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Resilience of Transmission Line Structures to Extreme Weather Events

Resilience of Transmission Line Structures to Extreme Weather Events
输电线路结构对极端天气事件的抵御能力
批准号:
RGPIN-2022-04693
负责人:
ElDamatty, Ashraf
金额:
$3.79万
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2022
资助国家:
加拿大
项目状态:
已结题
起止时间:
2022-01-01 至 2023-12-31

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中文摘要
翻译
所有基础设施的运行都依赖电力,这使得电力成为现代生活的重要组成部分。因此,危险事件期间的电力中断可能会对安全、经济安全和公共健康造成毁灭性的影响。拟议的工作将集中在极端风事件对输电线路(TL)结构的影响:最容易受到风灾影响的电网元素。在加拿大和其他国家,涉及这些结构在极端风力条件下失效的事件是一年一度的事件,气候变化的影响正在加剧这些事件发生的频率和强度。申请人的开创性研究创造了世界上第一个规范,使新的TL结构能够有效设计以抵御龙卷风和雷暴(雷暴风),并被纳入2020年美国土木工程师协会指南。然而,最大的挑战在于评估现有结构的结构性能。拟议的工作将涉及开发和验证最先进的数值模型,该模型将包括对TL段中的塔进行同时模拟,同时考虑塔之间通过中间导体发生的相互作用。该模型可以准确地预测塔楼在风作用下的破坏模式,计算在破坏过程中对相邻塔楼的动力作用,模拟塔楼之间的倒塌过程,预测塔楼的连锁倒塌程度。该模式将包括从计算流体力学(CFD)模拟线路地区的实际条件确定的天气动态风荷载以及下击暴流和龙卷风荷载。这个数值模型的所有组件都将在耗资3000万美元的WindEEE穹顶上进行实验验证,这是世界上唯一的三维风室。这项研究将在与各种类型的极端风事件下的TL结构行为有关的知识方面取得突破性进展,并将产生对公用事业公司特别是整个社会非常有益的工具。升级整个TL网络是不可能的。开发的工具将能够最准确地评估各个网段的故障风险,并将根据线路的重要性、冗余级别以及与其他公用事业的连接情况,帮助做出明智且经济高效的升级决策。由于电力是城市系统的支柱,拟议的研究是实现申请者长期目标的一个关键里程碑,即创建工具来评估社区对与天气有关的灾害的复原力,同时考虑到基础设施组成部分之间的依赖关系。这些研究将为八名研究生提供最先进的数值和实验技术方面的培训,同时解决一个严重的实际和紧迫的问题。
英文摘要
The reliance of all infrastructure operation on electrical power has made electricity a vital component of modern life. Power disruptions during a hazard event can thus have devastating effects on safety, economic security, and public health. The proposed work will focus on the impact of extreme wind events on transmission line (TL) structures: the grid elements most vulnerable to wind hazards. In Canada and other countries, incidents involving the failure of those structures during extreme wind conditions are annual events, and the effects of climate change are exacerbating the frequency and intensity of their occurrence. The applicant's pioneer research led to the creation of the world's first specification that enables the effective design of new TL structures to resist tornadoes and downbursts (thunderstorm wind), which was incorporated into the 2020 American Society of Civil Engineers guidelines. However, the greatest challenge lies in assessing the structural behaviour of existing structures. The proposed work will involve developing and validating a state-of-the-art numerical model that will include the simultaneous simulation of the towers in a TL segment while considering the interaction that happens between the towers through the in-between conductors. The model will accurately predict the failure mode of a tower during a wind event, compute the dynamic forces induced on adjacent towers during the failure mechanism, simulate the collapse as it progresses from tower to tower, and forecast the extent of the cascading collapses. The model will incorporate synoptic dynamic wind loads determined from computational fluid dynamics (CFD) simulations of real conditions at the line area plus downburst and tornado loads. All components of this numerical model will be validated experimentally at the $30M WindEEE dome, the world's only three-dimensional wind chamber. This research will provide a breakthrough advance in knowledge related to TL structural behaviour under various types of extreme wind events and will produce highly beneficial tools for utility companies in particular and society in general. Upgrading an entire TL network is impossible. The developed tool will enable the most accurate assessment of the failure risk for individual network segments and will facilitate informed and cost-effective upgrade decisions based on the importance of the line, the level of redundancy, and the connectivity with other utilities. Since electricity forms the backbone of an urban system, the proposed studies represent a key milestone in achieving the applicant's long-term goal of creating tools for evaluating community resilience to weather-related hazards, taking into account the dependencies among infrastructure components. The studies will provide eight graduate students with training in state-of-the-art numerical and experimental techniques, while addressing a serious practical and pressing problem.
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    567534-2021
  • 项目类别:
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  • 资助金额:
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  • 财政年份:
    2021
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  • 项目类别:
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  • 财政年份:
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    RGPIN-2016-04771
  • 项目类别:
    Discovery Grants Program - Individual
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  • 项目类别:
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  • 项目类别:
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  • 资助金额:
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  • 批准年份:
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