Research Papers

A Hybrid Sensitivity Analysis for Use in Early Design

[+] Author and Article Information
Ryan S. Hutcheson

 Texas A&M University, College Station, TX 77843rhutcheson@tamu.edu

Daniel A. McAdams

 Texas A&M University, College Station, TX 77843dmcadams@tamu.edu

Simulations were run on an Apple Mac Pro with two 3 GHz dual-core Xeon processors and 8 Gbyte of 667 MHz RAM.

J. Mech. Des 132(11), 111007 (Nov 15, 2010) (10 pages) doi:10.1115/1.4001408 History: Received March 31, 2009; Revised February 15, 2010; Published November 15, 2010; Online November 15, 2010

Sensitivity analyses are frequently used during the design of engineering systems to qualify and quantify the effect of parametric variation in the performance of a system. Two primary types of sensitivity analyses are generally used: local and global. Local analyses, generally involving derivative-based measures, have a significantly lower computational burden than global analyses but only provide measures of sensitivity around a nominal point. Global analyses, generally performed with a Monte Carlo sampling approach, and variation-based measures provide a complete description of sensitivity but incur a large computational burden and require information regarding the distributions of the design parameters in a concept. Local analyses are generally suited to the early stages of design when parametric information is limited, and a large number of concepts must be evaluated (necessitating a light computational burden). Global analyses are more suited to the later stages of design when more information about parametric distributions is available and fewer concepts are under consideration. Current derivative-based local approaches provide a different and incompatible set of measures than a global variation-based analysis. This makes a direct comparison of local to global measures ill posed. To reconcile local and global sensitivity analyses, a hybrid local variation-based sensitivity (HyVar) approach is presented. This approach has a similar computational burden to a local approach but produces measures or percentage contributions. The HyVar approach is directly comparable to global variation-based approaches. In this paper, the HyVar sensitivity analysis method is developed in the context of a functional based behavioral modeling framework. An example application of the method is presented along with a summary of results produced from a more comprehensive example.

Copyright © 2010 by American Society of Mechanical Engineers
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Grahic Jump Location
Figure 1

Example functional model

Grahic Jump Location
Figure 2

HyVar results charted

Grahic Jump Location
Figure 3

Global variation-based results charted

Grahic Jump Location
Figure 4

Hybrid racecar powertrain functional model

Grahic Jump Location
Figure 5

Autocross sensitivities for best concepts



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