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Sensitivity analysis methods for mitigating uncertainty in engineering system design
Author(s) -
Curran Qinxian Chelsea,
Allaire Douglas,
Willcox Karen E.
Publication year - 2018
Publication title -
systems engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.474
H-Index - 50
eISSN - 1520-6858
pISSN - 1098-1241
DOI - 10.1002/sys.21422
Subject(s) - sensitivity (control systems) , uncertainty analysis , computer science , engineering design process , sensitivity analysis , risk analysis (engineering) , schedule , context (archaeology) , entropy (arrow of time) , variance (accounting) , process (computing) , reliability engineering , operations research , industrial engineering , mathematical optimization , engineering , simulation , mathematics , mechanical engineering , medicine , paleontology , physics , accounting , quantum mechanics , electronic engineering , business , biology , operating system
For many engineering systems, current design methodologies do not adequately quantify and manage uncertainty as it arises during the design process, which can lead to unacceptable risks, increases in programmatic cost, and schedule overruns. This paper develops new sensitivity analysis methods that can be used to better understand and mitigate the effects of uncertainty in system design. In particular, a new entropy‐based sensitivity analysis methodology is introduced, which apportions output uncertainty into contributions due to not only the variance of input factors and their interactions, but also to features of the underlying probability distributions that are related to distribution shape and extent. Local sensitivity analysis techniques are also presented, which provide computationally inexpensive estimates of the change in output uncertainty resulting from design modifications. The proposed methods are demonstrated on an engineering example to show how they can be used in the design context to systematically manage uncertainty budgets—which specify the allowable level of uncertainty for a system—by helping to identify design alternatives, evaluate trade‐offs between available options, and guide decisions regarding the allocation of resources.