Speaker
Description
Advancing digital engineering for fusion energy requires domain-specific models that can support design, enable predictive simulations, and faithfully replicate experimental results, while also quantifying the associated uncertainties. We demonstrate an uncertainty quantification (UQ) workflow applied to a high-fidelity computational model of a benchmark fusion-relevant neutronics experiment. The workflow integrates multiple sources of input data uncertainties and propagates them through the simulation framework. This approach enables a rigorous assessment of predictive confidence and highlights the key drivers of variability in neutronics responses. The results underline the importance of embedding UQ into digital engineering pipelines, laying the groundwork for future uncertainty-aware digital twins of fusion components and facilities.
| Country or International Organisation | United States of America |
|---|---|
| Affiliation | MIT PSFC |
| Speaker's email address | segantin@mit.edu |
