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A Methodology to Manage Uncertainty During System-Level Conceptual Design

[+] Author Affiliations
Jay D. Martin

Applied Research Laboratory, State College, PA

Timothy W. Simpson

Pennsylvania State University, University Park, PA

Paper No. DETC2005-84984, pp. 1183-1193; 11 pages
  • ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2: 31st Design Automation Conference, Parts A and B
  • Long Beach, California, USA, September 24–28, 2005
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 0-7918-4739-X | eISBN: 0-7918-3766-1
  • Copyright © 2005 by ASME


Current design decisions must be made while considering uncertainty in both models and inputs to the design. In most cases this uncertainty is ignored in the hope that it is not important to the decision making process. This paper presents a methodology for managing uncertainty during system-level conceptual design of complex multidisciplinary systems. The methodology is based upon quantifying the information available in computationally expensive subsystem models with more computationally efficient kriging models. By using kriging models, the computational expense of a Monte Carlo simulation to assess the impact of the sources of uncertainty on system-level performance parameters becomes tractable. The use of a kriging model as an approximation to an original computer model introduces model uncertainty, which is included as part of the methodology. The methodology is demonstrated as a decision making tool for the design of a satellite system.

Copyright © 2005 by ASME



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