Hostname: page-component-7b7567c8c-7pwfn Total loading time: 0 Render date: 2026-11-12T22:28:46.626Z Has data issue: false hasContentIssue false
  • English
  • Français

MSG: A computer system for automated modeling of heat transfer

Published online by Cambridge University Press:  27 February 2009

Sui-ky Ringo Ling,
Louis Steinberg and
Yogesh Jaluria
Sui-ky Ringo Ling
Affiliation:
Department of Computer Science
Louis Steinberg
Affiliation:
Department of Computer Science
Yogesh Jaluria
Affiliation:
Department of Mechanical and Aerospace Engineering, Rutgers University, New Brunswick, NJ 08903, USA
Get accessRights & Permissions [Opens in a new window]

Abstract

The task of modeling, i.e., of creating a set of equations that can be used to predict the behavior of a physical object, is a key step in engineering analysis. This paper describes a computer system, MSG, for generating mathematical models to analyze physical systems involving heat transfer behavior. MSG is motivated by the need for modeling in an automated design process. The models are sets of equations which may include algebraic equations, ordinary differential equations and partial differential equations. MSG uses the strong domain theory to guide model construction in three sequential tasks: identify regions of interests on an object, determine relevant heat transfer and energy storage processes, and transform these processes into equations. The decisions in these tasks are guided by estimates of variation in temperature and material property, and the relative strengths of heat transfer processes.

Information

Type
Research Article
Information
AI EDAM , Volume 7 , Issue 4 , November 1993, pp. 287 - 300
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. Graphs of models, Artificial Intelligence 51 (1–3), 145–177.CrossRefGoogle Scholar
Arpaci, S. 1966. Conduction Heat Transfer. Reading, MA: Addison-Wesley.Google Scholar
Falkenhainer, B. and Forbus, K. 1991. Compositional modeling: finding the right model for the job, Artificial Intelligence 51 (1–3), 95–144.CrossRefGoogle Scholar
Falkenhainer, B. 1992. A look at idealization. In Working Notes of the AAAl-92 Workshop on Approximation and Abstraction of Computational Theories. San Jose, CA: AAAI.Google Scholar
Finn, D. P., Grimson, J. B. and Harty, N. M. 1992. An intelligent modelling assistant for preliminary analysis in design. Proceedings of the 2nd International Conference on Artificial Intelligence in Design. Pittsburgh, PA: Kluwer.Google Scholar
Gelsey, A. 1989. Automated Physical Modeling, Proceedings of the 11th International Joint Conference on Artificial Intelligence, Detroit, MI.Google Scholar
Incropera, F.P. and DeWitt, D. P. 1990. Fundamantals of Heat and Mass Transfer, 3rd edn. New York: John Wiley.Google Scholar
Jaluria, Y. and Torrance, K. E. 1986. Computational Heat Transfer. New York: Hemisphere.Google Scholar
Jamalabad, V. R., Langrana, N. A. and Jaluria, Y. 1990. Heuristic design of a material processing component. Proceedings of the 2nd Design Theory and Methodology Conference, pp. 119–126, Chicago IL: ASME.Google Scholar
Panton, R. L. 1984. Incompressible Flow. New York: John Wiley.Google Scholar
Viswanath, R. and Jaluria, Y. 1991. Knowledge-based system for the computer-aided design of ingot casting processes, Engineering with Computers 7, 109–120.CrossRefGoogle Scholar
Weld, D. S. and Addanki, S. 1990. Query-Directed Approximation. Technical Report 90–12–02, Computer Science Department, University of Washington.Google Scholar
Weld, D. S. Approximation reformulations, Proceedings of 8th National Conference on Artificial Intelligence, pp. 407–412, Cambridge, MA: AAAI.Google Scholar
Weld, D. S. 1992. Reasoning about model accuracy, Artificial Intelligence 56, 255–300.CrossRefGoogle Scholar