Nonlinear Dynamic Loadings and Responses for Wind Turbine Reliability

风力发电机可靠性的非线性动态载荷和响应

• 批准号: 0933292
• 负责人: Brian Feeny
• 金额: $ 28.03万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0933292&HistoricalAwards=false
• 关键词: Nonlinear; Dynamic; Loadings; Responses; Wind

0933292FeenyProject SummaryAs wind turbines increase in size, challenges in their reliability and maintenance increase. With the larger wind turbines, gearbox bearing failures become more frequent, often well before the designed life of the bearing. This means that loadings, and failure mechanisms, are not yet sufficiently understood to help the design process. It is likely that there exist unknown dynamical loading cases. Intellectual Merits: The aim of this work is to improve the understanding of wind-turbine blade loadings and how they translate to loadings on the gearbox. Loadings on the blades can induce oscillations in a variety of ways. Oscillations in the blades imply dynamic loadings transferred to the gearbox. Understanding these dynamic loadings is essential to the design of reliable gears and bearings, and hence economically viable wind turbines. This work will involve nonlinear perturbation analysis of resonances and stabilities on reduced-order, nonlinear models of the blades. The identified resonances would provide test case parameters for simulation packages such as FAST, Romax, and Simpack simulations. The benefits of reduced-order nonlinear perturbation analysis are (1) the identification resonances as functions of parameters. This gives guidelines for choosing parameters in simulations packages. (2) the identification of "unexpected" resonances, such as subharmonics, superharmonics, and hysteretic primary and secondary resonances. Some of these resonances may have coexisting stable solutions that depend on initial conditions, or could be associated with "dangerous" bifurcations. The modeling includes nonlinear stiffness and mixed nonlinearity (velocity, acceleration, and displacements), direct excitations and parametric excitations acting on nonlinear terms, due to cyclic aerodynamic loading and gravitational loading. The reduced order model will be coarser than simulation packages, but will lead to analytic expressions relating behavior to parameters, and will guide parameter choices for higher fidelity simulations. So the nonlinear studies will assist future, high fidelity simulations.Broader Impacts: The resulting improvements in bearing designs will lengthen the bearing life, and reduce maintenance efforts, and the associated enormous costs. This will help overcome one important issue that currently impedes the wind turbine industry from reaching its full potential. As such, results will be made available to the NREL Gearbox Reliability Collaborative. The project will train a doctoral student on wind turbine dynamics and design. The project will also support undergraduate researchers, exposing advanced undergraduates to research. Under-represented minorities and women will be sought through the Sloan program and the MSU AAGA program. The PI will add wind-turbine dynamics to his participation in an MSU Mathematics Science and Technology summer Mechanical Engineering class for middle schoolers.

0933292Feeny项目摘要随着风力涡轮机尺寸的增大，其可靠性和维护方面的挑战也随之增加。随着风力涡轮机越来越大，齿轮箱轴承故障变得更加频繁，通常早于轴承的设计寿命。这意味着尚未充分了解载荷和失效机制以帮助设计过程。很可能存在未知的动态载荷情况。智力优点：这项工作的目的是提高对风力涡轮机叶片载荷以及它们如何转化为齿轮箱载荷的理解。叶片上的负载会以多种方式引起振动。叶片中的振荡意味着动态载荷传递到齿轮箱。了解这些动态载荷对于设计可靠的齿轮和轴承以及经济上可行的风力涡轮机至关重要。这项工作将涉及叶片降阶非线性模型的共振和稳定性的非线性扰动分析。识别出的共振将为 FAST、Romax 和 Simpack 仿真等仿真包提供测试用例参数。降阶非线性扰动分析的好处是（1）将共振识别为参数的函数。这为在模拟包中选择参数提供了指导。 (2)“意外”谐振的识别，例如次谐波、超谐波以及磁滞一次和二次谐振。其中一些共振可能具有取决于初始条件的共存稳定解，或者可能与“危险”分岔相关。该建模包括非线性刚度和混合非线性（速度、加速度和位移）、由于循环气动载荷和重力载荷而作用于非线性项的直接激励和参数激励。降阶模型将比仿真包更粗糙，但会产生将行为与参数相关的分析表达式，并将指导更高保真度仿真的参数选择。因此，非线性研究将有助于未来的高保真度模拟。更广泛的影响：轴承设计的改进将延长轴承寿命，减少维护工作以及相关的巨大成本。这将有助于克服目前阻碍风力涡轮机行业充分发挥潜力的一个重要问题。因此，结果将提供给 NREL 齿轮箱可靠性协作组织。该项目将培训一名风力涡轮机动力学和设计方面的博士生。该项目还将支持本科生研究人员，让高年级本科生参与研究。将通过斯隆管理学院项目和密歇根州立大学 AAGA 项目寻找代表性不足的少数族裔和女性。该 PI 将在参加密歇根州立大学数学科学与技术暑期中学生机械工程课程时增加风力涡轮机动力学课程。