Abstract
Uncertainty Quantification and Digital Engineering in the Aerospace and Defense Industry
R. Goffee, SmartUQ
Essentially every government and industry entity in the US Aerospace and Defense Industry, as well as numerous global industries, already have some form of ongoing digital engineering activity. The vision for implementation of digital engineering is to connect research, development, production, operations, and sustainment to improve the efficiencies, effectiveness, and affordability of processes over the lifecycle of systems. Basic capabilities required of a model-based digital engineering approach to successfully achieve this vision are:
- An end-to-end system model – ability to transfer knowledge upstream and downstream and from program to program
- Application of reduced order response surfaces and probabilistic analyses to quantify uncertainty and risks in cost and performance at critical decision points
- Single, authoritative digital representation of the system over the life cycle – the authoritative digital surrogate “truth source”
In this presentation, conceptual applications of Uncertainty Quantification (UQ) techniques to perform the probabilistic analyses to define the authoritative digital surrogate “truth source” intrinsic to the concept of digital engineering will be illustrated. UQ of the output from engineering analyses using model-based approaches is essential to providing critical decision quality information at key decision points. Using the Department of Defense lifecycle framework for acquisition, operation, and sustainment of systems, approaches will be presented for the continuing collection and application of UQ knowledge over each stage to reduce uncertainties systematically and provide program decision makers with a probabilistic assessment of performance, risk, and costs essential to critical decisions.
Concepts for using UQ knowledge to forward-plan strategies and approaches to not only quantify but to mitigate uncertainties and risks will be presented. Starting with early requirements definition through design studies, knowledge about sensitivities and uncertainties in system performance can be used to develop more robust Systems Engineering Master Plans (SEMP) and Test & Evaluation Master Plans (TEMP) that stay dynamically connected to program requirements through the digital engineering ecosystem. As an illustration, identifying levels of uncertainties for constructive performance models, ground tests, and eventually flight tests of air vehicles will be addressed to demonstrate the potential of managing an uncertainty budget for each phase of the development process. With an a priori target for uncertainty reduction in each of the modeling, ground testing, and flight testing phases, it will be possible to minimize resources and cycle time to identify and mitigate uncertainties in flight performance, i.e., recognize that an additional computation or additional ground test will not reduce uncertainty any further so it is time to move to the next phase. Having targeted uncertainty budgets will also facilitate optimizing the minimum number of test condition required to achieve the authoritative digital surrogate “truth source.”
