Reliability-based design optimization of multidisciplinary system under aleatory and epistemic uncertainty

This paper proposes formulations and algorithms for reliability-based design optimization (RBDO) of both single and multidisciplinary systems under both aleatory uncertainty (i.e., natural or physical variability) and epistemic uncertainty (i.e., imprecise probabilistic information). The proposed formulations specifically deal with epistemic uncertainty arising from interval data. When the only information available for an input variable is in the form of interval data, it is likely that the distribution type for the input variable is not known or cannot be specified accurately. This paper uses a four-parameter flexible Johnson family of distributions to represent the uncertainty described by interval data. An efficient approach is proposed to decouple the design analysis from the uncertainty analysis. The proposed methodology for multidisciplinary system optimization does not require any coupled system level analysis. The proposed methods are illustrated through several example problems.

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