On the Topological Derivative in Shape Optimization
SIAM Journal on Control and Optimization
Estimation of modeling error in computational mechanics
Journal of Computational Physics
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
A small feature suppression/unsuppression system for preparing B-rep models for analysis
Proceedings of the 2005 ACM symposium on Solid and physical modeling
Concepts and Applications of Finite Element Analysis
Concepts and Applications of Finite Element Analysis
Estimating the impact of large design changes on field problems
Proceedings of the 2007 ACM symposium on Solid and physical modeling
A posteriori evaluation of simplification details for finite element model preparation
Computers and Structures
Review: A posteriori error estimation techniques in practical finite element analysis
Computers and Structures
Estimating defeaturing-induced engineering analysis errors for arbitrary 3D features
Computer-Aided Design
Quantitative control of idealized analysis models of thin designs
Computers and Structures
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This paper provides a general framework for the quantitative estimation of the effects of removing negative features on engineering analysis, or modification sensitivity for short. There are two main applications: (i) when defeaturing models so that finite element analysis may be carried out more quickly and with lower memory requirements, and (ii) when performing iterative design based on finite element analysis. Our approach can handle large as well as small features, and features with Neumann/natural boundary conditions prescribed on them; previous methods have difficulties in handling such cases. Estimation of the modification sensitivity is achieved by reformulating it as a modeling error caused by use of different mathematical models to describe the same engineering analysis problem. Results are obtained using the dual weighted residual (DWR) method in combination with a heuristic assumption of small variation of the dual solution after defeaturing. The final derived sensitivity estimator is expressed in terms of the difference of local boundary integrations over the feature boundary, which can be explicitly evaluated using solutions defined on the defeatured model. The algorithm's performance is demonstrated using a Poisson equation. Comparisons to results obtained by previous approaches indicate that it is both accurate and computationally efficient.