Optimizing parameterized CAD geometries using sensitivities based on adjoint functions

Trevor Robinson, Cecil Armstrong, Hung Chua, C. Othmer, T. Grahs

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)
567 Downloads (Pure)


An approach is presented for determining which parameters defining the features in a CAD model need to be modified, and by what amount, to optimize component performance. It uses sensitivities computed for the parameters to determine the change required in each to optimize the component. Parametric sensitivity is computed by combining a measure of boundary movement due to a parameter perturbation, known as design velocity, and an adjoint sensitivity map over the boundary. The sensitivity map results from an adjoint analysis and approximates the change in objective function (performance) due to a movement of the boundary. Gradient based optimization is used based on the parametric sensitivities.

This presented method is significantly less computationally expensive than alternative approaches, and has the advantage that optimization is based on the parameters defining the CAD model, allowing it to be integrated into design workflows. The efficiency of the approach allows all of the parameters in the CAD model to be used as optimization variables, potentially offering better optimization. The work is immune to many of the issues hampering existing approaches.
Original languageEnglish
Pages (from-to)253-268
JournalComputer-Aided Design and Applications
Issue number3
Publication statusPublished - 09 Aug 2013

ASJC Scopus subject areas

  • Computational Mechanics
  • Computer Graphics and Computer-Aided Design
  • Computational Mathematics

Fingerprint Dive into the research topics of 'Optimizing parameterized CAD geometries using sensitivities based on adjoint functions'. Together they form a unique fingerprint.

Cite this