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Title: Variance-Based Sensitivity Analysis to Support Simulation-Based Design Under Uncertainty

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.
Authors:
 [1] ;  [2] ;  [1]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Aeronautics and Astronautics
  2. Texas A & M Univ., College Station, TX (United States). Dept. of Mechanical Engineering
Publication Date:
Grant/Contract Number:
SC0009637; NNX14AC73A; FG02-08ER2585; SC0009297
Type:
Accepted Manuscript
Journal Name:
Journal of Mechanical Design
Additional Journal Information:
Journal Volume: 138; Journal Issue: 11; Journal ID: ISSN 1050-0472
Publisher:
ASME
Research Org:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING
OSTI Identifier:
1423944

Opgenoord, Max M. J., Allaire, Douglas L., and Willcox, Karen E.. Variance-Based Sensitivity Analysis to Support Simulation-Based Design Under Uncertainty. United States: N. p., Web. doi:10.1115/1.4034224.
Opgenoord, Max M. J., Allaire, Douglas L., & Willcox, Karen E.. Variance-Based Sensitivity Analysis to Support Simulation-Based Design Under Uncertainty. United States. doi:10.1115/1.4034224.
Opgenoord, Max M. J., Allaire, Douglas L., and Willcox, Karen E.. 2016. "Variance-Based Sensitivity Analysis to Support Simulation-Based Design Under Uncertainty". United States. doi:10.1115/1.4034224. https://www.osti.gov/servlets/purl/1423944.
@article{osti_1423944,
title = {Variance-Based Sensitivity Analysis to Support Simulation-Based Design Under Uncertainty},
author = {Opgenoord, Max M. J. and Allaire, Douglas L. and Willcox, Karen E.},
abstractNote = {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.},
doi = {10.1115/1.4034224},
journal = {Journal of Mechanical Design},
number = 11,
volume = 138,
place = {United States},
year = {2016},
month = {9}
}