| Summary: | The role of uncertainty management is increasingly being recognized in the design of complex systems that require multi-level multidisciplinary analyses. Most previous studies in this direction have only dealt with aleatory uncertainty (i.e., natural or physical variability). However, various modeling errors, assumptions and approximations, measurement errors, and sparse and imprecise information introduce additional epistemic uncertainty in model prediction. Therefore, an approach to multidisciplinary uncertainty analysis and system design that addresses both aleatory and epistemic uncertainty is needed. The objective of this dissertation is to develop a methodology that provides decision support to engineers for multidisciplinary design and analysis of systems under aleatory uncertainty (i.e., natural or physical variability) and epistemic uncertainty (due to sparse and imprecise data).
Specifically, the dissertation accomplishes this objective through: (1) Development of a probabilistic approach for the representation of epistemic uncertainty; (2) Development of a probabilistic framework for the propagation of both aleatory and epistemic uncertainty; (3) Development of formulations and algorithms for design optimization under aleatory and epistemic uncertainty, from the perspective of system robustness and reliability; (4) Development of a framework for uncertainty propagation in multidisciplinary system analysis; and (5) Development of formulations and algorithms for design optimization under aleatory and epistemic uncertainty for multidisciplinary systems, from the perspective of system robustness and reliability.
The methodology developed in this dissertation is illustrated through problems related to spacecraft design and analysis, such as the conceptual upper-stage design of a two-stage-to-orbit vehicle, and design and analysis of a fire detection satellite.
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