Publications

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This project focused on research and algorithmic development in optimization under uncertainty (OUU) problems driven by earth penetrator (EP) designs. While taking into account uncertainty, we addressed three challenges in current simulation-based engineering design and analysis processes. The first challenge required leveraging small local samples, already constructed by optimization algorithms, to build effective surrogate models. We used Gaussian Process (GP) models to construct these surrogates. We developed two OUU algorithms using 'local' GPs (OUU-LGP) and one OUU algorithm using 'global' GPs (OUU-GGP) that appear competitive or better than current methods. The second challenge was to develop a methodical design process based on multi-resolution, multi-fidelity models. We developed a Multi-Fidelity Bayesian Auto-regressive process (MF-BAP). The third challenge involved the development of tools that are computational feasible and accessible. We created MATLAB{reg_sign} and initial DAKOTA implementations of our algorithms.

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This report describes key ideas underlying the application of Quantification of Margins and Uncertainties (QMU) to nuclear weapons stockpile lifecycle decisions at Sandia National Laboratories. While QMU is a broad process and methodology for generating critical technical information to be used in stockpile management, this paper emphasizes one component, which is information produced by computational modeling and simulation. In particular, we discuss the key principles of developing QMU information in the form of Best Estimate Plus Uncertainty, the need to separate aleatory and epistemic uncertainty in QMU, and the risk-informed decision making that is best suited for decisive application of QMU. The paper is written at a high level, but provides a systematic bibliography of useful papers for the interested reader to deepen their understanding of these ideas.

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This report presents a initial validation strategy for specific SNL pulsed power program applications of the ALEGRA-HEDP radiation-magnetohydrodynamics computer code. The strategy is written to be (1) broadened and deepened with future evolution of particular specifications given in this version; (2) broadly applicable to computational capabilities other than ALEGRA-HEDP directed at the same pulsed power applications. The content and applicability of the document are highly constrained by the R&D thrust of the SNL pulsed power program. This means that the strategy has significant gaps, indicative of the flexibility required to respond to an ongoing experimental program that is heavily engaged in phenomena discovery.

This report summarizes the results of an effort to establish a framework for assigning and communicating technology readiness levels (TRLs) for the modeling and simulation (ModSim) capabilities at Sandia National Laboratories. This effort was undertaken as a special assignment for the Weapon Simulation and Computing (WSC) program office led by Art Hale, and lasted from January to September 2006. This report summarizes the results, conclusions, and recommendations, and is intended to help guide the program office in their decisions about the future direction of this work. The work was broken out into several distinct phases, starting with establishing the scope and definition of the assignment. These are characterized in a set of key assertions provided in the body of this report. Fundamentally, the assignment involved establishing an intellectual framework for TRL assignments to Sandia's modeling and simulation capabilities, including the development and testing of a process to conduct the assignments. To that end, we proposed a methodology for both assigning and understanding the TRLs, and outlined some of the restrictions that need to be placed on this process and the expected use of the result. One of the first assumptions we overturned was the notion of a ''static'' TRL--rather we concluded that problem context was essential in any TRL assignment, and that leads to dynamic results (i.e., a ModSim tool's readiness level depends on how it is used, and by whom). While we leveraged the classic TRL results from NASA, DoD, and Sandia's NW program, we came up with a substantially revised version of the TRL definitions, maintaining consistency with the classic level definitions and the Predictive Capability Maturity Model (PCMM) approach. In fact, we substantially leveraged the foundation the PCMM team provided, and augmented that as needed. Given the modeling and simulation TRL definitions and our proposed assignment methodology, we conducted four ''field trials'' to examine how this would work in practice. The results varied substantially, but did indicate that establishing the capability dependencies and making the TRL assignments was manageable and not particularly time consuming. The key differences arose in perceptions of how this information might be used, and what value it would have (opinions ranged from negative to positive value). The use cases and field trial results are included in this report. Taken together, the results suggest that we can make reasonably reliable TRL assignments, but that using those without the context of the information that led to those results (i.e., examining the measures suggested by the PCMM table, and extended for ModSim TRL purposes) produces an oversimplified result--that is, you cannot really boil things down to just a scalar value without losing critical information.

This paper provides an overview of several approaches to formulating and solving optimization under uncertainty (OUU) engineering design problems. In addition, the topic of high-performance computing and OUU is addressed, with a discussion of the coarse- and fine-grained parallel computing opportunities in the various OUU problem formulations. The OUU approaches covered here are: sampling-based OUU, surrogate model-based OUU, analytic reliability-based OUU (also known as reliability-based design optimization), polynomial chaos-based OUU, and stochastic perturbation-based OUU.

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This report is a white paper summarizing the literature and different approaches to the problem of calibrating computer model parameters in the face of model uncertainty. Model calibration is often formulated as finding the parameters that minimize the squared difference between the model-computed data (the predicted data) and the actual experimental data. This approach does not allow for explicit treatment of uncertainty or error in the model itself: the model is considered the %22true%22 deterministic representation of reality. While this approach does have utility, it is far from an accurate mathematical treatment of the true model calibration problem in which both the computed data and experimental data have error bars. This year, we examined methods to perform calibration accounting for the error in both the computer model and the data, as well as improving our understanding of its meaning for model predictability. We call this approach Calibration under Uncertainty (CUU). This talk presents our current thinking on CUU. We outline some current approaches in the literature, and discuss the Bayesian approach to CUU in detail.

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The views of state of art in verification and validation (V & V) in computational physics are discussed. These views are described in the framework in which predictive capability relies on V & V, as well as other factors that affect predictive capability. Some of the research topics addressed are development of improved procedures for the use of the phenomena identification and ranking table (PIRT) for prioritizing V & V activities, and the method of manufactured solutions for code verification. It also addressed development and use of hierarchical validation diagrams, and the construction and use of validation metrics incorporating statistical measures.