RT Journal Article ID 584fb92852e9e075 A1 Urbina, Angel A1 Mahadevan, Sankaran A1 Paez, Thomas L. T1 A BAYES NETWORK APPROACH TO UNCERTAINTY QUANTIFICATION IN HIERARCHICALLY DEVELOPED COMPUTATIONAL MODELS JF International Journal for Uncertainty Quantification JO IJUQ YR 2012 FD 2012-03-07 VO 2 IS 2 SP 173 OP 193 K1 uncertainty quantification K1 Markov chain Monte Carlo K1 Bayesian inference K1 hierarchical model development K1 structural dynamics AB Performance assessment of complex systems is ideally accomplished through system-level testing, but because they are expensive, such tests are seldom performed. On the other hand, for economic reasons, data from tests on individual components that are parts of complex systems are more readily available. The lack of system-level data leads to a need to build computational models of systems and use them for performance prediction in lieu of experiments. Because their complexity, models are sometimes built in a hierarchical manner, starting with simple components, progressing to collections of components, and finally, to the full system. Quantification of uncertainty in the predicted response of a system model is required in order to establish confidence in the representation of actual system behavior. This paper proposes a framework for the complex, but very practical problem of quantification of uncertainty in system-level model predictions. It is based on Bayes networks and uses the available data at multiple levels of complexity (i.e., components, subsystem, etc.). Because epistemic sources of uncertainty were shown to be secondary, in this application, aleatoric only uncertainty is included in the present uncertainty quantification. An example showing application of the techniques to uncertainty quantification of measures of response of a real, complex aerospace system is included. PB Begell House LK https://www.dl.begellhouse.com/journals/52034eb04b657aea,5c8b0c4a1cb02748,584fb92852e9e075.html