Quantification of margins and mixed uncertainties using evidence theory and stochastic expansions

• Published in: Reliability engineering & system safety 2015-06, Vol.138, p.59-72
• Main Authors: Shah, Harsheel, Hosder, Serhat, Winter, Tyler
• Format: Article
• Language: English
• Subjects: Boundaries; Chaos theory; Computer simulation; Evidence theory; Intervals; Margins and uncertainty quantification; Mathematical analysis; Mathematical models; Point collocation; Polynomial chaos; Reliability; Reliability engineering; Representation of uncertainty; Stochasticity; System safety; Uncertainty
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2015.01.012

The objective of this paper is to implement Dempster–Shafer Theory of Evidence (DSTE) in the presence of mixed (aleatory and multiple sources of epistemic) uncertainty to the reliability and performance assessment of complex engineering systems through the use of quantification of margins and uncertainties (QMU) methodology. This study focuses on quantifying the simulation uncertainties, both in the design condition and the performance boundaries along with the determination of margins. To address the possibility of multiple sources and intervals for epistemic uncertainty characterization, DSTE is used for uncertainty quantification. An approach to incorporate aleatory uncertainty in Dempster–Shafer structures is presented by discretizing the aleatory variable distributions into sets of intervals. In view of excessive computational costs for large scale applications and repetitive simulations needed for DSTE analysis, a stochastic response surface based on point-collocation non-intrusive polynomial chaos (NIPC) has been implemented as the surrogate for the model response. The technique is demonstrated on a model problem with non-linear analytical functions representing the outputs and performance boundaries of two coupled systems. Finally, the QMU approach is demonstrated on a multi-disciplinary analysis of a high speed civil transport (HSCT). •Quantification of margins and uncertainties (QMU) methodology with evidence theory.•Treatment of both inherent and epistemic uncertainties within evidence theory.•Stochastic expansions for representation of performance metrics and boundaries.•Demonstration of QMU on an analytical problem.•QMU analysis applied to an aerospace system (high speed civil transport).