Hostname: page-component-754f97d4cd-4vr9c Total loading time: 0 Render date: 2026-07-28T02:55:55.052Z Has data issue: false hasContentIssue false
  • English
  • Français

A physical modeling assistant for the preliminary stages of finite element analysis

Published online by Cambridge University Press:  27 February 2009

Donal P. Finn
Donal P. Finn
Affiliation:
Hitachi Dublin Laboratory, Trinity College, University of Dublin, Dublin 2, Ireland
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper describes work in progress aimed at developing an interactive modeling tool that assists engineers with the task of physical modeling in finite element analysis. Physical modeling precedes the numerical simulation phase of finite element analysis and involves applying modeling idealizations to real world physical systems so that complex engineering problems are more amenable to numerical computation. In the paper, the nature of physical modeling is explored, a cognitive model of how engineers are thought to model complex problems is described and based on this model a knowledge-based modeling assistant is proposed. The AI approach taken is based on Chandrasekaran's propose-critique-modify design model adapted for the task of physical modeling. Within this framework, the AI paradigms of case-based reasoning, derivational analogy and model-based reasoning are exploited. By representing fundamental thermal modeling scenarios as cases, complex physical systems can be modeled in a piecewise fashion. Derivational analogy permits generative adaptation of retrieved cases by using model-based engineering traces thereby providing a basis for critiquing case solutions. An initial prototype is described which has been implemented for the domain of convection heat transfer analysis.

Information

Type
Research Article
Information
AI EDAM , Volume 7 , Issue 4 , November 1993 , pp. 275 - 286
Copyright
Copyright © Cambridge University Press 1993

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Addanki, S., Cremonini, R. and Penberthy, J. S. 1991. Graph of Models, Artificial Intelligence 51, 145–177.CrossRefGoogle Scholar
Amarel, S. 1990. Workshop on design Technical Report LCSR-TR-160, Dept. of Computer Science, Rutgers University, New Brunswick.Google Scholar
Babuska, I. 1990. The problem of modeling the elastomechanics in engineering, Computer Methods in Applied Mechanics and Engineering 82, 155–182.CrossRefGoogle Scholar
Bathe, K. J., Lee, N. S. and Bucalem, M. L. 1990. On the use of hierachical models in engineering analysis, Computer Methods in Applied Mechanics and Engineering 82, 5–26.CrossRefGoogle Scholar
Carbonell, J. G. 1986. Derivational analogy: a theory of reconstructive problem solving and expertise acquisition. In Machine learning, eds Michalski, R. S., Carbonell, J. G., Mitchell, T. M., 371–392.Google Scholar
Chandrasekaran, B. 1990. Design problem solving: a task analysis, AI Magazine 11 59–71.Google Scholar
Falkenhainer, B. and Forbus, K. D. 1991. Compositional modeling: finding the right model for the job, Artificial Intelligence 51 95–143.CrossRefGoogle Scholar
Finn, D. P. 1993. Preliminary stages of engineering analysis and modeling—workshop summary, AI EDAM, this issue.CrossRefGoogle Scholar
Finger, S. and Dixon, J. R. 1989. A review of research in mechanical engineering design. Part II: representations, analysis and design for the life cycle, Research in Engineering Design 1, 121–137.CrossRefGoogle Scholar
Forbus, K. D. 1990. Qualitative physics: past, present, and future. Readings in Qualitative Reasoning about Physical Systems, New York: Morgan Kaufmann, pp. 11–39.CrossRefGoogle Scholar
Iwasaki, Y. 1992. An architecture for model-based evaluation of a preliminary design. 1992, Proceedings of the Workshop ‘Preliminary Stages of Engineering Analysis and Modeling.’ 2nd Conference on Artificial Intelligence in Design (AID ’92), CMU, Pittsburgh.Google Scholar
Ling, R. and Steinberg, L. 1993. MSG: a computer system for automated modeling of heat transfer, AI EDAM, this issue.CrossRefGoogle Scholar
Shephard, M. S., Baehmann, P. L., Georges, M. K. and Korngold, E. V., 1990. Framework for reliable generation and control of analysis idealisations, Computer Methods in Applied Mechanics and Engineering 82, 257–280.CrossRefGoogle Scholar
Tworzydlo, W. W. and Oden, J. T. 1990. Towards an automated environment in computational mechanics, Computer Methods in Applied Mechanics and Engineering, 104, 87–143. North Holland.CrossRefGoogle Scholar
Weld, D. S. and DeKleer, J. 1990. Multiple ontologies and automated modeling, Readings in Qualitative Reasoning about Physical Systems pp. 481–483. New York: Morgan Kaufmann.Google Scholar
Yip, K. 1993. Model simplification by asymptotic order of magnitude reasoning, In: Working Papers Qualitative Reasoning ’93 (QR ’93), ed Ward, D. pp. 266–272. Seattle: University of Washington.Google Scholar
Zienkiewicz, O. C. and Zhu, J. Z. 1990. The three R's of engineering analysis and error estimation and adaptivity, Computer Methods in Applied Mechanics and Engineering 82, 95–113.CrossRefGoogle Scholar
Zhukauskas, A. 1989. High Performance Single Phase Heat Exchangers. New York: Hemisphere Publishing Corporation.Google Scholar