The Source Physics Experiments (SPE) were designed to improve our physics-based understanding of explosion sources for the purposes of nuclear test monitoring. Phase I consisted of 6 chemical explosions in the Climax Stock Granite of the Nevada National Security Site (NNSS), while Phase II consisted of 4 explosions in a contrasting dry alluvium geology (DAG) in Yucca Flat, providing essential data in various media and emplacement conditions to further modeling efforts. For Phase III, the Rock Valley Direct Comparison (RVDC) seeks to directly compare earthquake and explosion source types. An unusually shallow series of events in 1993 along the Rock Valley Fault Zone in the southeastern portion of the NNSS has been targeted for this direct comparison. Depth ranges for the events, previously estimated to be less than 3 km, is achievable by modern drilling techniques and accessibility to the epicentral locations would require minimal improvements to the infrastructure. The events providing this unique opportunity for direct comparison are the focus of this report.
In order to impact physical mechanical system design decisions and realize the full promise of high-fidelity 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 system-level performance metrics, as full-system models (often notoriously expensive and time-consuming to develop) are generally required to propagate uncertainties to system-level quantities of interest. Methods for propagating parameter and boundary condition uncertainty in networks of interconnected components hold promise for enabling design under uncertainty in real-world applications. These methods avoid the need for time consuming mesh generation of full-system geometries when changes are made to components or subassemblies. Additionally, they explicitly tie full-system 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 open-source software library. We demonstrate these tools on a variety of applications and demonstrate the impact of problem-specific 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.