A velocity-vorticity-pressure formulation for the steady Navier–Stokes–Brinkman–Forchheimer problem

IF 7.3 1区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Santiago Badia , Carsten Carstensen , Alberto F. Martín , Ricardo Ruiz-Baier , Segundo Villa-Fuentes
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引用次数: 0

Abstract

The flow of incompressible fluid in highly permeable porous media in vorticity - velocity - Bernoulli pressure form leads to a double saddle-point problem in the Navier–Stokes–Brinkman–Forchheimer equations. The paper establishes, for small sources, the existence of solutions on the continuous and discrete level of lowest-order piecewise divergence-free Crouzeix–Raviart finite elements. The vorticity employs a vector version of the pressure space with normal and tangential velocity jump penalisation terms. A simple Raviart–Thomas interpolant leads to pressure-robust a priori error estimates. An explicit residual-based a posteriori error estimate allows for efficient and reliable a posteriori error control. The efficiency for the Forchheimer nonlinearity requires a novel discrete inequality of independent interest. The implementation is based upon a light-weight forest-of-trees data structure handled by a highly parallel set of adaptive mesh refining algorithms. Numerical simulations reveal robustness of the a posteriori error estimates and improved convergence rates by adaptive mesh-refining.
稳定Navier-Stokes-Brinkman-Forchheimer问题的速度-涡度-压力公式
不可压缩流体在高渗透性多孔介质中以涡度-速度-伯努利压力形式的流动导致了Navier-Stokes-Brinkman-Forchheimer方程中的双鞍点问题。在小源条件下,建立了最低阶分段无散度Crouzeix-Raviart有限元在连续和离散水平上解的存在性。涡度采用具有法向和切向速度跳跃惩罚项的压力空间的矢量版本。一个简单的拉维亚特-托马斯插值导致压力鲁棒先验误差估计。基于残差的显式后验误差估计可以有效可靠地后验误差控制。Forchheimer非线性的效率需要一个新的独立的离散不等式。该实现基于轻量级的树状森林数据结构,由一组高度并行的自适应网格细化算法处理。数值模拟结果表明,自适应网格细化对后验误差估计具有鲁棒性,提高了收敛速度。
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来源期刊
CiteScore
12.70
自引率
15.30%
发文量
719
审稿时长
44 days
期刊介绍: Computer Methods in Applied Mechanics and Engineering stands as a cornerstone in the realm of computational science and engineering. With a history spanning over five decades, the journal has been a key platform for disseminating papers on advanced mathematical modeling and numerical solutions. Interdisciplinary in nature, these contributions encompass mechanics, mathematics, computer science, and various scientific disciplines. The journal welcomes a broad range of computational methods addressing the simulation, analysis, and design of complex physical problems, making it a vital resource for researchers in the field.
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