddy-simulation (LES) procedures for the generation of thunderstorm-related inflow wind fields, informed and validated by diverse data streams, so as to make possible safe design and assessment of future wind turbines.This project is structured in four phases. In Phase 1, PIs will consider about 20 case studies representing severe weather events recorded in West Texas over the period, 2005-2012. A wide array of data sets for these events is available for validation of the 4-D mesoscale-LES inflow fields that will be developed. These flow fields will be employed in aeroelastic simulation of loads on single utility-scale wind turbine units as well as arrays. In Phase 2, PIs will assess the validity of ?engineering? simulation models based on older field campaigns (e.g., NIMROD and JAWS) against physics-based and data-driven mesoscale-LES models. The focus in Phase 3 of this project is on the development of low-dimensional representations of the inflow wind fields to be used to predict turbine loads accurately and efficiently. This will take coordination between the two collaborating researcher teams with complementary non-overlapping expertise. Consideration of turbine extreme and fatigue limit states over the long term will guide the low- dimensional model development. Finally, in Phase 4, PIs tackle the future projection component of this study. Armed with the inflow modeling from the previous phases, PIs address life-cycle performance of turbine units and arrays. Over a service life of 20 years, a turbine may be expected to experience several extreme weather events. For candidate Great Plains sites, PIs will examine anticipated load profiles on turbine components over the long term, taking into consideration changing climate effects (such as in changes in the frequency and severity of thunderstorms), informed by climate model data and with considerations for uncertainty. PIs expect, thus, to provide a picture of expected performance (and uncertainty on the same) of turbine units as they are considered in the context of failure against ultimate and fatigue limit states.The students involved in the research effort will develop skills in statistical modeling, boundary layer meteorology, turbulence simulation, aeroelastic and structural analysis, and reliability-based design procedures for wind turbines against extreme and fatigue limit states. While these skills are not part of a typical curriculum, there is a dire need for trained engineers who can assist with analyses of the kind that will be developed in this project. Indeed, today, for advanced analysis, consulting assistance is routinely sought from Western Europe. The PIs will provide education and training/internship opportunities for graduate and undergraduate students of engineering and atmospheric sciences to serve this economically vibrant industry. Minority undergraduate students will be engaged as research assistants through the Texas Research EXperience (TREX) program. The National Renewable Energy Laboratory will provide summer internship opportunities for students, and collaboration opportunities in research between their engineers and the PIs and their students. K-12 student and teacher involvement in planned STEM learning is planned with the Science House program at North Carolina State University.

项目负责人：Manuel， Lance / Basu， sukant提案编号：1336760 / 1336304机构：德克萨斯大学奥斯汀分校/北卡罗莱纳州立大学标题：合作研究：雷暴对风力涡轮机现场性能影响的回顾性评估和未来预测在风能资源丰富的地区，如美国大平原，风能开发近年来以迅猛的速度增长。气候学上，该地区也是雷暴等恶劣天气事件频发的地区；在最近的事件中，风力涡轮机的故障表明需要对相关的瞬态流入条件进行回顾性分析和重新评估设计。与全国人民见面？美国的目标是到2030年风能占比达到20%，更多的风力涡轮机可能会部署在大平原地区，因为极端天气事件可能会导致故障；气候变化对此类事件发生频率和严重程度的影响加剧了人们对今天的担忧？S涡轮设计。该项目旨在开发先进的中尺度和大涡模拟（LES）程序，以产生与雷暴有关的入流风场，并通过各种数据流获得信息和验证，从而使未来风力涡轮机的安全设计和评估成为可能。该项目分为四个阶段。在第一阶段，pi将考虑大约20个案例研究，这些案例代表了2005-2012年期间西德克萨斯州记录的恶劣天气事件。这些事件的大量数据集可用于验证将开发的4-D中尺度les流入场。这些流场将用于单个公用事业规模的风力发电机组以及阵列的气动弹性载荷模拟。在第二阶段，项目负责人将评估工程的有效性。基于旧的野外活动（例如NIMROD和JAWS）的模拟模型与基于物理和数据驱动的中尺度les模型。该项目第三阶段的重点是开发流入风场的低维表示，用于准确有效地预测涡轮机负荷。这将需要两个具有互补的非重叠专业知识的合作研究团队之间的协调。考虑涡轮的长期极限状态和疲劳极限状态将指导低维模型的开发。最后，在第4阶段，pi处理本研究的未来预测部分。有了上一阶段的流入模型，pi解决了涡轮机组和阵列的生命周期性能。在20年的使用寿命中，涡轮机可能会经历几次极端天气事件。对于候选的大平原站点，pi将考虑气候变化的影响（如雷暴频率和严重程度的变化），并考虑气候模型数据和不确定性，检查涡轮机部件的长期预期负荷分布。因此，当考虑到涡轮机组在极限和疲劳极限状态下的失效时，pi期望提供涡轮机组的预期性能（以及同样的不确定性）的图片。参与研究工作的学生将掌握统计建模、边界层气象学、湍流模拟、气动弹性和结构分析以及风力涡轮机抗极端和疲劳极限状态可靠性设计程序等方面的技能。虽然这些技能不是典型课程的一部分，但迫切需要训练有素的工程师来协助本项目中将要开发的那种分析。事实上，今天，为了进行高级分析，经常向西欧寻求咨询援助。这些项目将为工程和大气科学专业的研究生和本科生提供教育和培训/实习机会，以服务于这个充满经济活力的行业。少数民族本科生将通过德克萨斯研究经验（TREX）计划担任研究助理。国家可再生能源实验室将为学生提供暑期实习机会，并为他们的工程师和pi及其学生提供研究合作机会。北卡罗莱纳州立大学的科学之家项目计划K-12学生和教师参与计划中的STEM学习。