求解不可压缩的纳维-斯托克斯方程:非线性多尺度方法

IF 2.9 2区 数学 Q1 MATHEMATICS, APPLIED
Riedson Baptista , Isaac P. dos Santos , Lucia Catabriga
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引用次数: 0

摘要

在这项研究中,我们提出了一种非线性变分多尺度有限元方法,用于求解静态和瞬态不可压缩纳维-斯托克斯方程。该方法基于近似空间的两级分解,其中非线性人工粘度算子被专门添加到未解决的尺度上。它可以被视为一种自适应方法,因为子网格粘度的数量是根据与解析尺度相关的强形式方程残差自动引入的。本文介绍了子网格粘度的两种变体:一种只考虑动量方程的残差,另一种还包含质量守恒的残差。为了减轻双尺度方法的典型计算成本,微尺度空间是通过在元素边界上消失的多项式函数(即气泡函数)来定义的。我们通过一组二维参考问题,将该方法的数值和计算性能与流线-上风/Petrov-Galerkin(SUPG)公式结合压力稳定/Petrov-Galerkin(PSPG)方法所获得的结果进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Solving incompressible Navier-Stokes equations: A nonlinear multiscale approach
In this work, we present a nonlinear variational multiscale finite element method for solving both stationary and transient incompressible Navier-Stokes equations. The method is founded on a two-level decomposition of the approximation space, where a nonlinear artificial viscosity operator is exclusively added to the unresolved scales. It can be regarded as a self-adaptive method, since the amount of subgrid viscosity is automatically introduced according to the residual of the equation, in its strong form, associated with the resolved scales. Two variants for the subgrid viscosity are presented: one considering only the residual of the momentum equation and the other also incorporating the residual of the conservation of mass. To alleviate the computational cost typical of two-scale methods, the microscale space is defined through polynomial functions that vanish on the boundary of the elements, known as bubble functions. We compared the numerical and computational performance of the method with the results obtained by the Streamline-Upwind/Petrov-Galerkin (SUPG) formulation combined with the Pressure Stabilizing/Petrov-Galerkin (PSPG) method through a set of 2D reference problems.
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来源期刊
Computers & Mathematics with Applications
Computers & Mathematics with Applications 工程技术-计算机:跨学科应用
CiteScore
5.10
自引率
10.30%
发文量
396
审稿时长
9.9 weeks
期刊介绍: Computers & Mathematics with Applications provides a medium of exchange for those engaged in fields contributing to building successful simulations for science and engineering using Partial Differential Equations (PDEs).
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