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Workshop On Model Validation And Benchmarking For Tsunami Generation By Submarine Mass Failure; May 30-31-2003; Honolulu, HI

Workshop On Model Validation And Benchmarking For Tsunami Generation By Submarine Mass Failure; May 30-31-2003; Honolulu, HI
潜艇质量故障引发海啸的模型验证和基准研讨会;
批准号:
0301862
负责人:
Stephan Grilli
金额:
$3.7万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2003
资助国家:
美国
项目状态:
已结题
起止时间:
2003-01-01 至 2004-12-31

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中文摘要
翻译
这一行动为关于潜艇大规模失败造成海啸的模型验证和基准确定讲习班提供了支助。研讨会将在2003年国际海洋和极地工程师学会会议之后,于2003年5月30日至31日在夏威夷火奴鲁鲁举行。后一次会议将举行一次关于长波传播和助跑的建模的特别会议,研讨会的目的是定义相关的基准问题,以验证海底质量失效海啸数值模型,目标如下:介绍现有的海底质量失效海啸数值模型。讨论每个模型的有效性范围作为其基本假设的函数。确定运行每个模型所需的地质/岩土数据。现有的海底质量失效海啸的实验和现场研究。定义模型验证所需的新的实验基准问题。确定未来滑坡海啸研究的机会。在一期专刊上发表研讨会结果。海底质量失效海啸生成模型与理想化实验数据的比较基准问题用于评估和批评最先进的技术。根据一组严格的基准问题验证数值模型为这些模型提供了预测能力,从某种意义上说,它们可以以一定程度的置信度应用于类似的问题。
英文摘要
This action provides support for a workshop on Model Validation and Benchmarking for Tsunami Generation by Submarine Mass Failure. The workshop will be held in Honolulu, Hawaii, May 30-31, 2003, following the ISOPE 2003 conference (International Society for Offshore and Polar Engineers). The latter conference will have a special session on modeling of long wave propagation and runup, which will complement the workshop.The aim of the workshop is to define relevant benchmark problems to validate submarine mass failure tsunami numerical models through a number of objectives:Present existing numerical models of submarine mass failure tsunami generation.Discuss the range of validity of each model as a function of their basic assumptions.Identify geological/geotechnical data needed to run each model.Present existing experimental and field studies of submarine mass failure tsunamis.Define new experimental benchmark problems needed for model validation.Identify future landslide tsunami research opportunities.Publish workshop results in a special journal issue.Comparison of submarine mass failure tsunami generation models with experimental data for idealized benchmark problems is used to evaluate and criticize the state of the art. Validating the numerical models against a rigorous set of benchmark problems provides these models with a predictive capability, in the sense that they can be applied to similar problems with some degree of confidence.
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