Test Resource Allocation for Uncertainty Quantification of Multi-level and Coupled Systems

This paper develops analytical methods for test resource allocation that will aid in reducing the uncertainty in the system model prediction for multi-level, and coupled systems. The various component, subsystem, and system-level models, the corresponding parameters, and the model errors are connected efficiently using a Bayes network. This provides a unified framework for uncertainty analysis where test data can be integrated along with computational models and simulations. The Bayes network is then used in an inverse problem where the model parameters of multiple subsystems are calibrated simultaneously. This leads to a decrease in the variance of the model parameters, and hence, in the overall system performance prediction. An optimization-based procedure is used for resource allocation and those tests that can effectively reduce the uncertainty in the system model prediction are identified. The proposed methods are illustrated using two numerical examples: a multi-level structural dynamics problem and a multi-disciplinary thermally induced vibration problem.