Doctoral Dissertations
Date of Award
8-2025
Degree Type
Dissertation
Degree Name
Doctor of Philosophy
Major
Nuclear Engineering
Major Professor
Ivan G. Maldonado
Committee Members
Ivan Maldonado, Steven E. Skutnik, Vladimir Sobes, Gordon M. Petersen, Robert A. Joseph
Abstract
The management and handling of spent nuclear fuel (SNF) from pebble bed reactors (PBRs) requires reliable estimates of bulk fuel characteristics long after discharge. Historical efforts have been focused on light water reactor (LWR) fuels and have not been updated to reflect the distinctive features of PBR fuel, particularly the identification of the key parameters that would need to be tracked and recorded to achieve a reasonable prediction. This work addresses the question: “What reactor parameters and fuel conditions must be tracked to reasonably predict SNF characteristics from pebble bed reactors?”
The central hypothesis is that average discharge burnup and cooling time are sufficient to accurately estimate SNF characteristics relevant to radiological dose rate, criticality safety, and decay heat, provided that the effects of other parameters are incorporated through bounding calculations. To evaluate this claim, an extensive set of simulations was performed using SCALE 6.3.2, including TRITON, ORIGEN, KENO-VI, MAVRIC, TSUNAMI, and OBIWAN modules. Impactful parameters were identified, best modeling practices identified and utilized and impacts inherent to reactor’s operation were demonstrated. Lastly, a broad parameter space was explored—enrichment, specific power, fuel temperature, burnup, and cooling time—and automated via Python workflows on high-performance computing (HPC) resources using SLURM, an open-source workload manager and job scheduler for Linux clusters.
Key outputs such as criticality, decay heat, and radiological dose rates were analyzed for the spent fuel across the parameter space. A cluster-based statistical method was applied to group data and generate ±2σ uncertainty envelopes. Results demonstrate that once the impact of non-tracked parameters is captured as bounding variability, SNF behavior correlates strongly with only burnup and cooling time. These findings suggest that future SNF reporting and management strategies for PBRs can be streamlined, reducing data collection burdens while preserving safety margins. The results support regulatory, design, and operational planning efforts for entities responsible for handling advanced reactor spent fuel.
Recommended Citation
Wing, Jonathan Franklin, "PROPOSED METHODOLOGIES FOR PEBBLE BED REACTOR SPENT FUEL CHARACTERIZATION. " PhD diss., University of Tennessee, 2025.
https://trace.tennessee.edu/utk_graddiss/12669
Comments
The committee is currently reviewing for approval on final changes and may request some slight revisions.