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An Inverse Analysis Method for Design Optimization With Both Statistical and Fuzzy Uncertainties

[+] Author Affiliations
Liu Du, Kyung K. Choi

University of Iowa, Iowa City, IA

Paper No. DETC2006-99731, pp. 1225-1234; 10 pages
  • ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1: 32nd Design Automation Conference, Parts A and B
  • Philadelphia, Pennsylvania, USA, September 10–13, 2006
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 0-7918-4255-X | eISBN: 0-7918-3784-X
  • Copyright © 2006 by ASME


Structural analysis and design optimization have recently been extended to consider various uncertainties. If the statistical data for the uncertainties are sufficient to construct the input distribution function, the uncertainties can be treated as random variables and RBDO is used; otherwise, the uncertainties can be treated as fuzzy variables and PBDO is used. However, many structural design problems include both uncertainties with sufficient data and uncertainties with insufficient data. For these problems, RBDO will yield an unreliable design since the distribution functions of uncertainties are not believable. On the other hand, treating the random variables as fuzzy variables and invoking PBDO may yield too conservative design with a higher optimum cost. This paper proposes a new design formulation using the performance measure approach (PMA). For the inverse analysis, this paper proposes a new most probable/possible point (MPPP) search method called maximal failure search (MFS), which is an integration of the enhanced hybrid mean value method (HMV+) and maximal possibility search (MPS) method. Some mathematical and physical examples are used to demonstrate the proposed inverse analysis method and design formulation.

Copyright © 2006 by ASME
Topics: Design , Optimization



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