A Methodology for Identifying Experiments for Uncertainty Mitigation in Complex Multi-Disciplinary Design

The dissertation develops a systematic methodology to identify and mitigate sources of uncertainty in aircraft design, focusing on epistemic uncertainties such as model-form and parameter uncertainties. This approach enhances Systems Engineering by integrating modeling and simulation components and mapping simulation requirements onto a proposed modeling architecture. It addresses the challenge of identifying critical uncertainties in complex, multi-disciplinary aerospace systems through sensitivity analysis, investigating the impact of surrogate modeling and subjectivity in input probability density functions, and addressing the inverse problem for parameter uncertainty allocation. The final focus is on designing computational and physical experiments guided by computational experiments to reduce epistemic uncertainty, converting the full-scale modeling problem into a constrained optimization problem to represent full-scale behavior effectively in reduced-scale physical experiments.