{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129786"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129786","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"AI-based leakage prediction with uncertainty quantification and explainable AI for nuclear system","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_has_math":false,"creators":["Abusultan, Ahmed"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Nuclear, Plasma, Radiolgc Engr","degree_department":null,"school":null,"contributors":["Alam, Syed Bahauddin"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-09","date_published":"2025-05-09","updated_at":"2026-07-22T22:25:05Z","subjects":["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"],"languages":["en","eng"],"rights":["© 2025 by Ahmed Abusultan. All rights reserved."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129786","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Alam, Syed Bahauddin"]},{"key":"dc:creator","label":"Author","values":["Abusultan, Ahmed"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-05-09","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Nuclear, Plasma, Radiolgc Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["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"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["© 2025 by Ahmed Abusultan. All rights reserved."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129786"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Ahmed Abusultan, accepted the attached license on 2025-05-07 at 14:00.","The student, Ahmed Abusultan, submitted this Thesis for approval on 2025-05-07 at 14:14.","This Thesis was approved for publication on 2025-05-09 at 10:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22254 on 2025-10-19 at 19:55:50","The integrity of the Reactor Coolant System (RCS) is central to nuclear power plant (NPP) safety, particularly under Small Break Loss-of-Coolant Accident (SBLOCA) conditions, where undetected minor leakage can rapidly escalate into severe system failures. Traditional detection techniques—based on deterministic solvers, threshold-based alarms, or first-principles physics—are inadequate for modern reactor diagnostics: they fail to model complex nonlinear interdependencies, cannot generalize beyond predefined fault patterns, and lack the flexibility to produce real-time, uncertainty-aware predictions under variable operating conditions. These limitations are further exacerbated in data-sparse nuclear operating environments where real-time observability is limited, and failure precursors are often masked. This thesis introduces a machine learning-based framework for interpretable and uncertainty-aware leakage prediction in pressurized water reactors (PWRs) to overcome these challenges. Synthetic datasets encompassing 15 SBLOCA scenarios were generated using the Generic Pressurized Water Reactor (GPWR) simulator coupled with the RELAP5 thermohydraulic code. The predictive framework employs three complementary learning models: Fully Connected Neural Networks (FCNNs), Random Forest Regressors (RFR), and Gradient Boosting Regressors (GBR). These were selected for their proven performance in regression tasks and their balanced trade-offs across accuracy, interpretability, data efficiency, and computational tractability—a critical consideration in nuclear safety applications. While more complex deep architectures (e.g., LSTMs, GNNs, transformers) exist in the literature, they often require large training sets, suffer from black-box opacity, and pose significant validation challenges in regulatory environments. In contrast, the chosen models allow rigorous benchmarking and clearer model validation with domain experts. Performance across hot leg, cold leg, and surge line scenarios shows that FCNNs excel at capturing complex nonlinearities, while RFR and GBR offer robustness and greater interpretability. A Bayesian-inspired input perturbation method is employed to quantify prediction confidence, generating uncertainty bounds critical for operator trust and safety margins. LIME-based explainability further reveals key contributing features—such as noble gas count, pressurizer level, and containment humidity—whose influence varies with component and fault severity. This work presents a unified AI pipeline for nuclear leakage prediction and demonstrates that carefully selected machine learning models can enhance early fault detection, reduce false alarms, and support data-driven maintenance in safety-critical nuclear environments."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["AI-based leakage prediction with uncertainty quantification and explainable AI for nuclear system"]}]}],"canonical_facts":{"dc:contributor":["Alam, Syed Bahauddin"],"dc:creator":["Abusultan, Ahmed"],"dc:date":["2025-05-09","2025-05"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Ahmed Abusultan, accepted the attached license on 2025-05-07 at 14:00.","The student, Ahmed Abusultan, submitted this Thesis for approval on 2025-05-07 at 14:14.","This Thesis was approved for publication on 2025-05-09 at 10:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22254 on 2025-10-19 at 19:55:50","The integrity of the Reactor Coolant System (RCS) is central to nuclear power plant (NPP) safety, particularly under Small Break Loss-of-Coolant Accident (SBLOCA) conditions, where undetected minor leakage can rapidly escalate into severe system failures. Traditional detection techniques—based on deterministic solvers, threshold-based alarms, or first-principles physics—are inadequate for modern reactor diagnostics: they fail to model complex nonlinear interdependencies, cannot generalize beyond predefined fault patterns, and lack the flexibility to produce real-time, uncertainty-aware predictions under variable operating conditions. These limitations are further exacerbated in data-sparse nuclear operating environments where real-time observability is limited, and failure precursors are often masked. This thesis introduces a machine learning-based framework for interpretable and uncertainty-aware leakage prediction in pressurized water reactors (PWRs) to overcome these challenges. Synthetic datasets encompassing 15 SBLOCA scenarios were generated using the Generic Pressurized Water Reactor (GPWR) simulator coupled with the RELAP5 thermohydraulic code. The predictive framework employs three complementary learning models: Fully Connected Neural Networks (FCNNs), Random Forest Regressors (RFR), and Gradient Boosting Regressors (GBR). These were selected for their proven performance in regression tasks and their balanced trade-offs across accuracy, interpretability, data efficiency, and computational tractability—a critical consideration in nuclear safety applications. While more complex deep architectures (e.g., LSTMs, GNNs, transformers) exist in the literature, they often require large training sets, suffer from black-box opacity, and pose significant validation challenges in regulatory environments. In contrast, the chosen models allow rigorous benchmarking and clearer model validation with domain experts. Performance across hot leg, cold leg, and surge line scenarios shows that FCNNs excel at capturing complex nonlinearities, while RFR and GBR offer robustness and greater interpretability. A Bayesian-inspired input perturbation method is employed to quantify prediction confidence, generating uncertainty bounds critical for operator trust and safety margins. LIME-based explainability further reveals key contributing features—such as noble gas count, pressurizer level, and containment humidity—whose influence varies with component and fault severity. This work presents a unified AI pipeline for nuclear leakage prediction and demonstrates that carefully selected machine learning models can enhance early fault detection, reduce false alarms, and support data-driven maintenance in safety-critical nuclear environments."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129786"],"dc:language":["en","eng"],"dc:rights":["© 2025 by Ahmed Abusultan. All rights reserved."],"dc:subject":["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"],"dc:title":["AI-based leakage prediction with uncertainty quantification and explainable AI for nuclear system"],"dc:type":["text"],"thesis:degree_discipline":["Nuclear, Plasma, Radiolgc Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}