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.

翻译：复杂工程系统需要集成子系统仿真与指标计算，以驱动设计决策。本文提出了一种为系统级不确定性缓解目的而设计计算或物理实验的方法论。该方法遵循预先确定的问题本体论，其中物理、功能和建模架构均已确定。通过利用系统级工具进行敏感性分析技术，可以识别关键的认识论不确定性。随后，引入一个框架来设计具体的计算和物理实验，以生成关于参数的新知识并缓解不确定性。该方法通过一个早期设计阶段混合翼身（BWB）飞机概念的案例研究进行演示，展示了如何通过计算机实验或指导物理实验，利用气动结构分析来缓解系统级不确定性。所提出的方法具有足够的通用性，能够应对各种设计挑战中的不确定性管理，突显了实现更具风险感知的设计流程的潜力。