Unpacking model inadequacy: The quantification of silver release from TRISO fuel by considering empirical and mechanistic approaches

Journal Article · Fri Apr 04 00:00:00 EDT 2025 · Journal of Nuclear Materials

DOI:https://doi.org/10.1016/j.jnucmat.2025.155795· OSTI ID:2583246

Dhulipala, Somayajulu L.N. ^[1]; Simon, Pierre-Clément A. ^[2]; Demkowicz, Paul A. ^[2]; Hirschhorn, Jacob A. ^[2]; Novascone, Stephen R. ^[2]

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1. Idaho National Laboratory (INL), Idaho Falls, ID (United States); Idaho State Univ., Pocatello, ID (United States)
2. Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Increasing adoption of the proposed tristructural isotropic (TRISO) particle fuel for both advanced and existing reactors makes it critical to assess and address any uncertainties and inadequacies of TRISO fission product release models. Model inadequacy stems from simplifications made to the computational model when compared to the experiments. The modeling and simulation efforts conducted using the BISON fuel performance code, along with the experimental campaigns carried out under the Advanced Gas Reactor Fuel Development and Qualification Program, afford a unique opportunity to conduct a rigorous modeling inadequacy assessment within the Bayesian uncertainty quantification (UQ) framework. Here, this study compares the standard Bayesian framework against the Kennedy-O'Hagan (KOH) framework, which explicitly represents modeling inadequacy, in regard to UQ for TRISO silver release models. For this purpose, both the traditional Arrhenius equation fitted to experimental data and the more advanced lower-length-scale (LLS)-informed model, which considers microstructure information, are independently considered. Applying the inverse UQ process on the AGR-2 and -3/4 datasets revealed modeling inadequacy to be the most dominant source of uncertainty. Experimental noise uncertainty is also significant; however, model parameter uncertainty can be considered negligible. Interestingly, both the Arrhenius equation and the LLS-informed model demonstrated similar levels of modeling inadequacy. For the forward predictive UQ, the KOH framework improved both the accuracy and quality of quantified uncertainties in comparison to the standard Bayesian framework. This is true for both the Arrhenius equation and the LLS-informed model. In comparing these modeling approaches, both demonstrated similar performance at the engineering scale, while the LLS-informed model expectedly outperformed the Arrhenius equation at the mesoscale. These conclusions highlight the importance of explicitly accounting for modeling inadequacy in the UQ process, and reinforce the need for continuous refinement of physics-based models in order to address the modeling inadequacy.

• Research Organization: Idaho National Laboratory (INL), Idaho Falls, ID (United States)
• Sponsoring Organization: USDOE Office of Nuclear Energy (NE), Nuclear Energy Advanced Modeling and Simulation (NEAMS)
• Grant/Contract Number: AC07-05ID14517
• OSTI ID: 2583246
• Report Number(s): INL/JOU--24-81015-Rev000
• Journal Information: Journal of Nuclear Materials, Journal Name: Journal of Nuclear Materials Journal Issue: 0 Vol. 610; ISSN 0022-3115
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

Advanced reactors
Bayesian analysis
Gaussian process
Model inadequacy
Model structural error
TRISO fuel
Uncertainty quantification