A Methodology to Manage System-level Uncertainty During Conceptual Design

[+] Author and Article Information
Jay D. Martin

 The Applied Research Laboratory, State College, PA 16804-0030

Timothy W. Simpson

 The Pennsylvania State University, University Park, PA 16802

J. Mech. Des 128(4), 959-968 (Jan 11, 2006) (10 pages) doi:10.1115/1.2204975 History: Received August 23, 2005; Revised January 11, 2006

Current design decisions must be made while considering uncertainty in both models of the design and inputs to the design. In most cases, high fidelity models are used with the assumption that the resulting model uncertainties are insignificant to the decision making process. This paper presents a methodology for managing uncertainty during system-level conceptual design of complex multidisciplinary systems. This methodology is based upon quantifying the information available in a set of observations of 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 © 2006 by American Society of Mechanical Engineers
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Figure 4

Revised system diagram using kriging models

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Figure 5

Bar charts of the system input/output correlations

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Figure 1

Flowchart of proposed methodology

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Figure 2

Block diagram of the process

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Figure 3

Diagram of system and subsystem models



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