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Conference Spotlight
2026 Nuclear Energy Conference & Expo (NECX)
August 24–27, 2026
Dallas, TX|Hilton Anatole
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Center for Used Fuel Research: Building confidence in storage and transport
Used nuclear fuel storage and transportation have reached a critical juncture.
Dozens of utilities need reliable data on how used nuclear fuel performs in dry storage casks and canisters to extend regulatory licenses at sites across the United States. Likewise, the Department of Energy expects to take ownership of the used nuclear fuel—termed “spent nuclear fuel” in the laws and regulations governing its stewardship—and transfer it to one or more federal staging facilities for management and disposition.
Meanwhile, dozens of reactor companies are testing prototypes of advanced reactors and advanced reactor fuels. Eventually, regulators and industry must also verify the safety and security of storage methods for these advanced fuel types.
To help address these challenges, the DOE established the Center for Used Fuel Research (CUFR) in January 2026 for work related to the long-term storage and transport of used nuclear fuel.
Technical Session|Mathematics and Computation (MCD)
Monday, June 1, 2026|3:15–5:00PM MDT|Director's Row E
Session Chair:
Kendra Long
Alternate Chair:
Majdi I. Radaideh (Univ. Michigan, Ann Arbor)
To access paper attachments, you must be logged in and registered for the meeting.
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Machine Learning Weather Models for Nuclear Fallout Dispersion Prediction
3:15–3:35PM MDT
Jorden Gershenson
Paper
A Domain Decomposition Approach for 1-D Transport with Anisotropy using Physics-Informed Neural Networks
3:35–3:55PM MDT
Ravi S. Shastri (Univ. Michigan, Ann Arbor), Abraham Skoczylas, Patrick Myers (Univ. Michigan), Majdi I. Radaideh (Univ. Michigan, Ann Arbor), Brian C. Kiedrowski (Univ. Michigan)
Control Rod Worth Calibration using Gaussian Process Regression for the TRIGA Mk-II Reactor
3:55–4:15PM MDT
Jeongwon Seo (Univ. Texas, Austin), Sam Queralt (Univ. Texas, Austin), William S. Charlton (Univ. Texas, Austin), Kevin T. Clarno (Univ. Texas, Austin)
Machine Learning Applications to Predict Transmission Spectra of Molten Salts
4:15–4:35PM MDT
Aarin N. Henning (Georgia Tech), Mathew W. Swinney (ORNL)
