A Methodology to Identify Physical or Computational Experiment Conditions for Uncertainty Mitigation

TL;DR

This paper presents a comprehensive methodology for designing experiments, both physical and computational, aimed at reducing uncertainties in complex engineering system designs, demonstrated through an aircraft case study.

Contribution

It introduces a systematic approach to identify and mitigate epistemic uncertainties using sensitivity analysis and tailored experiments within a system-level framework.

Findings

Effective uncertainty mitigation in aircraft design demonstrated

Versatile methodology applicable to various engineering challenges

Enhanced risk-informed decision-making in system design

Abstract

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.