Archive of earlier Uncertainty Quantification/Verification & Validation Seminar Series—Seminars 1–6

Stronglinks, barriers and weaklinks are safety crticial components and elements that provide assured safety. Stronglinks and barriers protect the system from low energy insults by use of their strength. However, at some environmental energy level, these components will fail, and weaklinks, which control the process of system failure, are used to ensure safety when stronglink performance is no longer assured. Unfortunately, there are no recognized mechanical weaklinks in any of our stockpile systems, even through there are a number of credible mechanical insults that could cause a loss of assured safety. In this presentation we will discuss a system level analysis capability to determine the statistical design constrains of a mechanical weaklink. We begin this presentation by formulating a theory of safety for mechanical environments based on sets of environments whose insults are protected against by a set of safety critical components. The goal of this work is to build a safety cover where the union of all of these sets is the set of all credible environments. Membership to sets is determined through the use of statistical analysis. Weaklink design criteria will be determined by covering the set of environments not covered by stronglinks. Safety margins will be given. An example using W76-1 dynamics will be presented.

Uncertainty is often (perhaps always) a fundamental aspect of the human world view rather than of the world itself. As such, the meaning of uncertainty is inseparable from the problem at hand. In particular, the views on uncertainty differ between the modeling and simulation community and the metrology community. The metrology perspective is formalized in the ISO “Guide to the Expression of Uncertainty in Measurement”. While this document certainly has its limitations, the method it describes works very well in practice when applied to many measurement uncertainty problems. I would like to discuss measurement, uncertainty, the ISO “Guide”, and beyond.

A validation metric is a quantitative measure of agreement between physical reality, as measured by a collection of experiments, and computational predictions. A set of validation metrics based on statistical confidence intervals, and applicable to validation of deterministic models, is described in Oberkampf and Barone (JCP, 217:5-36, 2006, also available as SAND2005-4302). Recently, the VALMET Matlab code was developed to calculate this set of metrics for one-dimensional data sets. This talk will describe VALMET, its features, how its output relates to the described metrics, and how it can be used for practical validation studies. A demonstration of the code on several sample problems will be performed.

We consider a general measure of validation assessment that can be used to characterize the disagreement between the quantitative predictions from a model and relevant empirical data when predictions and data may contain both aleatory and epistemic uncertainty. This validation assessment metric can characterize the mismatch between predictions expressed as probability distributions and any number of observations and it has a variety of properties useful in engineering. This paper extends the metric for use in pooling observations from multiple system response quantities expressed in different units and dimensions and in accounting for observations that are outside of the range considered “possible” by the model. It explores the application of the metric when the predicted quantity is a scalar (real) value. In such cases, the prediction and observation may still have the forms of probability distributions which represent measurement uncertainty rather than intrinsic variability of the quantity. The metric is also generalized to the case when predictions or data contain epistemic uncertainty that cannot be well characterized with any single probability distribution. We suggest that when the uncertainties of the prediction and the observations overlap, the validation metric between them can be small or even zero, but this does not mean that a model’s predictive capability will necessarily be high. A model’s predictive capability is a function of the acknowledged imprecision of its predictions. This imprecision should be appropriately inflated when the model’s performance is found to be poor when assessed by the validation metric. Thus, although acknowledging epistemic uncertainty in either predictions or observations tends to lower the apparent discrepancy between theory and data and thus result in a smaller value of a validation assessment metric, it will propagate through the extrapolation of the model and express itself as lower precision in the model’s predictive capability.