论文标题
分析意识失败：问题设置和后验估计
Analysis-aware defeaturing: problem setting and a posteriori estimation
论文作者
论文摘要
失败在于通过删除被认为与给定模拟无关的几何特征来简化几何模型。删除功能和计算机辅助设计模型的简化实现了工程分析问题的更快模拟，并简化了通常不可行的网格划分问题。然后忽略了失败对分析的影响，截至目前，基本上很少有量化这种影响的策略。很好地了解该过程的效果是自动整合设计和分析的重要步骤。我们通过理解其对包含单个特征的几何模型的泊松方程的影响来形式化失败的过程，并在功能本身上具有Neumann边界条件。我们在$ \ Mathbb {r}^n $，$ n \ in \ {2,3 \} $中的确切解决方案和失败的几何形状之间的能量误差中得出了一个后验估计器，这是简单，可靠的，可靠且有效地振荡。估计器对特征大小的依赖性是显式的。
Defeaturing consists in simplifying geometrical models by removing the geometrical features that are considered not relevant for a given simulation. Feature removal and simplification of computer-aided design models enables faster simulations for engineering analysis problems, and simplifies the meshing problem that is otherwise often unfeasible. The effects of defeaturing on the analysis are then neglected and, as of today, there are basically very few strategies to quantitatively evaluate such an impact. Understanding well the effects of this process is an important step for automatic integration of design and analysis. We formalize the process of defeaturing by understanding its effect on the solution of Poisson equation defined on the geometrical model of interest containing a single feature, with Neumann boundary conditions on the feature itself. We derive an a posteriori estimator of the energy error between the solutions of the exact and the defeatured geometries in $\mathbb{R}^n$, $n\in\{2,3\}$, that is simple, reliable and efficient up to oscillations. The dependence of the estimator upon the size of the features is explicit.
