Center for Computing Research
Automated Ensemble Analysis in Support of Nuclear Deterrence Programs
Managing the modeling-simulation-analysis workflows that provide the basis for Sandia’s Nuclear Deterrence programs is a requirement for assuring verifiable, documented, and reproducible results. The Sandia Analysis Workbench (SAW) has now been extended to provide workflow management through the final tasks of ensemble analysis using Sandia’s Slycat framework. This new capability enhances multi-platform modeling-simulation workflows through the Next Generation Workflow (NGW) system. With the goal of providing end-to-end workflow capability to the nuclear deterrence programs, the new technology integrates Slycat and SAW. It fills the workflow gap between computational simulation results and post-processing tasks of ensemble analysis and the characterization of uncertainty quantification (UQ). This work compliments simulation data management while providing encapsulated, targeted sub-workflows for ensemble analysis, verification and validation, and UQ. The integration of Slycat management into SAW affords a common point of control and configuration. This connects analysis with modeling and simulation, and provides a documented provenance of that analysis. The heart of the work is a set of innovative NGW components that harvest ensemble features, quantities of interest (QoIs), simulation responses, and in situ generated images, videos, and surface meshes. These components are triggered on-demand by the workflow engine when the prerequisite data and conditions are satisfied. Executing from an HPC platform, the components apply those artifacts to generate parameterized, user-ready analyses on the Slycat server. These components can eliminate the need for analyst intervention to hand-process artifacts or QoIs. The technology automates data flow and evidence production needed for decision-support in quantification of margins and uncertainty. Finally, these components deliver an automated and repeatable shortcut to Slycat’s meta-analysis crucial for optimizing ensembles, evaluating parameter studies, and understanding sensitivity analysis.
Contact: Hunt, Warren L.