Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  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 lor memory requirements, and (ii) when performing iterative design based on finite element analysis. Our approach can handle large as ll 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 ighted 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. Highlights?  study sensitivity estimation of removing negative feature on engineering analysis. ? It is achieved by reformulating it as a modeling error. ? The final estimator is expressed as local integrations over the feature boundary. ? Its performance is demonstrated using a Poisson equation. ? Comparisons with classical topological sensitivity are also made.
['Ming Li', 'Shuming Gao', 'Ralph Robert Martin']
I am writing a research paper on a idea Estimating the effects of removing negative features on engineering analysis
[{"from": "human", "value": "I am writing a research paper on a idea Estimating the effects of removing negative features on engineering analysis"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  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 lor memory requirements, and (ii) when performing iterative design based on finite element analysis. Our approach can handle large as ll 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 ighted 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. Highlights?  study sensitivity estimation of removing negative feature on engineering analysis. ? It is achieved by reformulating it as a modeling error. ? The final estimator is expressed as local integrations over the feature boundary. ? Its performance is demonstrated using a Poisson equation. ? Comparisons with classical topological sensitivity are also made."}]