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.

最近海啸的实地调查报告表明，被附近岛屿遮蔽的大陆地区的当地居民可能认为这些岛屿可以保护他们免受海啸的袭击。 最近使用被称为“主动学习”的数学程序得出的数值结果在世界媒体（《经济学人》、《明镜周刊》、《科学》、《韩国先驱报》、《Kathimerini》）上引起了极大的关注，因为这些结果表明，在大多数情况下，岛屿会放大其身后阴影区的海啸。 在该应用中，主动学习方法需要比传统数学方法少大约100，000倍的计算，并且不清楚放大效应是否是真实的。 通过综合实验室实验，将研究这种效应的物理表现。 如果物理实验确实证实了数字理想化，这项研究将有助于通过更好地针对高危人群的教育活动来拯救生命。 例如，将确定美国太平洋沿岸沿着被近海岛屿遮蔽的海岸线是否比先前认为的更脆弱。早期的主动学习的数值结果只适用于非破碎波。 虽然许多现有的数值代码试图模拟轻微的长波破碎，因为他们有时这样做，目前还不清楚他们的表现如何时，分散的长波破碎和相互作用。 同样不清楚的是，岛屿之间的地峡是否分散了波浪能量，或者进一步集中在它们后面的大陆上，或者在什么地理条件下，这两种效应都占主导地位。 通过实验室实验，将确定当波浪破碎时这种令人烦恼的现象是否持续。 结果将有助于验证主动学习作为减少不确定性的数学过程，从而大大降低计算成本。 此外，大量的实验室数据集将开发，以帮助基准的相互作用破碎波阵面的数值计算，条件下尚未研究。 计划开展一项外展活动，对高危人群进行教育，并提高应急管理人员对这种不寻常的放大现象的认识。