  Complex engineering systems require integration of simulation of sub-systems
and calculation of metrics to drive design decisions. This paper introduces a
methodology for designing computational or physical experiments for
system-level uncertainty mitigation purposes. The methodology follows a
previously determined problem ontology, where physical, functional and modeling
architectures are decided upon. By carrying out sensitivity analysis techniques
utilizing system-level tools, critical epistemic uncertainties can be
identified. Afterwards, a framework is introduced to design specific
computational and physical experimentation for generating new knowledge about
parameters, and for uncertainty mitigation. The methodology is demonstrated
through a case study on an early-stage design Blended-Wing-Body (BWB) aircraft
concept, showcasing how aerostructures analyses can be leveraged for mitigating
system-level uncertainty, by computer experiments or guiding physical
experimentation. The proposed methodology is versatile enough to tackle
uncertainty management across various design challenges, highlighting the
potential for more risk-informed design processes.
