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A normal ray refinement technique for Cartesian-grid based Navier-Stokes solvers

机译:基于笛卡尔网格的Navier-Stokes求解器的法线细化技术

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In this work, a novel technique called normal ray refinement (NRR) is developed, implemented and investigated. Normal ray refinement is designed to allow for the viscous fluid flow simulations using an unstructured Cartesian grid framework in a computationally efficient manner. A key benefit of using a Cartesian grid method is that the grid can be automatically generated, thereby saving a vast amount of time and effort for complex geometries. The main drawback of using the Cartesian grid method is the large number of cells required to resolve viscous boundary layers, and it is this problem that the NRR approach addresses. The NRR approach relies on the use of refined normal rays of cells emanating from the body surface and spanning the boundary layer. Separating these rays along the body surface are relatively large cells too coarse to accurately capture viscous gradients. The heart of the NRR approach lies in the inter-ray communication strategies used between the normal rays that allow the accurate simulation of boundary layers even though the cells separating the rays are large. This yields a large reduction in the number of cells in the grid, which reduces the computational cost of simulation. This paper provides a background on different viscous Cartesian grid-based methods, followed by an explanation of the NRR approach, then some initial 2D results obtained using NRR for Reynolds numbers up to 1 million. It is shown that NRR can yield substantial reduction in computational cost relative to the standard Cartesian approach.View full textDownload full textKeywordsCartesian grid, normal ray refinement, unstructured grid, Navier-Stokes, CFDRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/10618562.2012.691970
机译:在这项工作中,开发,实施和研究了一种称为法线细化(NRR)的新技术。正常射线细化被设计为允许使用非结构化的笛卡尔网格框架以计算有效的方式进行粘性流体流动模拟。使用笛卡尔网格方法的主要好处是可以自动生成网格,从而为复杂的几何形状节省了大量时间和精力。使用笛卡尔网格方法的主要缺点是解析粘性边界层需要大量的像元,而NRR方法正是解决了这个问题。 NRR方法依赖于使用从体表发出并跨越边界层的细化法线射线。沿体表分离这些射线的是相对较大的细胞,它们过于粗糙,无法精确捕获粘性梯度。 NRR方法的核心在于法线之间使用的线间通信策略,即使分隔射线的单元很大,该策略也可以精确模拟边界层。这大大减少了网格中的单元数量,从而降低了仿真的计算成本。本文提供了不同的基于笛卡尔笛卡尔网格的方法的背景知识,随后对NRR方法进行了解释,然后给出了使用NRR获得不超过100万雷诺数的一些初始2D结果。结果表明,相对于标准的笛卡尔方法,NRR可以显着降低计算成本。 ,services_compact:“ citeulike,netvibes,twitter,technorati,美味,linkedin,facebook,stumbleupon,digg,google,更多”,发布号:“ ra-4dff56cd6bb1830b”};添加到候选列表链接永久链接http://dx.doi.org/10.1080/10618562.2012.691970

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