边界感知的直线网格:将非结构化数据集精确逼近为具有实体边界处理能力的直线网格

Dana El-Rushaidat, Raine Yeh, X. Tricoche
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引用次数: 0

摘要

计算流体动力学模拟产生越来越大的数据集,这些数据集通常在具有实体边界的非结构化网格上定义。尽管非结构化网格允许这种几何图形的灵活表示和网格分辨率的细化,但它们存在高存储成本、非平凡空间查询和低重建平滑性的问题。另一方面,直线网格没有这些缺点,但它们不能表示复杂的边界。在本文中,我们提出了一种将具有实体边界的大型非结构化数据集高质量逼近到我们赋予边界处理能力的修改直线网格上的技术。生成的数据表示可以适应具有挑战性的边界,同时支持高阶重构内核,并且内存占用大大减少。因此,我们的数据表示享有传统直线网格的所有优点,同时解决了其基本的几何限制。我们在几个CFD数据集上验证了该方法,并表明该方法能够准确、高质量地逼近模拟数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Boundary-Aware Rectilinear Grid: Accurate Approximation of Unstructured Dataset into Rectilinear Grid with Solid Boundary Handling Capabilities
Computational fluid dynamics simulations produce increasingly large datasets that are often defined over unstructured grids with solid boundaries. Though unstructured grids allow for the flexible representation of this geometry and the refinement of the grid resolution, they suffer from high storage cost, non-trivial spatial queries, and low reconstruction smoothness. On the other hand, rectilinear grids do not have these drawbacks, but they cannot represent complex boundaries. We present in this paper a technique for the high-quality approximation of large unstructured datasets with solid boundaries onto modified rectilinear grids that we endow with boundary handling capabilities. The resulting data representation can accommodate challenging boundaries while supporting high-order reconstruction kernels with a much-reduced memory footprint. As such, our data representation enjoys all the benefits of conventional rectilinear grids while addressing their fundamental geometric limitations. We demonstrate the proposed approach on several CFD datasets and show that our method achieves an accurate and high-quality approximation of simulation datasets.
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