Research Initiation Award: Uncertainty Modeling, Probabilistic Models, and Life-cycle Reliability of Floating Offshore Wind Turbines

研究启动奖：浮动海上风力发电机的不确定性建模、概率模型和生命周期可靠性

• 批准号: 2118586
• 负责人: Doeun Choe
• 金额: $ 30万
• 依托单位: New Mexico State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118586&HistoricalAwards=false
• 关键词: Research; Initiation; Award; Uncertainty; Modeling

Research Initiation Awards provide support for faculty at Historically Black Colleges and Universities who are building a research program. It is expected that the award helps to further the faculty member's research capability and effectiveness, improves research and teaching at her home institution, and involves undergraduate students in research experiences. The award to Prairie View A&M University has potential broader and societal impact in a number of areas. Floating offshore wind turbines (FOWTs) have been considered as one of the most promising alternatives to access energy resources with vast available space and fewer restrictions and regulations. The project seeks to analyze floating offshore wind turbine uncertainties that arise due to the ocean environment and hydrodynamic calculations, as well as due to uncertainties in the structural and mechanical systems, which threaten the reliability and the feasibility of FOWTs. Undergraduate students, high school students and students from community colleges will gain research experiences through this project. The current floating offshore wind turbine technologies have challenges to be resolved in order to produce sustainable and cost-effective power. Semi-submersible type FOWTs are known to be one of the most promising options with construction and maintenance cost problems yet to be solved. This project focuses on identification of the uncertainties and on estimating the structural safety factors. The research objectives of this project are to: understand the uncertainties that exist in FOWTs; predict the structural behavior and life-cycle structural reliability of FOWTs; and obtain safety factors for structural design of semi-submersible-type FOWTs that uniquely consider the uncertainties and coupled dynamics of aerodynamics of the wind turbine and hydrodynamics of the floating body under extreme environmental loads. By considering the uncertainties found, the probabilistic capacity and demand models for structural components of FOWTs will be developed using a Bayesian Method. Lastly, the developed models will be used to estimate the life-cycle structural reliability and to obtain the safety factors for the base tower moment and shear forces of FOWTs. The results of this research may eventually reduce the current challenge of the high cost of FOWTs.

研究启动奖为历史悠久的黑人学院和大学的教职员工提供支持，他们正在建立一个研究项目。预计该奖项有助于进一步提高教师的研究能力和有效性，改善她所在机构的研究和教学，并让本科生参与研究经验。授予Prairie View A&Amm大学这一奖项可能会在多个领域产生更广泛的社会影响。浮式海上风电机组具有空间大、限制少等优点，被认为是最有前途的能源替代方案之一。该项目旨在分析由于海洋环境和水动力计算以及结构和机械系统中的不确定性而产生的浮动海上风力涡轮机的不确定性，这些不确定性威胁到FOWTs的可靠性和可行性。本科生、高中生和社区学院的学生将通过这个项目获得研究经验。目前的浮式海上风力涡轮机技术面临着需要解决的挑战，以生产可持续和具有成本效益的电力。半潜式FOWTs被认为是最有前途的选择之一，但建造和维护成本问题尚未解决。本项目的重点是不确定性的识别和结构安全系数的估计。该项目的研究目标是：了解FOWTs中存在的不确定性；预测FOWTs的结构行为和全生命周期结构可靠性；获得唯一考虑极端环境载荷下风力机气动和浮体水动力的不确定性和耦合动力学的半潜式FOWTs结构设计安全系数。通过考虑发现的不确定性，将使用贝叶斯方法建立FOWTs结构部件的概率能力和需求模型。最后，将所建立的模型用于估计结构的全寿命周期可靠度，并得到底部塔柱弯矩和剪力的安全系数。这项研究的结果可能最终会减少目前FOWTs成本高的挑战。

项目成果

Uncertainty models for the structural design of floating offshore wind turbines: A review

• DOI: 10.1016/j.rser.2023.113610
• 发表时间: 2023-10
• 期刊: Renewable and Sustainable Energy Reviews
• 影响因子: 15.9
• 作者: M. Ramezani;D. Choe;Khashayar Heydarpour;Bonjun Koo

Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning

使用深度学习预测海上风电场的风速、潜在风力和相关不确定性

• DOI: 10.1115/power2020-16557
• 发表时间: 2020
• 期刊: ASME 2020 Power Conference collocated with the 2020 International Conference on Nuclear Engineering
• 作者: Choe, Do-Eun;Talor, Gary;Kim, Changkyu
• 通讯作者: Kim, Changkyu

Sequence-based modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades

• DOI: 10.1016/j.renene.2021.04.025
• 发表时间: 2021-04-30
• 期刊: RENEWABLE ENERGY
• 影响因子: 8.7
• 作者: Choe, Do-Eun;Kim, Hyoung-Chul;Kim, Moo-Hyun
• 通讯作者: Kim, Moo-Hyun