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REU site: Research Experience for Undergraduates in Wind Energy Systems

REU site: Research Experience for Undergraduates in Wind Energy Systems
REU 网站:风能系统本科生的研究经验
批准号:
2150488
负责人:
Stefano Leonardi
金额:
$39.17万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-04-01 至 2025-03-31

项目摘要

项目成果

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中文摘要
翻译
德克萨斯大学达拉斯分校的这个为期3年的REU项目为本科生提供现场研究经验,为美国实现到2050年实现风能系统对电网贡献35%的战略目标做出贡献。每年有10名参与者将为风能技术推力领域的研究做出贡献,包括建模测量,诊断控制系统和设计制造。研究和专业发展活动的结合旨在激发终身学习的追求和对推进可再生能源科学的兴趣。促进多样性和包容性的活动是该项目的核心,特别是从服务不足的群体中招募学生,以推进他们与STEM相关的职业准备。 德克萨斯大学达拉斯分校REU项目的技术目标和范围包括三个风能推力领域:1)建模&测量:电力生产、功率波动和负载的模拟;风电场性能诊断和模型验证的实验测量;风资源表征。2)诊断&控制系统:风力涡轮机系统的状态监测;风力涡轮机和风电场中能量捕获和负载减轻的优化;用于尾流管理的风电场控制。3)设计制造:下一代风力叶片设计、制造和测试方法,风力涡轮机组件的增材制造。本科生将参与各种项目,从而更好地了解风能系统及其竞争性设计。这包括寻求揭示湍流和尾流动力学对发电和波动的影响,开发新的控制算法以增加能量捕获,同时减轻结构负载,并发现基于物理建模和深度学习算法的新诊断方法,以确定风力涡轮机组件的健康状态以及延长其使用寿命的操作策略。此外,学生将探索新的材料和方法,可以采用这些材料和方法来制造风力涡轮机叶片和塔架,降低成本。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
This 3-year REU project at the University of Texas-Dallas provides on-site research experiences for undergraduates that contribute toward the U.S. reaching its strategic goal of achieving 35% contribution of wind energy systems to the electricity grid by 2050. Ten participants each year will contribute to research in wind-energy technical thrust areas, including modeling & measurements, diagnostic & control systems, and design & manufacturing. The combination of research and professional development activities aims to stimulate the pursuit of life-long learning and interest in advancing the science of renewable energy. Activities to promote diversity and inclusion are central to the project, particularly recruiting students from under-served groups to advance their STEM-related career preparation. The technical goals and scope of the University of Texas-Dallas REU project comprise three wind-energy thrust areas: 1) Modeling & Measurements: simulation for power production, power fluctuations, and loads; experimental measurements for wind farm performance diagnostic and model validation; wind resource characterization. 2) Diagnostic & Control Systems: condition monitoring for wind turbine systems; optimization of energy capture and load mitigation in wind turbines and wind farms; wind farm controls for wake management. 3) Design & Manufacturing: next generation wind blade designs, manufacturing and testing methods, additive manufacturing of wind turbine components. Undergraduate students will engage in a variety of projects that lead to a greater understanding of wind energy systems and their competitive design. This includes seeking to uncover the impact of turbulence and wake dynamics on power production and fluctuations, developing novel control algorithms to increase the capture of energy while mitigating structural loads, and discovering new diagnostic methods based on physics-based modeling and deep-learning algorithms to determine the health status of wind turbine components as well as operating strategies to increase their lifetime. In addition, students will explore novel materials and methods that can potentially be adopted to fabricate wind turbine blades and towers at reduced costs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(1)
专著(0)
科研奖励(0)
会议论文
A fast spatio-temporal temperature predictor for vacuum assisted resin infusion molding process based on deep machine learning modeling
基于深度机器学习建模的真空辅助树脂灌注成型过程的快速时空温度预测器
DOI: 10.1007/s10845-023-02113-4
发表时间: 2023
期刊: Journal of Intelligent Manufacturing
影响因子: 8.3
作者: [Zhang, Runyu, Liu, Yingjian, Zheng, Thomas, Eddin, Sarah, Nolet, Steven, Liang, Yi-Ling, Rezazadeh, Shaghayegh, Wilson, Joseph, Lu, Hongbing, Qian, Dong]
通讯作者: Qian, Dong
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  • 负责人:
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