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Knowledge Composition for Efficient Analysis Problem Formulation: Part 1 — Motivation and Requirements

[+] Author Affiliations
Manas Bajaj, Russell S. Peak, Christiaan J. J. Paredis

Georgia Institute of Technology, Atlanta, GA

Paper No. DETC2007-35049, pp. 789-801; 13 pages
doi:10.1115/DETC2007-35049
From:
  • ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2: 27th Computers and Information in Engineering Conference, Parts A and B
  • Las Vegas, Nevada, USA, September 4–7, 2007
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 0-7918-4803-5 | eISBN: 0-7918-3806-4
  • Copyright © 2007 by ASME

abstract

In simulation-based design a key challenge is to formulate and solve analysis problems efficiently to evaluate a variety of design alternatives. Numerically solving analysis problems has benefited from advancements in commercial off-the-shelf mathematical solvers and computational capabilities. However, the formulation of analysis problems for a given set of design alternatives is still typically a laborious and costly process. In the scope of design alternatives with variable topology multi-body (VTMB) characteristics, these papers (Part 1 and Part 2) present research that addresses the following primary question: How can we improve the efficiency of the analysis problem formulation process for VTMB design alternatives? The objective of this paper (Part 1) is to identify requirements for a methodology that answers this. The methodology is formulates analysis problems for VTMB design alternatives based on decisions taken by analysts and independent of the solution method (such as finite element analysis) and the solver. This paper presents a gap analysis using an example VTMB problem and identifies key inadequacies in existing approaches for analysis problem formulation. Based on the gap analysis and technical background, we present five main requirements relating to (a) key drivers for efficiently creating analysis models; (b) abstracting and formalizing analysis knowledge for composing analysis models; and (c) automatically creating, reconfiguring and verifying analysis models.

Copyright © 2007 by ASME

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