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U.S.-Germany Planning Visit: Structural Health Monitoring Sensors for Offshore Wind Turbines

U.S.-Germany Planning Visit: Structural Health Monitoring Sensors for Offshore Wind Turbines
美德计划访问:海上风力发电机结构健康监测传感器
批准号:
0820607
负责人:
Jerome Lynch
金额:
$0.76万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2008
资助国家:
美国
项目状态:
已结题
起止时间:
2008-04-01 至 2009-03-31

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中文摘要
翻译
OISE-0820607美国-德国规划访问:海上风力涡轮机结构健康监测传感器该项目支持密歇根大学土木与环境工程系的杰罗姆林奇博士、加尔维斯顿得克萨斯农工大学海事系统工程系的约翰斯威特曼博士&和德国汉诺威大学结构分析研究所的雷蒙德罗尔夫斯博士之间的合作。 该提案及其合作伙伴0813764将资助Lynch博士、Sweetman博士和密歇根大学的两名学生对德国进行为期六天的访问,以制定一项关于安装在海上风力涡轮机上的结构健康监测技术的研究提案。 此外,PI?我们计划开设一门有关海上风能的研究生课程。 美国团队拥有用于损坏识别的无线传感器和嵌入式数据处理方面的专业知识,德国合作者提供专业知识和访问德国湾部署的新海上风力涡轮机领域的机会。 这对美国团队来说是一个独特的机会,因为美国目前还不存在这样一个海上风力涡轮机领域,将部署的传感器将收集一组丰富的数据,充分捕捉海上涡轮机固有的风浪结构相互作用的复杂性。 这些数据将有助于优化未来海上风力涡轮机的设计,从而帮助美国寻求可再生能源。
英文摘要
OISE-0820607 U.S.-Germany Planning Visit: Structural Health Monitoring Sensors for Offshore Wind Turbines This project supports a collaboration between Dr. Jerome Lynch in the Department of Civil and Environmental Engineering at the University of Michigan, Dr. John Sweetman in the Department of Maritime Systems Engineering at Texas A&M at Galveston, and Dr. Raimund Rolfes at the Institute for Structural Analysis at the University of Hannover in Germany. This proposal, and its collaborative mate 0813764, will fund a six day visit to Germany for Dr. Lynch, Dr. Sweetman, and two students from the University of Michigan to develop a research proposal dealing with structural health monitoring technologies to be installed on offshore wind turbines. In addition, the PI?s plan to develop a graduate course dealing with offshore wind energy. The U.S team has expertise in wireless sensors and embedded data processing for damage identification, and the German collaborator provides expertise and access to a new offshore field of wind turbines being deployed in the German Bight. This is a unique opportunity for the U.S. team, because such a field of offshore wind turbines does not currently exist in the U.S. The sensors that will be deployed will harvest a rich set of data that fully captures the complexity of wind-wave-structure interactions inherent to offshore turbines. This data will be useful in optimizing the design of future offshore wind turbines thereby helping the U.S. in its quest for renewable energy sources.
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