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Adaptive multinumeric finite element methods for shallow water flow

Adaptive multinumeric finite element methods for shallow water flow
浅水流的自适应多数值有限元方法
批准号:
0107247
负责人:
Clinton Dawson
金额:
$16.99万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2001
资助国家:
美国
项目状态:
已结题
起止时间:
2001-09-01 至 2005-08-31

项目摘要

项目成果

Clinton Dawson的其他基金

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中文摘要
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英文摘要
Recent progress in coastal ocean modeling has emphasized two main themes: 1) the use of relatively large computational domains which encompass much larger areas than the region of specific interest, the main concept being to place the open ocean boundaries far away in deep water non-resonant ocean basins, and 2) strategically providing computational resolution using unstructured grids in order to maintain an approximately constant level of localized error throughout the domain. This large domain/local grid refinement strategy has led to certain computational difficulties. First, the range of flow regimes varies dramatically from the deep ocean to the shallow near shore and inland regions which include inlets, rivers, and flood plains with surrounding levee systems. Not only are the depths dramatically different, but the force balances in the descriptive equations vary dramatically as well. Various algorithms perform very differently within these widely disparate flow regimes in terms of stability, accuracy and localized mass conservation properties. Second, the high level of grid resolution provided in localized high flow gradient and/or very shallow water depth regions actually degrades the stability properties of many algorithms that worked quite well with coarser discretizations, and work very well in regions with smoother solutions. The main focus of this project is to overcome these difficulties through the use of suitably coupled, finite element hp-adaptive algorithms, which are based on mathematically sound error estimates. The investigators have an extensive history in developing continuous Galerkin finite element methods for shallow water problems, and have recently investigated the use of discontinuous Galerkin methods for these problems. By exploiting the strengths of these two approaches, they plan to develop simulation tools for solving shallow water problems which can model large domains with locally refined, unstructured grids, can accurately resolve high gradient flow regions, can locally adapt to changes in flow characteristics, and which honor local mass conservation principles where necessary. Specifically, under this project, the investigators will (1) further develop and analyze discontinuous Galerkin methods for shallow water flows in two and three dimensions (2) thoroughly compare continuous and discontinuous Galerkin methods for some model problems, and (3) investigate novel multi-algorithmic approaches based on coupling the two methodologies for shallow water equations and related mathematical models.Accurate mathematical and computer modeling of coastal ocean circulation and transport of chemical species in shallow waters has significant implications from an economic, environmental and public health perspective. Major inter-related issues include coastal inundation, navigation, sediment movement, pollutant transport and fisheries. Accurate prediction of hurricane storm surges can help save lives and property in many low lying regions throughout the United States and the world. The prediction of coastal currents and water levels is also of major significance in commercial and military navigation, e.g. in the design of harbors and navigation channels. Current computer simulation tools are lacking in their ability to reliably and efficiently model these complex flow regimes. The investigators on this project, through the use of advanced mathematical modeling, numerical algorithms and distributed computing technology, will develop state-of-the-art simulation tools for these applications.
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Collaborative Research: Advancing the Data-to-Distribution Pipeline for Scalable Data-Consistent Inversion to Quantify Uncertainties in Coastal Hazards
  • 批准号:
    2208461
  • 项目类别:
    Standard Grant
  • 资助金额:
    $17.46万
  • 财政年份:
    2022
  • 负责人:
    Clinton Dawson
  • 依托单位:
PREEVENTS Track 2: Collaborative Research: A Dynamic Unified Framework for Hurricane Storm Surge Analysis and Prediction Spanning across the Coastal Floodplain and Ocean
  • 批准号:
    1854986
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $35.94万
  • 财政年份:
    2019
  • 负责人:
    Clinton Dawson
  • 依托单位:
Collaborative Research: Construction and Analysis of Numerical Methods for Stochastic Inverse Problems with Application to Coastal Hydrodynamics
  • 批准号:
    1818847
  • 项目类别:
    Standard Grant
  • 资助金额:
    $10.0万
  • 财政年份:
    2018
  • 负责人:
    Clinton Dawson
  • 依托单位:
Collaborative Research: Numerical and Probabilistic Modeling of Aboveground Storage Tanks Subjected to Multi-Hazard Storm Events
  • 批准号:
    1635115
  • 项目类别:
    Standard Grant
  • 资助金额:
    $24.0万
  • 财政年份:
    2016
  • 负责人:
    Clinton Dawson
  • 依托单位: