A Methodology to Identify Physical or Computational Experiment Conditions for Uncertainty Mitigation
This work addresses uncertainty management for engineering design processes, but it appears incremental as it builds on existing problem ontologies and sensitivity analysis techniques.
The paper tackles the problem of managing uncertainty in complex engineering systems by introducing a methodology to design computational or physical experiments for system-level uncertainty mitigation, demonstrated through a case study on a Blended-Wing-Body aircraft concept.
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.