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Article . 2025
License: CC BY
Data sources: Datacite
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
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Advances in Computational Geometry and Design Optimization

Authors: Rohan K. Patel; Akira S. Nakamura; Ethan R. Lee; and Lucas F. Jensen;

Advances in Computational Geometry and Design Optimization

Abstract

This paper presents an approach which increases the flexibility of a computer-aided design (CAD) model by automatically refining its parameterization and adding new CAD features to the model's feature tree. It aims to overcome the limitations imposed by the choice of parameters used during the initial model creation, which constrains how the model shape can change during design optimisation. Parametric effectiveness compares the maximum performance improvement that can be achieved using a parameterisation strategy to the maximum performance improvement that can be obtained where the model is unconstrained in how it moves. As such, it provides a measure of how good a parameterisation strategy is and allows different strategies to becompared. The change in parametric effectiveness due to inserting multiple different CAD features can be calculated using a single adjoint analysis; therefore, the computational cost is essentially independent of the number of parameterisation strategies being analysed. The described approach can be used to automatically add new features to the model and subsequently allows the use of the newly added parameters, along with the existing parameters to be used for optimization, providing the opportunity for a better performingproduct.Thedeveloped approachis applied onCADmodels created inCATIAV5 for 2D and 3Dfiniteelement and computational fluid dynamic problems.

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Keywords

cad . feature . optimisation . adjoint . parameterisation

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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