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Goal-oriented model adaptivity for viscous incompressible flows

机译:以目标为导向的粘性不可压缩流动模型自适应性

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摘要

In van Opstal et al. (Comput Mech 50:779-788, 2012) airbag inflation simulations were performed where the flow was approximated by Stokes flow. Inside the intricately folded initial geometry the Stokes assumption is argued to hold. This linearity assumption leads to a boundary-integral representation, the key to bypassing mesh generation and remeshing. It therefore enables very large displacements with near-contact. However, such a coarse assumption cannot hold throughout the domain, where it breaks down one needs to revert to the original model. The present work formalizes this idea. A model adaptive approach is proposed, in which the coarse model (a Stokes boundary-integral equation) is locally replaced by the original high-fidelity model (Navier-Stokes) based on a-posteriori estimates of the error in a quantity of interest. This adaptive modeling framework aims at taking away the burden and heuristics of manually partitioning the domain while providing new insight into the physics. We elucidate how challenges pertaining to model disparity can be addressed. Essentially, the solution in the interior of the coarse model domain is reconstructed as a post-processing step. We furthermore present a two-dimensional numerical experiments to show that the error estimator is reliable.
机译:在van Opstal等人(Comput Mech 50:779-788,2012)中,进行了安全气囊充气模拟,其中流量近似于斯托克斯流量。在错综复杂的初始几何形状中,斯托克斯假设被认为是成立的。这种线性假设导致了边界积分表示,这是绕过网格生成和重新划分网格的关键。因此,它能够在近距离接触的情况下实现非常大的位移。然而,这种粗略的假设不能在整个领域中成立,当它崩溃时,人们需要恢复到原始模型。目前的工作正式确立了这一想法。该文提出一种模型自适应方法,其中粗模型(Stokes边界积分方程)局部替换为基于对感兴趣量误差的后验估计的原始高保真模型(Navier-Stokes)。这个自适应建模框架旨在消除手动划分域的负担和启发式方法,同时提供对物理场的新见解。我们阐明了如何解决与模型差异相关的挑战。从本质上讲,粗模型域内部的解被重构为后处理步骤。此外,我们提出了一个二维数值实验,以证明误差估计器是可靠的。

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