发表机构
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个任务驱动框架，集成模拟与可视化，用于分析混合网格上的多尺度海洋内波流场，通过体素重建和多视图交互，识别横向波传播等难观察现象。
AI 中文摘要
内波是发生在大洋表面之下的大振幅重力波，沿不同密度水层之间的界面传播。理解其生成、传播和演化至关重要，因为这些波在海洋系统中发挥着重要作用，有助于营养盐输运、生物生产力以及跨海洋和大陆架的能量传递。领域科学家使用高分辨率数值海洋模型，在混合计算网格上研究内波动力学及相关的海岸和近岸过程。这些模型生成大规模、三维时空数据集，捕捉内波流动行为及其与多个海洋变量的相互作用。这些数据集通常使用交互性有限的命令行工具进行分析。为应对这些挑战，我们与领域科学家合作，设计了一种任务驱动的可视化方法，用于分析混合网格上的多尺度、多变量流场数据。该框架包含一种混合网格体素重建方法，支持连续的三维分析，并采用协调的多视图设计，支持对复杂流场结构的交互式探索。与领域专家进行的基于洞察的评估表明，该系统能够识别以前难以观察的现象，包括横向波传播、能量输运路径和浅化驱动的混合。除应用领域外，我们的贡献为不规则网格上多尺度、多变量流场数据的可视化分析提供了可推广的技术和设计原则。
英文摘要
Internal waves are large-amplitude gravity waves that occur below the ocean surface and propagate along interfaces separating water layers of different densities. Understanding their generation, propagation, and evolution is essential, as these waves play a vital role in the ocean system by contributing to nutrient transport, biological productivity, and the transfer of energy across the ocean and continental shelf. Domain scientists use high-resolution numerical ocean models, to study internal-wave dynamics and associated coastal and nearshore processes on hybrid computational grids. These models generate large-scale, three-dimensional spatiotemporal datasets that capture internal wave flow behavior and interactions with multiple ocean variables. These datasets are generally analyzed using command-line tools with limited interactivity. To address these challenges, we in collaboration with domain scientists designed a task-driven visualization methodology for analyzing multiscale, multivariate flow data on hybrid grids. The framework incorporates a hybrid-grid volumetric reconstruction method, enabling continuous 3D analysis and a coordinated multi-view design that supports interactive exploration of complex flow structures. An insight-based evaluation with domain experts demonstrates that the system enables the identification of previously difficult-to-observe phenomena, including transverse wave propagation, energy transport pathways, and shoaling-driven mixing. Beyond the application domain, our contributions provide generalizable techniques and design principles for visual analysis of multiscale, multivariate flow data on irregular grids.
CommentsAccepted for publication in IEEE Transactions on Visualization and Computer Graphics (TVCG), IEEE VIS 2026
