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.

Pi：Manuel，Lance/Basu，SukantaProposal编号：1336760/1336304机构：德克萨斯大学奥斯汀分校/北卡罗来纳州立大学标题：合作研究：雷暴对风力涡轮机现场性能影响的回顾评估和未来预测在风资源丰富的地区，如美国大平原，近年来风能开发以迅猛的速度增长。在气候方面，该地区也是雷暴等恶劣天气事件常见的地区；最近发生的风力涡轮机故障表明，有必要对相关瞬时流入条件的设计进行回顾分析和重新评估。为了达到这个国家的目标？S的目标是到2030年，更多的风力涡轮机可能安装在预计会出现极端天气事件故障的大平原上；气候变化对此类事件的变化频率和严重程度的影响加剧了人们对今天的担忧？S涡轮机设计。该项目旨在开发高级中尺度和大涡模拟(LES)程序，以生成与雷暴相关的流入风场，并通过不同的数据流进行信息和验证，从而使未来的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，公共关系督察将考虑约20个案例研究，这些案例代表了2005-2012年期间在德克萨斯州西部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中尺度流入场。这些流场将被用于单个公用事业规模的风力发电机组和阵列上的气动弹性载荷模拟。在第二阶段，绩效指标将评估工程的有效性。基于较老的现场战役(如Nimrod和JAWS)的模拟模型，与基于物理和数据驱动的中尺度模型相比较。该项目第三阶段的重点是开发用于准确和高效地预测涡轮机负荷的流入风场的低维表示法。这将需要两个合作的研究团队之间的协调，这些团队具有互补的、不重叠的专业知识。长期考虑涡轮的极限状态和疲劳极限状态将指导低维模型的开发。最后，在第四阶段，私人投资总监处理这项研究的未来预测部分。在前几个阶段的流入建模的基础上，PI解决了汽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。对于大平原候选地点，绩效指标将考虑气候变化的影响(如雷暴频率和严重程度的变化)、气候模型数据提供的信息以及不确定性的考虑，检查长期内涡轮机部件的预期负荷分布。因此，PIS期望提供在针对极限和疲劳极限状态的故障情况下考虑的涡轮机组的预期性能(以及相同的不确定性)的图景。参与研究工作的学生将发展统计建模、边界层气象学、湍流模拟、气动弹性和结构分析以及针对极端和疲劳极限状态的风力涡轮机的基于可靠性的设计程序的技能。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，他们可以协助进行本项目将开发的那种分析。事实上，今天，为了进行高级分析，人们通常会向西欧寻求咨询帮助。个人投资促进计划将为工程和大气科学的研究生和本科生提供教育和培训/实习机会，为这个经济活力十足的行业服务。少数族裔本科生将通过德克萨斯研究体验(TREX)计划被聘为研究助理。国家可再生能源实验室将为学生提供暑期实习机会，以及他们的工程师与PI及其学生在研究方面的合作机会。北卡罗来纳州立大学的科学之家项目计划让K-12学生和教师参与计划中的STEM学习。