NDLI: Estimating the impact of large design changes on field problems

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Estimating the impact of large design changes on field problems

Content Provider	ACM Digital Library
Author	Gopalakrishnan, Sankara Hari Suresh, Krishnan
Abstract	CAD systems today are feature-based in that they represent geometry via a collection of features, and associated feature parameters. During the design process, feature parameters are optimized to meet various design objectives including functionality, manufacturability, aesthetics, etc. This paper focuses on predicting the impact of parametric feature changes on quantities of interest, such as average stress within a given region, etc. When the parametric change is sufficiently small, one can rely on classic shape sensitivity and reanalysis methods to rapidly predict the desired quantity of interest. However, when the parametric changes are large, both methods are unreliable. In this paper, we propose an alternate methodology for predicting the impact of large parametric feature changes on field problems, and thereby opening fundamentally new avenues for design exploration and parametric optimization. The proposed methodology is based on the novel concept of feature sensitivity, a generalization of topological sensitivity. Topological sensitivity captures the first-order change in quantities of interest when a small spherical hole is created within an existing geometry. This is generalized here to an arbitrary collection of internal or boundary features. Subsequently, we demonstrate its application to large parametric feature changes. The methodology is illustrated via numerical experiments involving a 2-D scalar field problem.
Starting Page	205
Ending Page	215
Page Count	11
File Format	PDF
ISBN	9781595936660
DOI	10.1145/1236246.1236275
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-06-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Design parameters Parametric optimization Feature sensitivity Topological sensitivity Features
Content Type	Text
Resource Type	Article

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