Collaborative Research : Nonlinear Long Wave Amplification in the Shadow Zone of Offshore Islands

合作研究：近海岛屿阴影区的非线性长波放大

• 批准号: 1538190
• 负责人: James Kaihatu
• 金额: $ 41.88万
• 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: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1538190&HistoricalAwards=false
• 关键词: Collaborative; Research; Nonlinear; Long; Wave

Field survey reports from recent tsunamis suggest that local residents in mainland areas shadowed by nearby islands may be under the impression that these islands protect them from tsunamis. Recent numerical results using the mathematical procedure known as "active learning" have generated substantial attention in world media (The Economist, Der Spiegel, Science, Korean Herald, Kathimerini), because they suggest that, in most cases, islands amplify tsunamis in the shadow zones behind them. In this application, the active learning methodology requires about 100,000 times fewer computations than conventional mathematical approaches, and it is unclear if the amplification effect is real. Through comprehensive laboratory experiments, the physical manifestation of this effect will be studied. If indeed the physical experiments confirm the numerical idealizations, this research will help save lives by better targeting educational campaigns to at risk populations. For example, it will be determined if coastlines shadowed by offshore islands along the Pacific Coast of the US are more vulnerable than earlier believed. The early numerical results from active learning are only applicable for non-breaking waves. While many existing numerical codes attempt to model mild long-wave breaking, as they sometimes do, it is unclear how well they perform when scattered long waves break and interact. It is equally unclear if the isthmus between islands scatters the wave energy or focuses further in the mainland behind them, or under what geographical conditions either effect prevails. Through the laboratory experiments, it will be determined if this vexing phenomenon persists when waves break. The results will help validate active learning as a mathematical procedure for uncertainty reduction which greatly reduces computational costs. Also, a substantial laboratory data set will be developed to help benchmark numerical computations for interacting breaking wave fronts, under conditions as yet unstudied. An outreach campaign is planned to educate populations at risk and improve the awareness of emergency managers on this unusual amplification phenomenon.

最近的海啸调查报告表明，附近岛屿遮蔽的大陆地区的当地居民可能会印象，即这些岛屿保护它们免受海啸的侵害。 使用称为“主动学习”的数学程序的最新数值结果在世界媒体（经济学家，Der Spiegel，Science，Korean Herald，Kathimerini）中引起了很大的关注，因为它们建议在大多数情况下，岛屿在其后面的阴影区域扩大了海啸。 在此应用程序中，主动学习方法比常规数学方法所需的计算少约100,000倍，而且还不清楚放大效果是否是真实的。 通过全面的实验室实验，将研究这种效果的物理表现。 如果确实的物理实验证实了数值理想化，那么这项研究将通过更好地针对危险人群的教育运动来帮助挽救生命。 例如，将确定美国太平洋海岸沿海岛屿遮蔽的海岸线是否比以前认为的更脆弱。主动学习的早期数值结果仅适用于非断裂波。 尽管许多现有的数值代码试图对轻度的长波破裂进行建模，但他们有时会像这样做，但尚不清楚它们在散落的长波破裂和相互作用时的性能如何。 同样尚不清楚岛之间的地峡是散布波能量还是在其后面的大陆上进一步集中在地理位置上，或者在哪种地理条件下效果占主导。 通过实验室实验，将确定这种烦恼的现象在波破裂时是否持续存在。 结果将有助于验证积极的学习作为减少不确定性的数学程序，从而大大降低计算成本。 此外，在尚未研究的条件下，将开发出大量的实验室数据集，以帮助基于相互作用的断裂波阵线的基准数值计算。 计划进行一项外展运动，以教育处于风险的人群，并提高应急管理人员对这种异常放大现象的认识。