NEESR Payload: Dissipation of Long-Wave Energy by Discontinuous Macro-Roughness Representing Forested Areas

NEESR 有效负载：代表森林区域的不连续宏观粗糙度对长波能量的耗散

• 批准号: 0936595
• 负责人: Jennifer Irish
• 金额: --
• 依托单位: Texas A&M Engineering Experiment Station
• 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 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0936595&HistoricalAwards=false
• 关键词: NEESR; Payload; Dissipation; Long; Wave

This award is an outcome of the NSF 09-524 program solicitation ?George E. Brown, Jr. Network for Earthquake Engineering Simulation (NEES) Research (NEESR)? competition and includes Texas A&M University (Texas Engineering Experiment Station). This project will utilize the NEES equipment site at Oregon State University, namely the NEES Tsunami Wave Basin. Tsunamis are classified as long waves. These waves are generated by earthquakes and landslides, among other mechanisms. Tsunami waves travel rapidly inland, reaching high elevations, i.e., runup. These events can devastate entire communities and regions, as seen during the Boxing Day Tsunami in 2004. Tsunamis can cause widespread damage to homes, buildings, and other structures, and they can have long-lasting socioeconomic consequences. The project goal is to learn how tsunami waves respond to patches of macro-roughness. For example, how do patches of forest next to open land reduce the force of tsunami waves and change the landward distance tsunami waves travel? Two objectives will be pursued. Objective 1 is to understand the effects of macro-roughness patches on long-wave energy dissipation, refraction, and shadowing. Objective 2 is to investigate the effects of long-wave propagation in patches of macro-roughness on flow pathways and wave runup variation.Expected research outcomes include: (1) long-wave energy dissipation as a function of macro-roughness coverage and (2) long-wave runup as a function of macro-roughness geometry. These datasets will reveal physics of long-wave propagation over complex sea bottoms. The above outcomes will lead to improved treatment of macro-roughness in models used to predict tsunami inundation. These outcomes will also lead to improved quantification of tsunami wave forces on buildings and other structures at the coast.The research outcomes will have broad scientific impacts. They will contribute to the state-of-knowledge for flows, for example, through mangrove fields, upland forests, and wetlands. They will also be readily transferred to both the storm surge and tsunami inundation problems impacting society at large. The educational outcomes will have broad impacts by reaching both secondary school and collegiate students via hands-on educational activities, among other programs, already established at the NEES Tsunami Wave Basin Equipment Site. In addition to supporting one Ph.D. student at Texas A&M University, up to three undergraduates will participate in this project through the NSF Research Experience for Undergraduates and Louis Stokes Alliance for Minority Participation programs.Data from this project will be archived and made available to the public through the NEES data repository.

该奖项是美国国家科学基金会09-524计划征集活动的成果。地震工程模拟研究网络(NEESR)？竞争对手，包括德克萨斯农工大学(德克萨斯工程实验站)。该项目将利用俄勒冈州立大学的NEES设备场地，即NEES海啸波盆地。海啸被归类为长波。这些波是由地震和山体滑坡等机制产生的。海啸波迅速向内陆传播，到达高海拔，即上升。这些事件可以摧毁整个社区和地区，就像2004年节礼日海啸所看到的那样。海啸可能对房屋、建筑物和其他建筑物造成广泛破坏，并可能产生长期的社会经济后果。该项目的目标是了解海啸波如何对成片的宏观粗糙度做出反应。例如，空地旁的一片片森林如何减少海啸波的力量，并改变海啸波向陆地传播的距离？我们将会追求两个目标。目标1是了解宏观粗糙度斑块对长波能量耗散、折射和阴影的影响。目标2是研究长波在宏观粗糙度斑块中传播对水流路径和波浪爬升变化的影响。预期的研究结果包括：(1)长波能量耗散作为宏观粗糙度覆盖率的函数；(2)长波上升作为宏观粗糙度几何形状的函数。这些数据集将揭示长波在复杂海底的传播物理。上述结果将改善用于预测海啸淹没的模型中宏观粗糙度的处理。这些成果还将改进海啸对沿海建筑物和其他构筑物的海浪作用力的量化。研究成果将产生广泛的科学影响。他们将为水流的知识状态做出贡献，例如，通过红树林、高地森林和湿地。它们还将很容易地转移到影响整个社会的风暴潮和海啸淹没问题上。教育成果将产生广泛的影响，通过实践教育活动接触到中学生和大学生，以及已经在NEES海啸波盆地设备现场建立的其他项目。除了支持德克萨斯农工大学的一名博士生外，最多三名本科生将通过NSF本科生研究体验和路易斯·斯托克斯少数民族参与计划参与这个项目。该项目的数据将被存档，并通过NEES数据库向公众提供。