Network Uncertainty Quantification for Analysis of MultiComponent Systems

Abstract

In order to impact physical mechanical system design decisions and realize the full promise of highfidelity computational tools, simulation results must be integrated at the earliest stages of the design process. This is particularly challenging when dealing with uncertainty and optimizing for systemlevel performance metrics, as fullsystem models (often notoriously expensive and timeconsuming to develop) are generally required to propagate uncertainties to systemlevel quantities of interest. Methods for propagating parameter and boundary condition uncertainty in networks of interconnected components hold promise for enabling design under uncertainty in realworld applications. These methods avoid the need for time consuming mesh generation of fullsystem geometries when changes are made to components or subassemblies. Additionally, they explicitly tie fullsystem model predictions to component/subassembly validation data which is valuable for qualification. These methods work by leveraging the fact that many engineered systems are inherently modular, being comprised of a hierarchy of components and subassemblies that are individually modified or replaced to define new system designs. By doing so, these methods enable rapid model development and the incorporation of uncertainty quantification earlier in the design process. The resulting formulation of the uncertainty propagation problem is iterative. We express the system model as a network of interconnected component models, which exchange solution information at component boundaries. We present a pair of approaches for propagating uncertainty in this type of decomposed system and provide implementations in the form of an opensource software library. We demonstrate these tools on a variety of applications and demonstrate the impact of problemspecific details on the performance and accuracy of the resulting UQ analysis. This work represents the most comprehensive investigation of these network uncertainty propagation methods to date.