Development of a data-driven automatic unstructured mesh generation tool suitable for compound flooding studies

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software Pub Date : 2025-06-21 DOI:10.1016/j.envsoft.2025.106587

Jaime R. Calzada , Y. Joseph Zhang , Fei Ye , Linlin Cui

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引用次数: 0

Abstract

We develop a new automatic tool for effectively generating large unstructured meshes suitable for coastal compound flooding studies. The new tool (Geomesh) takes only digital elevation data as inputs and is free of any other artificial manipulation, consistent with the philosophy of the target model (SCHISM) it attempts to serve. Geomesh starts with modules of the popular package JIGSAW for delineating geometric features and specifying size function used in mesh generation, with improvements added to drastically improve efficiency for large applications. To accommodate SCHISM's need for hybrid triangular-quadrilateral mesh, we develop a new tool for quadrilateral generation and then use a constraint triangulator to merge the quads into the triangular mesh. We demonstrate the performance of a mesh generated using Geomesh for US coast using SCHISM 2D in simulating the compound surge during Hurricane Harvey. The tool has potential to greatly speed up the mesh generation process in large domains.

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适于复合驱研究的数据驱动非结构化网格自动生成工具的开发

我们开发了一种新的自动工具，可以有效地生成适合沿海复合洪水研究的大型非结构化网格。新工具（Geomesh）只接受数字高程数据作为输入，没有任何其他人为操作，与它试图服务的目标模型（SCHISM）的理念一致。Geomesh从流行的JIGSAW包的模块开始，用于描绘几何特征和指定网格生成中使用的尺寸函数，并进行了改进，以大大提高大型应用程序的效率。为了适应SCHISM对三角四边形混合网格的需求，我们开发了一种新的四边形生成工具，然后使用约束三角剖分器将四边形合并到三角网格中。利用SCHISM 2D模拟哈维飓风期间的复合浪涌，展示了利用Geomesh生成的美国海岸网格的性能。该工具具有极大地加快大域网格生成过程的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Environmental Modelling & Software 工程技术-工程：环境

CiteScore

9.30

自引率

8.20%

发文量

241

审稿时长

60 days

期刊介绍： Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.