AI-based leakage prediction with uncertainty quantification and explainable AI for nuclear system

Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Nuclear, Plasma, Radiolgc Engr

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Abusultan, Ahmed

Contributors dc:contributor

• Alam, Syed Bahauddin

• ANN
• Artificial Neural Network
• BWR
• Boiling Water Reactor
• CV
• Cross Validation
• FCNN Fully Connected Neural Network
• GPWR Generic Pressurized Water Reactor
• LIME Local Interpretable Model
• Agnostic Explanations
• LOCA
• Loss of Coolant Accident
• MAE Mean Absolute Error
• MSE
• Mean Squared Error
• NPP
• Nuclear Power Plant
• NRC
• Nuclear Regulatory Commission
• PRA
• Probabilistic Risk Assessment
• PWR
• Pressurized Water Reactor
• RELAP5
• Reactor Excursion and Leak Analysis Program
• RMSE
• Root Mean Squared Error
• RCS
• Reactor Coolant System
• SBLOCA
• Small Break Loss of Coolant Accident
• TPE
• Tree Structured Parzen Estimator
• UQ
• Uncertainty Quantification
• XAI
• Explainable Artificial Intelligence

Statement dc:rights

• © 2025 by Ahmed Abusultan. All rights reserved.

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129786

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/129786