[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119765-en":3,"doc-seo-119765-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119765,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Predictions and Uncertainty Estimates of Reactor Pressure Vessel Steel Embrittlement Using Machine Learning","Extending the safe operation of active nuclear reactors depends on predicting how reactor pressure vessel (RPV) steels embrittle under neutron irradiation. This work combines state-of-the-art machine-learning ensembles of neural networks with large-scale data collection and integration to build an improved embrittlement model for RPV steel. The model delivers higher accuracy at high fluence, broader composition coverage, uncertainty quantification, and online accessibility. Results enable efficient exploration of composition, flux, and fluence effects and outperform the ASTM E900-15 benchmark across assessed metrics, demonstrating strong capability for demanding materials-property prediction.","Predictions and Uncertainty Estimates of Reactor Pressure Vessel Steel Embrittlement Using Machine Learning  \nAuthors: Ryan Jacobs 1, Takuya Yamamoto2, G. Robert Odette,2 Dane Morgan1  \n1 Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, WI, USA  \n2 Mechanical Engineering Department, University of California Santa Barbara, Santa Barbara, CA, USA.  \nKeywords: reactor pressure vessel, embrittlement, transition temperature shift, machine learning, neural network  \nAbstract: An essential aspect of extending safe operation of the world’s active nuclear reactors is understanding and predicting the embrittlement that occurs in the steels that make up the Reactor pressure vessel (RPV) . In this work we integrate state of the art machine learning methods using ensembles of neural networks with unprecedented data collection and integration to develop a new model for RPV steel embrittlement. The new model has multiple improvements over previous machine learning and hand-tuned efforts, including greater accuracy (e.g., at high-fluence relevant for extending the life of present reactors), wider domain of applicability (e.g., including a wide-range of compositions), uncertainty quantification, and online accessibility for easy use by the community. These improvements provide a model with significant new capabilities, including the ability to easily and accurately explore compositions, flux, and fluence effects on RPV steel embrittlement for the first time. Furthermore, our detailed comparisons show our approach improves on the leading American Society for Testing and Materials (ASTM) E900-15 standard model for RPV embrittlement on every metric we assessed, demonstrating the efficacy of machine learning approaches for this type of highly demanding materials property prediction.  \n1. Introduction:  \nNuclear power is a key component of global clean energy production, producing roughly 20% of the power in the US and 25% of the power in the EU as of 2021. For perspective, as of 2019 this represents approximately half of the low-carbon electricity generation in the EU. 1,2 Given the urgent need to shift from fossil fuels to green energy sources to mitigate the numerous negative effects of global climate change,3 the role of nuclear energy as a source of clean  \nelectricity will be even more important in the future. In the near term, nuclear energy’s contribution will depend on life extension of the existing fleet of reactors.  \nA key component of light water nuclear reactors (LWRs) is the massive heavy section reactor pressure vessel (RPV), which houses the nuclear fuel core and pressurized reactor coolant. The RPV is fabricated from low-alloy steels. Since RPVs are individually custom-built, they contain a wide range of Cu, Ni, Mn, P, Si, C and other dissolved solutes. During reactor operation, the neutron flux leaking from the reactor core impinges the walls of the RPV, resulting in embrittlement of the steel, manifested as an upward shift in the ductile to brittle transition temperature (TTS) . Embrittlement is mainly the result of radiation-enhanced formation of Curich and Mn-Ni-Si-rich precipitates (CRPs and MNPs) .4–6 The precipitates impede dislocation glide, resulting in irradiation hardening manifested as an increase in the yield stress (∆􀟪y), which in turn results in an increase of the TTS. Replacement of the RPV is not feasible. Thus, it is essential to understand, and accurately predict, how the RPV steels embrittle as a function of the neutron fluence (with typical units of neutrons n/cm2), which is the corresponding flux (n/cm2-s) integrated over time, up to 80 years, or more, of extended reactor life. Note, a detailed review of embrittlement mechanisms and associated predictive TTS models can be found in the review from Odette et al.,6 hence, there is no attempt to cover the many details therein in this machine learning focused paper.  \nThere are two main sources of embrittlement data. The f","cbCaicXCJ7nn7Hqp","https://ap.wps.com/l/cbCaicXCJ7nn7Hqp","pdf",3493908,1,57,"English","en",105,"# Introduction\n## Reactor pressure vessel embrittlement in light water reactors\n## Data sources for embrittlement (surveillance and test reactors)\n## Existing predictive TTS models (ASTM E900, EONY, OWAY)","[{\"question\":\"Why is predicting reactor pressure vessel steel embrittlement important?\",\"answer\":\"Because RPV replacement is not feasible, accurate prediction of how steels embrittle with neutron fluence is essential for life extension and safe long-term operation.\"},{\"question\":\"What causes embrittlement in RPV steels according to the document?\",\"answer\":\"Neutron irradiation enhances the formation of Cu-rich and Mn-Ni-Si-rich precipitates, which impede dislocation glide, leading to irradiation hardening and an increase in the ductile-to-brittle transition temperature (TTS).\"},{\"question\":\"How does the proposed machine-learning model improve over previous approaches?\",\"answer\":\"It uses ensemble neural networks with unprecedented data integration to improve accuracy at high fluence, extend applicability across compositions, quantify uncertainty, and provide online accessibility, outperforming the ASTM E900-15 model on every evaluated metric.\"}]","Predictions and Uncertainty Estimates of Reactor Pressure Vessel Steel Embrittlement Using Machine Learning | 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is predicting reactor pressure vessel steel embrittlement important?","Question",{"text":76,"@type":77},"Because RPV replacement is not feasible, accurate prediction of how steels embrittle with neutron fluence is essential for life extension and safe long-term operation.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What causes embrittlement in RPV steels according to the document?",{"text":81,"@type":77},"Neutron irradiation enhances the formation of Cu-rich and Mn-Ni-Si-rich precipitates, which impede dislocation glide, leading to irradiation hardening and an increase in the ductile-to-brittle transition temperature (TTS).",{"name":83,"@type":74,"acceptedAnswer":84},"How does the proposed machine-learning model improve over previous approaches?",{"text":85,"@type":77},"It uses ensemble neural networks with unprecedented data integration to improve accuracy at high fluence, extend applicability across compositions, quantify uncertainty, and provide online accessibility, 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