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影响。 最近使用被称为“主动学习”的数学程序的数值结果引起了世界媒体的广泛关注（《经济学人》、《明镜周刊》、《科学》、《韩国先驱报》、《Kathimerini》），因为它们表明，在大多数情况下，岛屿会在阴影中放大海啸他们后面的区域。 在此应用中，主动学习方法所需的计算量比传统数学方法少约 100,000 倍，并且尚不清楚放大效应是否真实。 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，那么这项研究将通过更好地针对高危人群开展教育活动来帮助拯救生命。 例如，将确定美国太平洋沿岸近海岛屿阴影下的海岸线是否比之前认为的更脆弱。主动学习的早期数值结果仅适用于非破浪。 尽管许多现有的数值代码试图模拟温和的长波破碎（有时会这样做），但尚不清楚当分散的长波破碎和相互作用时它们的表现如何。 同样不清楚的是，岛屿之间的地峡是否会分散波浪能量，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会产生这种效应。 通过实验室实验，我们将确定波浪破碎时这种令人烦恼的现象是否仍然存在。 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，还将开发大量的实验室数据集，以帮助在尚未研究的条件下对相互作用的破碎波阵面的数值计算进行基准测试。 计划开展一项外展活动，对处于危险中的人群进行教育，并提高应急管理人员对这种不寻常的放大现象的认识。