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.

主要研究者：Manuel，Lance / Basu，Sukanta提案编号：1336760 /1336304机构：德克萨斯大学奥斯汀分校/北卡罗来纳州州立大学标题：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力资源丰富的地区，如美国大平原，近年来风能的开发以迅猛的速度增长。从气候学上讲，该地区也是雷暴等恶劣天气事件常见的地区;最近事件中风力涡轮机的故障表明需要对相关瞬态流入条件的设计进行回顾性分析和重新评估。去见全国人民？到2030年，更多的风力涡轮机可能会被安置在大平原上，那里预计会发生极端天气事件;气候变化对这种事件的频率和严重程度的影响加剧了人们对今天的担忧？的涡轮机设计。该项目旨在开发先进的中尺度和大涡模拟（LES）程序，用于生成雷暴相关的流入风场，并通过各种数据流进行通知和验证，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将考虑约20个案例研究，代表2005-2012年期间在西德克萨斯州记录的恶劣天气事件。一个广泛的数据集，这些事件可用于验证的4-D中尺度LES流入领域，将开发。这些流场将用于单个实用规模风力涡轮机单元以及阵列上的载荷的气动弹性模拟。在第2阶段，PI将评估？工程学？基于较旧的现场活动的模拟模型（例如，尼姆罗德和JAWS）对基于物理和数据驱动的中尺度LES模型。本项目第3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载荷。这将需要两个合作研究小组之间的协调，这两个小组具有互补的、不重叠的专门知识。长期考虑涡轮机极限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。有了前几个阶段的入流建模，PI解决了涡轮机单元和阵列的生命周期性能。在20年的使用寿命中，预计涡轮机可能会经历几次极端天气事件。对于大平原地区的候选场地，PI将检查长期内涡轮机部件上的预期负荷分布，同时考虑气候变化的影响（例如雷暴频率和严重程度的变化），并根据气候模型数据和不确定性因素进行分析。因此，PI期望提供预期性能的图片（和不确定性）的涡轮机单位，因为他们被认为是在破坏的背景下对最终和疲劳极限状态。参与研究工作的学生将发展技能，统计建模，边界层气象学，湍流模拟，气动弹性和结构分析，和基于可靠性的风力涡轮机极端和疲劳极限状态的设计程序。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中将开发的分析。事实上，今天，在进行高级分析时，经常向西欧寻求咨询援助。PI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，以服务于这个经济活跃的行业。少数民族本科生将通过德克萨斯州研究经验（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，以及他们的工程师和PI及其学生之间的研究合作机会。K-12学生和教师参与计划STEM学习计划与科学之家计划在北卡罗来纳州州立大学。