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Numerical modeling of subaerial and submarine landslide-generated tsunami waves-recent advances and future challenges

机译:地下和海底滑坡产生的海啸波的数值模拟-最新进展和未来挑战

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摘要

Landslide-generated waves (LGWs) are among natural hazards that have stimulated attentions and concerns of engineers and researchers during the past decades. At the same period, the application of numerical modeling has been progressively increased to assess, control, and manage the risks of such hazards. This paper represents an overview of numerical studies on LGWs to explore associated recent advances and future challenges. In this review, the main landslide events followed by an LGW hazard are scrutinized. The uncertainty regarding landslide characteristics and the lack of data concerning generated tsunami properties highlights the necessity of probabilistic analysis and numerical modeling. More than 53 % of landslides show the slide length larger than about 20 times of the slide thickness. This fact justifies the popular application of depth-averaged equations (DAEs) for landslides' motion simulations. Such models are reviewed and tabulated based on their mathematical, numerical, and conceptual approaches. A landslide is generally treated as a homogeneous, mixture, or a multi-phase fluid with different rheologies. The Coulomb type rheology is the most-used rheology applied in more than 70 % of landslide models. Some of the recent studies are considering the effects of multi-phase nature, dynamic changes of rheological parameters, and grain-size segregation of the landslide on its deformations. The numerical tools that model LGWs are also reviewed, categorized, and examined. These models conceptualize a landslide as a general rigid LGW (R-LGW) or deformable LGW (D-LGW) mass. The rigid slide assumption is mainly applied in the LGW models with a focus on the accurate simulation of the wave propagation stage, particularly by means of higher order Boussinesq-type wave equations (BWEs). The majority of D-LGW models solve either the Navier-Stokes equations (NSEs) for a multi-phase (landslide material, water, and air) flow or the shallow water equations (SWEs) for a two-layer (a layer of granular material moving beneath a layer of water) flow. NSEs are more comprehensive models but less robust than DAEs. The key effect of dispersion in LGWs, which are typically important in intermediate and even deep water wave domains, challenges researchers to apply higher order BWEs instead of SWEs in two-layer models. Regarding numerical approaches, Lagrangian's are more robust than Eulerian's, but they have been rarely applied due to their high computational demands for real cases. The remaining challenges are reviewed as the necessity of probabilistic analysis to assess the risk of the related hazards more accurately for both past and potential LGW hazards; further thorough laboratory-scale experiments and field data measurements to have accurate and detailed benchmark data; providing RS/GIS-based worldwide hazard map for potential LGWs and compiled database for occurred events; extending BWEs for granular flows and DAEs with non-hydrostatic corrections; and economizing the computational costs of models by advanced techniques like parallel processing and GPU accelerators.
机译:滑坡产生的波浪(LGW)是自然灾害之一,在过去的几十年中引起了工程师和研究人员的关注。同时,数值建模的应用也逐渐增加,以评估,控制和管理此类危险的风险。本文概述了有关轻武器的数值研究,以探索相关的最新进展和未来挑战。在这篇评论中,对主要的滑坡事件以及随后的LGW危害进行了详细审查。滑坡特征的不确定性和有关海啸性质的数据的缺乏凸显了概率分析和数值模拟的必要性。超过53%的滑坡表明滑坡长度大于滑坡厚度的20倍。这一事实证明了深度平均方程(DAE)在滑坡运动模拟中的广泛应用是合理的。此类模型基于其数学,数值和概念方法进行审查和制表。滑坡通常被视为具有不同流变性的均质,混合物或多相流体。库仑型流变学是在70%以上的滑坡模型中应用最广泛的流变学。最近的一些研究正在考虑多相性质,流变参数的动态变化以及滑坡的颗粒尺寸偏析对其变形的影响。对LGW进行建模的数值工具也将进行审查,分类和检查。这些模型将滑坡概念化为一般的刚性LGW(R-LGW)或可变形LGW(D-LGW)质量。刚性滑动假设主要应用于LGW模型中,重点是对波传播阶段的精确模拟,尤其是借助高阶Boussinesq型波动方程(BWE)。大多数D-LGW模型要么针对多相(滑坡材料,水和空气)流求解Navier-Stokes方程(NSE),要么针对两层(一层颗粒状)求解浅水方程(SWE)。物质在一层水之下流动)流动。 NSE是更全面的模型,但不如DAE强大。 LGW分散的关键作用在中间乃至深水波域中通常很重要,这对研究人员提出了挑战,要求他们在两层模型中应用高阶BWE而不是SWE。关于数值方法,拉格朗日方法比欧拉方法更健壮,但由于对实际案例的计算要求较高,因此很少应用。回顾了剩余的挑战,因为有必要进行概率分析以更准确地评估过去和潜在的LGW危害的相关危害的风险;进一步彻底的实验室规模实验和现场数据测量,以获取准确而详细的基准数据;为潜在的轻武器提供基于RS / GIS的全球危险地图,并为发生的事件提供汇编数据库;通过非静水校正扩展BWE以用于颗粒流和DAE;并通过并行处理和GPU加速器等先进技术来节省模型的计算成本。

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