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Identifying reformulations of mechanical parametric design constraints

Published online by Cambridge University Press:  27 February 2009

John D. Watton  and
James R. Rinderle
John D. Watton
Affiliation:
Applied Mathematics and Computer Technology Division, Alcoa Laboratories, Alcoa Center, PA 15069, U.S.A.
James R. Rinderle
Affiliation:
Mechanical Engineering Department, Carnegie–Mellon University, Pittsburgh, PA 15213, U.S.A.
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Abstract

The complexity of mechanical design is reflected in the complexity of the design constraints which relate functional requirements to design parameters. Reformulations of the design constraints can significantly reduce this complexity. This is accomplished by a transformation to alternative design parameters, such as a critical ratio, a non-dimensional parameter, or a simple difference; e.g. the ratio of surface area to volume for heat transfer loss, the Reynold's number in fluid mechanics, or the velocity difference across a fluid coupling. We have developed a method by which the alternative parameters are chosen for physical significance and for the ability to create a more direct correspondence to functional behavior as determined by measures of serial and block decomposability of the constraints. Rules have been developed for the creation of physically significant new parameters from the algebraic combination of the original parameters. The rules are based on engineering principles and rely on knowledge about what a parameter physically represents rather than other qualities such as dimensions. A computer based system, called EUDOXUS, has been developed to automate this procedure. The system operates on a set of design constraints to produce sets of transformed constraints in terms of alternative design parameters. The method and its implementation have demonstrated successful results for many highly nonlinear and highly coupled parameterized designs from many mechanical engineering domains.

Type
Research Article
Information
AI EDAM , Volume 5 , Issue 3 , August 1991 , pp. 173 - 188
Copyright
Copyright © Cambridge University Press 1991

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References

Aho, A. V., Hopcroft, J. E. and Ullman, J. D. 1983. Data Structures and Algorithms. Reading, MA: Addison-Wesley.Google Scholar
Buckingham, E. 1914. ‘On physically similar systems: illustrations of the use of dimensional equations’. Physical Review 4, 345.CrossRefGoogle Scholar
Dechter, R., and Pearl, J. 1988. Network-based heuristics for constraint-satisfaction problems’. Artificial Intelligence 34, 1–38.CrossRefGoogle Scholar
Huntley, H. E. 1953. Dimensional Analysis. London: McDonald.Google Scholar
Incropera, F. P. and Dewitt, D. P. 1981. Fundamentals of Heat Transfer. New York: John Wiley.Google Scholar
Kline, S. J. 1986. Similitude and Approximation Theory. New York: Springer-Verlag.CrossRefGoogle Scholar
Mackworth, A. K. 1977. Consistency in networks of relations. Artificial Intelligence 8, 99–118.CrossRefGoogle Scholar
Montanari, U. 1974. Networks of constraints: fundamental properties and applications to picture processing. Information Sciences 7, 95–132.CrossRefGoogle Scholar
Streeter, V. L. and Wylie, E. B. 1979. Fluid Mechanics. New York: McGraw-Hill.Google Scholar
Watton, J. D. 1989. Automatic Reformulation of Mechanical Design Constraints to Enhance Qualitative and Quantitative Design Decisions. PhD dissertation, Carnegie-Mellon University.Google Scholar
Watton, J. D. and Rinderle, J. R. 1991. Improving mechanical design decisions with alternate formulations of constraints. Journal of Engineering Design, 2, 55–68.CrossRefGoogle Scholar