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Analysis and Implementation of Multiple Models and Multi-Models for Shallow-Water Type Models of Large Mass Flows

机译：大质量流量浅水模型的多种模型和多种模型的分析与实现

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摘要

Dense large scale granular avalanches are a complex class of flows with physics that has often been poorly captured by models that are computationally tractable. Sparsity of actual flow data (usually only a posteriori deposit information is available) and large uncertainty in the mechanisms of initiation and flow propagation make the modeling task challenging and a subject of much continuing interest. Models that appear to represent the physics well in certain flows turn out to be poorly behaved in others due to intrinsic mathematical or numerical issues. Nevertheless, given the large implications on life and property many models with different modeling assumptions have been proposed.;While, inverse problems can shed some light on parameter choices it is difficult to make firm judgements on the validity or appropriateness of any single or set of modeling assumptions for a particular target flow or potential flows that needs to be modeled for predictive use in hazard analysis. We will present here an uncertainty quantification based approach to carefully, analyze the effect of modeling assumptions on quantities of interest in simulations based on three established models (Mohr-Coulomb, Pouliquen-Forterre and Voellmy-Salm) and thereby derive a model (from a set of modeling assumptions) suitable for use in a particular context. We also illustrate that a useful approach is to use a Bayesian modeling averaging based on the limited available observation data to combine the outcomes of alternative models.;We juxtapose observation data to the simulation results, only with the purpose to maintain a link to the real occurrence of a geophysical flow representing a possible outcome of the case studies described. The fundamental focus of this study is to explore the dynamics of the simulated flows, enabling a notion of the contribution of different mechanisms or models elements inside the simulation procedure, in a fully quantitative, predictive-use oriented and statistical framework. Subsequently, using Bayesian model averaging for hazard analysis based on an identified quantity of interest. The combined model strategy will be embedded in a UQ framework that attempts to quantify two types of so-called "epistemic uncertainty" in resulting simulated maps. This consists of the uncertainty on the selection of rheology model and the one affecting the specific parameters space of the chosen rheology.
机译：密集的大规模颗粒雪崩是一类复杂的物理流，通常很难被可计算的模型捕获。实际流量数据的稀疏性（通常只有后验沉积物信息可用）以及引发和流量传播机制的巨大不确定性使建模任务具有挑战性，并且引起人们持续关注。由于固有的数学或数值问题，在某些流程中似乎能很好地表示物理的模型在其他流程中表现欠佳。然而，考虑到对生命和财产的巨大影响，已经提出了许多具有不同建模假设的模型。虽然反问题可以为参数选择提供一些启示，但是很难对任何单个或一组模型的有效性或适当性做出坚定的判断。为特定目标流量或潜在流量建模的假设，需要进行建模以用于危险性分析中的预测性用途。在这里，我们将介绍一种基于不确定性量化的方法，以基于三个已建立的模型（Mohr-Coulomb，Pouliquen-Forterre和Voellmy-Salm）仔细分析建模假设对感兴趣量的影响，从而得出模型（从适用于特定上下文的一组建模假设）。我们还说明了一种有用的方法是基于有限的可用观测数据使用贝叶斯模型平均来组合替代模型的结果。;我们将观测数据并置到模拟结果中，仅是为了保持与真实模型的链接地球物理流的发生代表了所述案例研究的可能结果。这项研究的基本重点是探索模拟流的动态性，从而在完全定量，面向预测用途的统计框架中实现模拟过程内部不同机制或模型元素的贡献的概念。随后，基于确定的关注数量，使用贝叶斯模型平均进行危害分析。组合的模型策略将被嵌入到UQ框架中，该框架试图在生成的模拟图中量化两种类型的所谓的“流行性不确定性”。这包括流变模型选择的不确定性和影响所选流变学特定参数空间的不确定性。

著录项

  • 作者

    Safaei, Ali Akhavan.;

  • 作者单位

    State University of New York at Buffalo.;

  • 授予单位 State University of New York at Buffalo.;
  • 学科 Mechanical engineering.
  • 学位 M.S.
  • 年度 2018
  • 页码 120 p.
  • 总页数 120
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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