Uncertainty Quantification and Decision Making in Hierarchical Development of Computational Models

Abstract

As engineering systems grow in size and complexity, it is becoming increasingly difficult to assess their performance through full scale testing. Modeling and simulation fill the gap left by the lack of full scale testing for an actual use environment. Modeling and simulation-based assessment also requires the quantification of uncertainty in the predicted response of the system model, in order to establish the confidence in representing the actual system behavior. Sources of uncertainty arise from (1) the stochastic nature of components, (2) their coupling with each other, (3) from data, (4) model assumptions and (5) model approximations. Computational models for large systems are built in a hierarchical way from component, subsystem to system level. Individual component data is more readily available then full system data. This research proposes a framework that allows quantification of uncertainty in a hierarchical system model prediction and uses the available data at multiple levels. Sources of both aleatoric and epistemic uncertainty are included in such quantification. Techniques to quantify margins of performance and uncertainties in order to estimate the confidence in the system model prediction are investigated. Finally, the results of the uncertainty analysis are used to develop a decision making methodology that allocates resources for further data collection and model improvement activities.