Nuclear Engineering and Design

Nonparametric Stochastic Subspaces via the Bootstrap Method for Characterizing Model Error

Reliable forward uncertainty quantification in engineering requires methods that account for aleatory and epistemic uncertainties. In many applications, epistemic effects arising from uncertain parameters and model form dominate prediction error and strongly influence engineering decisions. Because distinguishing and...

Calibration Assessment for Nested Uncertainty Sets

We present a Bayesian framework for assessing the calibration of nested uncertainty sets from simulation studies. Calibration is typically evaluated by repeatedly simulating data from a known model and comparing empirical coverage probabilities with their nominal values, an approach that often requires hundreds or thou...

Consistent quantification of energy uncertainties in experimental time-of-flight nuclear data across resolved, unresolved, and fast regimes

A major task for nuclear data evaluations is the curation of measurement data to be used in the inference of nuclear data quantities. The quality of the resulting measurement data uncertainty depends on the rigor of the evaluator reviewing the data set and the uncertainty information provided by the experimenter. Nucle...

Multi-source data integration for soil parameter uncertainty quantification

Calibration of soil parameters is essential for reliable geotechnical analysis, yet remains challenging due to limited field measurements, model discrepancy, correlated outputs and the need to incorporate multiple-source data types. This paper proposes a sequential Bayesian framework that enables staged data assimilati...

On Methods of Uncertainty Quantification in Computational Modeling and Simulation Harmonizing

Comparative assessment of ISO/IEC Guide 98-3 and Monte Carlo methods for temperature sensor calibration: A simulation-based study

The evaluation of measurement uncertainty is a task in metrology, and remains challenging when measurement models show nonlinearity, or when input quantities follow non-Gaussian probability distributions. Although the ISO/IEC Guide 98-3 (GUM) is widely applied in calibration practice, studies have highlighted situation...