首页> 外文期刊>Numerical Heat Transfer, Part B. Fundamentals: An International Journal of Computation and Methodology >Development of a compact and accurate discretization for incompressible navier-stokes equations based on an equation-solving solution gradient, Part I: Kernel scheme fundamentals
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Development of a compact and accurate discretization for incompressible navier-stokes equations based on an equation-solving solution gradient, Part I: Kernel scheme fundamentals

机译:基于方程解解梯度的不可压缩纳维斯托克斯方程的紧凑而精确的离散化开发,第一部分:内核方案基础

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This is the first part of our attempt to develop a compact and accurate numerical scheme for incompressible Navier-Stokes equations in complicated domains. The objective of this article is to unveil the kernel scheme fundamentals in our formulation, where the solution gradient required for an accurate discretization is computed directly as an additional variable rather than interpolated from solution values at neighboring computational nodes. To achieve this goal, a supplementary equation and its associated control volume are introduced to retain a compact and accurate discretization. Scheme essentials are exposed by numerical analyses on simple one-dimensional modeled problems to reveal its formal accuracy. Due to its highly comprehensible and practical features, this formulation can be easily extended to solve problems in two-dimensional rectangular grid systems. Several one- and two-dimensional problems are solved to verify its simulation accuracy. From the numerical analyses and computational results of test problems, it is found that the present formulation is a useful tool to solve convection-diffusion equations and can be employed as the kernel scheme for fluid flow simulations.
机译:这是我们为复杂域中不可压缩的Navier-Stokes方程开发紧凑而精确的数值方案的尝试的第一部分。本文的目的是在我们的公式中揭示内核方案的基本原理,其中将精确离散化所需的求解梯度直接作为附加变量进行计算,而不是从相邻计算节点上的求解值进行插值。为了实现这一目标,引入了一个补充方程式及其相关的控制量,以保持紧凑而准确的离散化。通过对简单的一维建模问题进行数值分析,揭示了方案的要点,以揭示其形式准确性。由于具有高度的可理解性和实用性,因此可以轻松扩展此公式以解决二维矩形网格系统中的问题。解决了几个一维和二维问题,以验证其仿真精度。从测试问题的数值分析和计算结果可以发现,该公式是求解对流扩散方程的有用工具,可以用作流体模拟的核心方案。

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