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Multilevel modelling for engineering design optimization

Published online by Cambridge University Press:  27 February 2009

Thomas Ellman ,
John Keane ,
Mark Schwabacher  and
Ke-Thia Yao
Thomas Ellman
Affiliation:
Department of Computer Science, Hill Center for Mathematical Sciences, Rutgers University, Piscataway, NJ 08855, U.S.A.
John Keane
Affiliation:
Department of Computer Science, Hill Center for Mathematical Sciences, Rutgers University, Piscataway, NJ 08855, U.S.A.
Mark Schwabacher
Affiliation:
Department of Computer Science, Hill Center for Mathematical Sciences, Rutgers University, Piscataway, NJ 08855, U.S.A.
Ke-Thia Yao
Affiliation:
Department of Computer Science, Hill Center for Mathematical Sciences, Rutgers University, Piscataway, NJ 08855, U.S.A.
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Abstract

Physical systems can be modelled at many levels of approximation. The right model depends on the problem to be solved. In many cases, a combination of models will be more effective than a single model. Our research investigates this idea in the context of engineering design optimization. We present a family of strategies that use multiple models for unconstrained optimization of engineering designs. The strategies are useful when multiple approximations of an objective function can be implemented by compositional modelling techniques. We show how a compositional modelling library can be used to construct a variety of locally calibratable approximation schemes that can be incorporated into the optimization strategies. We analyze the optimization strategies and approximation schemes to formulate and prove sufficient conditions for correctness and convergence. We also report experimental tests of our methods in the domain of sailing yacht design. Our results demonstrate dramatic reductions in the CPU time required for optimization, on the problems we tested, with no significant loss in design quality.

Keywords

DesignOptimizationApproximationModelling
Type
Articles
Information
AI EDAM , Volume 11 , Issue 5 , November 1997 , pp. 357 - 378
Copyright
Copyright © Cambridge University Press 1997

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References

REFERENCES

Addanki, S., Cremonini, R., & Penberthy, S. (1991). Graphs of models. Artificial Intelligence 51, 145–178Google Scholar
Allgower, E., & Georg, K. (1990). Numerical Continuation Methods. Springer-Verlag, New York.CrossRefGoogle Scholar
Ashby, D.L., Dudley, M.R., Iguchi, S.K., Browne, L., & Katz, J. (1992). Potential Flow Theory and Operation Guide for the Panel Code PAMRC 12.Google Scholar
Cagen, J., & Williams, B. (1993). First order necessary conditions for robust optimality. Proc. Design Automation Conf. ASME, Albuquerque, NM, 539–549.Google Scholar
Conte, S., & de Boor, C. (1980). Elementary Numerical Analysis: An Algorithmic Approach. McGraw-Hill, New York.Google Scholar
Dahlquist, G., & Bjorck, A. (1974). Numerical Methods. Prentice-Hall, Englewood Cliffs, NJ.Google Scholar
Dennis, J. & Schnabel, R. (1983). Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Prentice-Hall, Englewood Cliffs, NJ.Google Scholar
Ellman, T., Keane, J., & Schwabacher, M. (1992). The Rutgers CAP project design associate. Report No. CAP-TR-7. Department of Computer Science, Rutgers University, New Brunswick, NJ.Google Scholar
Ellman, T., Keane, J., & Schwabacher, M. (1993). Intelligent model selection for hillclimbing search in computer-aided design. Proc. Eleventh Natl. Conf. on Artificial Intelligence. Washington, DC, 594–599Google Scholar
Ellman, T., Keane, J., Schwabacher, M., & Murata, T. (1995). A transformation system for interactive reformulation of design optimization strategies. Proc. Tenth Knowledge-Based Software Engineering Conf. Boston, MA, 44–51.Google Scholar
Ellman, T., Keane, J., Banerjee, A., & Armhold, G. (1997). A transformation system for interactive reformulation of design optimization strategies. Research in Engineering Design (submitted for review).Google Scholar
Falkenhainer, B., & Forbus, K. (1991). Compositional modelling: Finding the right model for the job. Artificial Intelligence 51, 95–144.CrossRefGoogle Scholar
Gelsey, A. (1995). Intelligent automated quality control for computational simulation. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 9(5), 387–400.CrossRefGoogle Scholar
Gill, P., Murray, W., & Wright, M. (1981). Practical Optimization. Academic Press, London.Google Scholar
Katz, J., & Plotkin, A. (1991). Low-Speed Aerodynamics: From Wing Theory to Panel Methods. McGraw-Hill, New York.Google Scholar
Lawrence, C., Zhou, J., & Tits, A. (1995) User's guide for CFSQP version 2.3: A Code for solving (large scale) constrained nonlinear (mini-max) optimization problems, generating iterates satisfying all inequality constraints. Report No. TR-94–16r1. Institute for Systems Research, University of Maryland.Google Scholar
Letcher, J. (1975). Sailing hull hydrodynamics, with reanalysis of the Antiope data. Transactions of the Society of Navel Architects and Marine Engineers 83.Google Scholar
Letcher, J. (1991). The Aero/Hydro VPP Manual. Aero/Hydro, Inc., South-West Harbor, ME.Google Scholar
Letcher, J., Marshall, J., Oliver, J., & Salvesen, N. (1987). Stars and stripes. Scientific American 257(2), 24–32.CrossRefGoogle Scholar
Moré, J.J., & Wright, S.J. (1995). Optimization Software Guide. SIAM. Philadelphia.Google Scholar
Nayak, P. (1994). Causal approximations. Artificial Intelligence 70, 277–334.CrossRefGoogle Scholar
Newman, J. & Wu, T. (1973). A generalized slender body theory for fishlike forms. Journal of Fluid Mechanics 57(4).CrossRefGoogle Scholar
Orelup, M.F., Dixon, J.R., Cohen, P.R., & Simmons, M.K. (1988). Dominic ii: Meta-level control in iterative redesign. Proc. Natl. Conf. on Artificial Intelligence pp. 25–30. MIT Press, Cambridge, MA.Google Scholar
Osio, G., & Amon, C. (1996). An engineering design methodology with multistage Bayesian surrogates and optimal sampling. Research in Engineering Design 8, 189–206.Google Scholar
Pearl, J. (1984). Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addision-Wesley, Reading, MA.Google Scholar
Peressini, A., Sullivan, F., & Uhl, J. (1988). The Mathematics of Nonlinear Programming. Springer-Verlag, New York.CrossRefGoogle Scholar
Powell, D. (1990). Inter-gen: A hybrid approach to engineering design optimization. Ph.D. Thesis. Rensselaer Polytechnic Institute, Department of Computer Science.Google Scholar
Press, W., Flannery, B., Teukolsky, S., & Vetterling, W. (1986). Numerical Recipes. Cambridge University Press, New York.Google Scholar
Rudin, W. (1964). Principles of Mathematical Analysis. McGraw-Hill, New York.Google Scholar
Sacerdoti, E.D. (1974). Planning in a hierarchy of abstraction spaces. Artificial Intelligence 5, 115–135.Google Scholar
Simon, H. (1981). The Sciences of the Artificial. MIT Press, Cambridge, MA.Google Scholar
Sobieszczanski-Sobieski, J. (1982). A linear decomposition method for large optimization problems—Blueprint for development. Report No. NASA TM-83248. National Aeronautics and Space Administration.Google Scholar
Sobieszczanski-Sobieski, J., & Hatfka, R. (1996). Multidisciplinary aerospace design optimization: Survey of recent developments. Report No. AIAA 96–0711. American Institute of Aeronautics and Astronautics.CrossRefGoogle Scholar
Tong, S.S. (1990). Coupling symbolic manipulation and numerical simulation for complex engineering designs. Intelligent Mathematical Software Systems, pp. 241–252. North-Holland, New York.Google Scholar
Vanderplaats, G. (1984). Numerical Optimization Techniques for Engineering Design: With Applications. McGraw-Hill, New York.Google Scholar
Weems, K., Lin, W., & Chen, H. (1994). Calculations of viscous nonlinear free-surface flows using an interactive zonal approach. Proc. CFD Workshop of the Ship Research Institute of Japan.Google Scholar
Weld, D. (1990). Approximation reformulations. Proc. Eighth Natl. Conf. on Artificial Intelligence. MIT Press, Boston.Google Scholar
Yao, K.-T., & Gelsey, A. (1996). Intelligent automated grid generation for numerical simulations. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10(3), 215–234.CrossRefGoogle Scholar