Rate Theory Model of Irradiation-Induced Solute Clustering in b.c.c. Fe-Based Alloys
Solute nanoclusters are critical to the structural and mechanical integrity of numerous alloys based on the b.c.c. Fe matrix, which have risen to prominence as candidates for advanced nuclear reactor applications. Because irradiation can profoundly alter the morphology and composition of these solut...
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Veröffentlicht in: | JOM (1989) 2020-11, Vol.72 (11), p.4017-4027 |
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description | Solute nanoclusters are critical to the structural and mechanical integrity of numerous alloys based on the b.c.c. Fe matrix, which have risen to prominence as candidates for advanced nuclear reactor applications. Because irradiation can profoundly alter the morphology and composition of these solute nanoclusters, it is critical to understand and predict solute clustering behavior in the presence of irradiation. In this study, we advance a simple theory to model irradiation-induced nanocluster evolution subject to different irradiating particles. The model is trained and validated with experimental data following an approach similar to training a machine learning algorithm, resulting in an agile model that can be used for rapid screening of new alloys. Using the model, nanocluster evolution is found to depend upon the disordering parameter (i.e., cluster morphology and dose rate) and irradiation temperature, and is most sensitive to the solute migration, vacancy formation, and vacancy migration energies. Results are discussed with respect to the irradiation temperature shift for varying irradiating particle types and dose rates. |
doi_str_mv | 10.1007/s11837-020-04365-4 |
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Fe-Based Alloys</title><source>SpringerLink Journals - AutoHoldings</source><creator>Swenson, Matthew J. ; Wharry, Janelle P.</creator><creatorcontrib>Swenson, Matthew J. ; Wharry, Janelle P.</creatorcontrib><description>Solute nanoclusters are critical to the structural and mechanical integrity of numerous alloys based on the b.c.c. Fe matrix, which have risen to prominence as candidates for advanced nuclear reactor applications. Because irradiation can profoundly alter the morphology and composition of these solute nanoclusters, it is critical to understand and predict solute clustering behavior in the presence of irradiation. In this study, we advance a simple theory to model irradiation-induced nanocluster evolution subject to different irradiating particles. The model is trained and validated with experimental data following an approach similar to training a machine learning algorithm, resulting in an agile model that can be used for rapid screening of new alloys. Using the model, nanocluster evolution is found to depend upon the disordering parameter (i.e., cluster morphology and dose rate) and irradiation temperature, and is most sensitive to the solute migration, vacancy formation, and vacancy migration energies. Results are discussed with respect to the irradiation temperature shift for varying irradiating particle types and dose rates.</description><identifier>ISSN: 1047-4838</identifier><identifier>EISSN: 1543-1851</identifier><identifier>DOI: 10.1007/s11837-020-04365-4</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Alloys ; Atoms & subatomic particles ; Charged particles ; Chemistry/Food Science ; Clustering ; Earth Sciences ; Efficiency ; Engineering ; Environment ; Evolution ; Experiments ; Ferrous alloys ; Machine learning ; Morphology ; Nanoclusters ; Nanostructured Materials under Extreme Environments ; Nuclear reactors ; Physics ; Point defects ; Radiation ; Radiation dosage ; Rate theory ; Sensitivity analysis ; Tomography ; Vacancies ; Variables</subject><ispartof>JOM (1989), 2020-11, Vol.72 (11), p.4017-4027</ispartof><rights>The Minerals, Metals & Materials Society 2020</rights><rights>Copyright Springer Nature B.V. 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Fe-Based Alloys</title><title>JOM (1989)</title><addtitle>JOM</addtitle><description>Solute nanoclusters are critical to the structural and mechanical integrity of numerous alloys based on the b.c.c. Fe matrix, which have risen to prominence as candidates for advanced nuclear reactor applications. Because irradiation can profoundly alter the morphology and composition of these solute nanoclusters, it is critical to understand and predict solute clustering behavior in the presence of irradiation. In this study, we advance a simple theory to model irradiation-induced nanocluster evolution subject to different irradiating particles. The model is trained and validated with experimental data following an approach similar to training a machine learning algorithm, resulting in an agile model that can be used for rapid screening of new alloys. Using the model, nanocluster evolution is found to depend upon the disordering parameter (i.e., cluster morphology and dose rate) and irradiation temperature, and is most sensitive to the solute migration, vacancy formation, and vacancy migration energies. Results are discussed with respect to the irradiation temperature shift for varying irradiating particle types and dose rates.</description><subject>Algorithms</subject><subject>Alloys</subject><subject>Atoms & subatomic particles</subject><subject>Charged particles</subject><subject>Chemistry/Food Science</subject><subject>Clustering</subject><subject>Earth Sciences</subject><subject>Efficiency</subject><subject>Engineering</subject><subject>Environment</subject><subject>Evolution</subject><subject>Experiments</subject><subject>Ferrous alloys</subject><subject>Machine learning</subject><subject>Morphology</subject><subject>Nanoclusters</subject><subject>Nanostructured Materials under Extreme Environments</subject><subject>Nuclear reactors</subject><subject>Physics</subject><subject>Point defects</subject><subject>Radiation</subject><subject>Radiation dosage</subject><subject>Rate theory</subject><subject>Sensitivity analysis</subject><subject>Tomography</subject><subject>Vacancies</subject><subject>Variables</subject><issn>1047-4838</issn><issn>1543-1851</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kEFLxDAQhYMouK7-AU8Bz1kzTdokx3VxdWFF0HoOaZNql9qsSXvYf2_WCt5kDm9g3jfDPISugS6AUnEbASQThGaUUM6KnPATNIOcMwIyh9PUUy4Il0yeo4sYdzRBXMEMlS9mcLj8cD4c8JO3rsO-wZsQjG3N0PqebHo71s7iV9-Nybrqxji40PbvuO1xtahT4bUjdyYm07Lr_CFeorPGdNFd_eocva3vy9Uj2T4_bFbLLalZwQYCTtW8skxZUwF1wsiKU1apNM2cVRUzXEDOXAFNxpIoDiI31khDG140ls3RzbR3H_zX6OKgd34MfTqpMy6VELlgkFzZ5KqDjzG4Ru9D-2nCQQPVx_T0lJ5O6emf9DRPEJuguD_-6sLf6n-ob5G7cQ4</recordid><startdate>20201101</startdate><enddate>20201101</enddate><creator>Swenson, Matthew J.</creator><creator>Wharry, Janelle P.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>4T-</scope><scope>4U-</scope><scope>7SR</scope><scope>7TA</scope><scope>7WY</scope><scope>7XB</scope><scope>883</scope><scope>88I</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>K60</scope><scope>K6~</scope><scope>KB.</scope><scope>L.-</scope><scope>M0F</scope><scope>M2P</scope><scope>PDBOC</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>S0X</scope><orcidid>https://orcid.org/0000-0001-6163-3897</orcidid></search><sort><creationdate>20201101</creationdate><title>Rate Theory Model of Irradiation-Induced Solute Clustering in b.c.c. Fe-Based Alloys</title><author>Swenson, Matthew J. ; Wharry, Janelle P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-1e9c4bd39dab10e7a8b403b9c362ed9b3a47153e61f233e694175ada8a0f46fd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Alloys</topic><topic>Atoms & subatomic particles</topic><topic>Charged particles</topic><topic>Chemistry/Food Science</topic><topic>Clustering</topic><topic>Earth Sciences</topic><topic>Efficiency</topic><topic>Engineering</topic><topic>Environment</topic><topic>Evolution</topic><topic>Experiments</topic><topic>Ferrous alloys</topic><topic>Machine learning</topic><topic>Morphology</topic><topic>Nanoclusters</topic><topic>Nanostructured Materials under Extreme Environments</topic><topic>Nuclear reactors</topic><topic>Physics</topic><topic>Point defects</topic><topic>Radiation</topic><topic>Radiation dosage</topic><topic>Rate theory</topic><topic>Sensitivity analysis</topic><topic>Tomography</topic><topic>Vacancies</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Swenson, Matthew J.</creatorcontrib><creatorcontrib>Wharry, Janelle P.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Docstoc</collection><collection>University Readers</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>ABI/INFORM Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Trade & Industry (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Materials Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Trade & Industry</collection><collection>Science Database</collection><collection>Materials Science Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><jtitle>JOM (1989)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Swenson, Matthew J.</au><au>Wharry, Janelle P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Rate Theory Model of Irradiation-Induced Solute Clustering in b.c.c. 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subjects | Algorithms Alloys Atoms & subatomic particles Charged particles Chemistry/Food Science Clustering Earth Sciences Efficiency Engineering Environment Evolution Experiments Ferrous alloys Machine learning Morphology Nanoclusters Nanostructured Materials under Extreme Environments Nuclear reactors Physics Point defects Radiation Radiation dosage Rate theory Sensitivity analysis Tomography Vacancies Variables |
title | Rate Theory Model of Irradiation-Induced Solute Clustering in b.c.c. Fe-Based Alloys |
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