Quantification of margins and uncertainties
QMU focuses on the identification, characterization, and analysis of performance thresholds and their associated margins for engineering systems that are evaluated under conditions of uncertainty, particularly when portions of those results are generated using computational modeling and simulation.
[3] The methodology has since been applied in other applications where safety or mission critical decisions for complex projects must be made using results based on modeling and simulation.
Examples outside of the nuclear weapons field include applications at NASA for interplanetary spacecraft and rover development,[4] missile six-degree-of-freedom (6DOF) simulation results,[5] and characterization of material properties in terminal ballistic encounters.
The margin represents the targeted range the system is being designed to operate in to safely avoid the upper and lower performance bounds.
These M/U values can serve as quantified inputs that can help authorities make risk-informed decisions regarding how to interpret and act upon results based on simulations.
The simulation in the QMU process produces output results for the key performance thresholds of interest, known as the Best Estimate Plus Uncertainty (BE+U).
If the uncertainty limit for U includes a finite upper bound due to the particular distribution of that variable, a lower M/U ratio may be acceptable.
The underlying objective of QMU is to present information to decision-makers that fully characterizes the results in light of the uncertainty as understood by the model developers.
Subject matter expert (SME) judgment and other external factors such as stakeholder opinions and regulatory issues must also be considered by the decision-making authority before a final outcome is decided.
QMU provides a formal method for describing the required fidelity relative to the design threshold margins for key performance variables.
Analysis of the various M/U ratios for the key performance variables can help identify model components that are in need of fidelity upgrades to order to increase simulation effectiveness.
However, for safety-critical systems where experimental test data is lacking, simulation results provide a critical input to the decision-making process.
The use of quantified results for key simulation parameters can lead decision makers to believe all possible risks have been fully accounted for, which is particularly challenging for complex systems.
