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: University of Texas at Dallas
• 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
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2150488&HistoricalAwards=false
• 关键词: REU; site; Research; Experience; Undergraduates

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.

德克萨斯大学达拉斯分校为期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年风能系统对电网的贡献达到35%的战略目标做出贡献。每年有10名参与者将为风能技术重点领域的研究做出贡献，包括建模和测量、诊断和控制系统以及设计和制造。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，以促进他们与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)建模和测量：电力生产、电力波动和负荷的模拟；风电场性能诊断和模型验证的实验测量风能资源特性。2)诊断与控制系统：风力发电系统状态监测；优化风力涡轮机和风电场的能量捕获和负荷缓解；风电场控制尾流管理。3)设计与制造：下一代风机叶片设计、制造与测试方法、风机部件增材制造。本科生将参与各种各样的项目，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电力生产和波动的影响，开发新的控制算法以增加能量捕获，同时减轻结构负载，以及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，学生们将探索新的材料和方法，以降低成本制造风力涡轮机叶片和塔。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

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