Sensitivity analysis plays a critical role in quantifying uncertainty in the design of engineering systems. A variance-based global sensitivity analysis is often used to rank the importance of input factors, based on their contribution to the variance of the output quantity of interest. However, this analysis assumes that all input variability can be reduced to zero, which is typically not the case in a design setting. Distributional sensitivity analysis (DSA) instead treats the uncertainty reduction in the inputs as a random variable, and defines a variance-based sensitivity index function that characterizes the relative contribution to the output variance as a function of the amount of uncertainty reduction. This paper develops a computationally efficient implementation for the DSA formulation and extends it to include distributions commonly used in engineering design under uncertainty. Application of the DSA method to the conceptual design of a commercial jetliner demonstrates how the sensitivity analysis provides valuable information to designers and decision-makers on where and how to target uncertainty reduction efforts.
Variance-Based Sensitivity Analysis to Support Simulation-Based Design Under Uncertainty
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 5, 2016; final manuscript received June 30, 2016; published online September 12, 2016. Assoc. Editor: Samy Missoum.
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Opgenoord, M. M. J., Allaire, D. L., and Willcox, K. E. (September 12, 2016). "Variance-Based Sensitivity Analysis to Support Simulation-Based Design Under Uncertainty." ASME. J. Mech. Des. November 2016; 138(11): 111410. https://doi.org/10.1115/1.4034224
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