Abstract
This paper presents an efficient optimization process, where the parameters defining the features in a feature-based CAD model are used as design variables. The process exploits adjoint methods for the computation of gradients, and as such the computational cost is essentially independent of the number of design variables, making it ideal for optimization in large design spaces. The novelty of this paper lies in linking the adjoint surface sensitivity information with geometric sensitivity values, referred to as design velocities, computed for CAD models created in commercial CAD systems (e.g. CATIA V5 or Siemens NX). This process computes gradients based on the CAD feature parameters, which are used by the optimization algorithm, which in turn updates the values of the same parameters in the CAD model. In this paper, the design velocity and resulting gradient calculations are validated against analytical and finite-difference results. The proposed approach is demonstrated to be compatible with different commercial CAD packages and computational fluid dynamics solvers.
Highlights
In the context of the industrial design process, a product design typically starts with CAD geometry and eventually delivers optimized geometry in CAD
This paper presents an efficient optimization process, where the parameters defining the features in a feature-based CAD model are used as design variables
The novelty of this paper lies in linking the adjoint surface sensitivity information with geometric sensitivity values, referred to as design velocities, computed for CAD models created in commercial CAD systems (e.g. CATIA V5 or Siemens NX)
Summary
In the context of the industrial design process, a product design typically starts with CAD geometry and eventually delivers optimized geometry in CAD. There is a need to use CAD models within the optimization framework to strengthen the industrial workflow Current research in this area aims to enable shape optimization using either a dumb geometry, which is a non-parametric CAD model from which the construction history has been removed, or a CAD model with its construction history, features, and parameters included. To a large extent this will depend on the skill and experience of the CAD model creator, and their ability to visualize and parameterize the design space The drawbacks of this approach are that, as the choice of CAD features constrains how the model can change shape, it may stifle the creation of innovative solutions. It may be necessary to insert additional features into the CAD model feature tree if a radical change in shape or performance is desired
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