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 , if not none, 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 the Laplace equation defined on the geometrical model of interest  , with Neumann boundary conditions on the features themselves . We derive an a posteriori estimator of the energy error between the solutions of the exact and the defeatured geometries in R^n,  that is simple, efficient and reliable up to oscillations. The dependence of the estimator upon the size of the features is explicit , and the effectivity index is independent from the number of features considered .
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 the Laplace 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 R^n, n\in\{2,3\ that is simple, efficient and reliable up to oscillations. The dependence of the estimator upon the size of the features is explicit .
D
, if not none,
A
containing a single feature
R
features themselves
feature itself
A
n\in\{2,3\
D
, and the effectivity index is independent from the number of features considered