{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/2037"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/2037","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Artificial Neural Network and Dynamic Probabilistic Risk Assessment for passive safety systems","abstract":"This research emphasizes the application of Artificial Neural Networks (ANNs) and Dynamic Probabilistic Risk Assessment (DPRA) as advanced methodologies for the safety assessment of passive safety systems, particularly in mitigating Loss of Coolant Accidents (LOCAs). While using the BWRX-300 SMR, the study highlights the broader applicability of these techniques to enhance traditional Probabilistic Safety Assessment (PSA). ANNs were employed to predict failure probabilities of critical components, enabling accurate modeling of nonlinear interactions and complex failure scenarios that traditional methods might overlook. DPRA, implemented using the EMRALD tool, provided dynamic, time-dependent analysis of system interactions and accident progression, offering a realistic and detailed evaluation of evolving risk scenarios. By integrating these approaches, the research not only identifies vulnerabilities but also provides actionable insights to improve the effectiveness of passive safety systems. The findings underscore the potential of ANNs and DPRA to revolutionize safety assessments in nuclear energy systems, paving the way for more resilient designs.","abstract_html":"This research emphasizes the application of Artificial Neural Networks (ANNs) and Dynamic Probabilistic Risk Assessment (DPRA) as advanced methodologies for the safety assessment of passive safety systems, particularly in mitigating Loss of Coolant Accidents (LOCAs). While using the BWRX-300 SMR, the study highlights the broader applicability of these techniques to enhance traditional Probabilistic Safety Assessment (PSA). ANNs were employed to predict failure probabilities of critical components, enabling accurate modeling of nonlinear interactions and complex failure scenarios that traditional methods might overlook. DPRA, implemented using the EMRALD tool, provided dynamic, time-dependent analysis of system interactions and accident progression, offering a realistic and detailed evaluation of evolving risk scenarios. By integrating these approaches, the research not only identifies vulnerabilities but also provides actionable insights to improve the effectiveness of passive safety systems. The findings underscore the potential of ANNs and DPRA to revolutionize safety assessments in nuclear energy systems, paving the way for more resilient designs.","abstract_has_math":false,"creators":["Basak, Saikat"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Nuclear Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Lu, Lixuan"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12-01","date_published":"2024-12-01","updated_at":"2026-07-24T05:35:28Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/2037","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Lu, Lixuan"]},{"key":"dc:creator","label":"Author","values":["Basak, Saikat"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-19T21:07:40Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-12-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Nuclear Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/2037"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This research emphasizes the application of Artificial Neural Networks (ANNs) and Dynamic Probabilistic Risk Assessment (DPRA) as advanced methodologies for the safety assessment of passive safety systems, particularly in mitigating Loss of Coolant Accidents (LOCAs). While using the BWRX-300 SMR, the study highlights the broader applicability of these techniques to enhance traditional Probabilistic Safety Assessment (PSA). ANNs were employed to predict failure probabilities of critical components, enabling accurate modeling of nonlinear interactions and complex failure scenarios that traditional methods might overlook. DPRA, implemented using the EMRALD tool, provided dynamic, time-dependent analysis of system interactions and accident progression, offering a realistic and detailed evaluation of evolving risk scenarios. By integrating these approaches, the research not only identifies vulnerabilities but also provides actionable insights to improve the effectiveness of passive safety systems. The findings underscore the potential of ANNs and DPRA to revolutionize safety assessments in nuclear energy systems, paving the way for more resilient designs."]},{"key":"dc:title","label":"Title","values":["Artificial Neural Network and Dynamic Probabilistic Risk Assessment for passive safety systems"]}]}],"canonical_facts":{"dc:contributor.advisor":["Lu, Lixuan"],"dc:creator":["Basak, Saikat"],"dc:date.accessioned":["2026-01-19T21:07:40Z"],"dc:date.issued":["2024-12-01"],"dc:description.abstract":["This research emphasizes the application of Artificial Neural Networks (ANNs) and Dynamic Probabilistic Risk Assessment (DPRA) as advanced methodologies for the safety assessment of passive safety systems, particularly in mitigating Loss of Coolant Accidents (LOCAs). While using the BWRX-300 SMR, the study highlights the broader applicability of these techniques to enhance traditional Probabilistic Safety Assessment (PSA). ANNs were employed to predict failure probabilities of critical components, enabling accurate modeling of nonlinear interactions and complex failure scenarios that traditional methods might overlook. DPRA, implemented using the EMRALD tool, provided dynamic, time-dependent analysis of system interactions and accident progression, offering a realistic and detailed evaluation of evolving risk scenarios. By integrating these approaches, the research not only identifies vulnerabilities but also provides actionable insights to improve the effectiveness of passive safety systems. The findings underscore the potential of ANNs and DPRA to revolutionize safety assessments in nuclear energy systems, paving the way for more resilient designs."],"dc:identifier.uri":["https://hdl.handle.net/10155/2037"],"dc:language.iso":["en"],"dc:title":["Artificial Neural Network and Dynamic Probabilistic Risk Assessment for passive safety systems"],"dc:type":["Thesis"],"thesis:degree_discipline":["Nuclear Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:28Z"}