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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“潜艇整体故障引发海啸的模型验证和基准测试”研讨会提供了支持。该研讨会将于2003年5月30日至31日在夏威夷檀香山举行,之后是ISOPE 2003会议(国际海洋和极地工程师协会)。后一个会议将有一个关于长波传播和运行建模的特别会议,这将补充研讨会。研讨会的目的是定义相关的基准问题,通过一些目标来验证潜艇质量失效海啸数值模型:介绍现有的潜艇质量失效海啸产生的数值模型。讨论每个模型作为其基本假设的函数的有效性范围。确定运行每个模型所需的地质/岩土数据。介绍了现有的海底集体失效海啸的实验和现场研究。定义模型验证所需的新实验基准问题。确定未来滑坡海啸的研究机会。在一个特殊的期刊上发表研讨会的结果。针对理想基准问题,将潜艇整体失效海啸产生模型与实验数据进行比较,以评价和批判目前的技术水平。针对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,因为它们可以在一定程度上有信心地应用于类似的问题。
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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