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Constraint Management of Reduced Representation Variables in Decomposition-Based Design Optimization

[+] Author Affiliations
Michael J. Alexander, Panos Y. Papalambros

University of Michigan, Ann Arbor, MI

James T. Allison

The Mathworks, Inc., Natick, MA

David J. Gorsich

U.S. Army TARDEC, Warren, MI

Paper No. DETC2010-28788, pp. 755-764; 10 pages
doi:10.1115/DETC2010-28788
From:
  • ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1: 36th Design Automation Conference, Parts A and B
  • Montreal, Quebec, Canada, August 15–18, 2010
  • Conference Sponsors: Design Engineering Division and Computers in Engineering Division
  • ISBN: 978-0-7918-4409-0 | eISBN: 978-0-7918-3881-5
  • Copyright © 2010 by ASME

abstract

In decomposition-based design optimization strategies, such as Analytical Target Cascading (ATC), it is sometimes necessary to use reduced dimensionality representations to approximate functions of large dimensionality whose values need to be exchanged among subproblems. The reduced representation variables may not be physically meaningful, and it can become challenging to constrain them properly and define the model validity region. For example, in coordination strategies like ATC, representing vector-valued coupling variables with improperly constrained reduced representation variables can lead to poor performance or convergence failure. This paper examines two approaches for constraining effectively the model validity region of reduced representation variables based on proper orthogonal decomposition: a penalty value-based heuristic and a support vector domain description. An ATC application on electric vehicle design helps to illustrate the concepts discussed.

Copyright © 2010 by ASME
Topics: Design , Optimization

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