A surrogate machine learning model for advanced gas-cooled reactor graphite core safety analysis
[Display omitted] •A machine learning model was developed to predict seismic core displacements from crack configurations for the advanced gas-cooled reactor.•The model was trained on a dataset from software which simulates a severe earthquake.•The aim was to increase the computational efficiency of...
Gespeichert in:
| Veröffentlicht in: | Nuclear engineering and design 2022-08, Vol.395, p.111842, Article 111842 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | 111842 |
| container_title | Nuclear engineering and design |
| container_volume | 395 |
| creator | Jones, H. Rhys Mu, Tingting Kudawoo, Dzifa Brown, Gavin Martinuzzi, Philippe McLachlan, Neil |
| description | [Display omitted]
•A machine learning model was developed to predict seismic core displacements from crack configurations for the advanced gas-cooled reactor.•The model was trained on a dataset from software which simulates a severe earthquake.•The aim was to increase the computational efficiency of data generation.
A surrogate machine learning model was developed with the aim of predicting seismic graphite core displacements from crack configurations for the advanced gas-cooled reactor. The model was trained on a dataset generated by a software package which simulates the behaviour of the graphite core during a severe earthquake. Several machine learning techniques, such as the use of convolutional neural networks, were identified as highly applicable to this particular problem. Through the development of the model, several observations and insights were garnered which may be of interest from a graphite core analysis and safety perspective. The best performing model was capable of making 95% of test set predictions within a 20 percentage point margin of the ground truth. |
| doi_str_mv | 10.1016/j.nucengdes.2022.111842 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2762942340</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0029549322001960</els_id><sourcerecordid>2762942340</sourcerecordid><originalsourceid>FETCH-LOGICAL-c392t-46aa247fb5aa029097d05238e13959503704867b4b618fea774b47007ae04a7c3</originalsourceid><addsrcrecordid>eNqFkE9LxDAQxYMouK5-BgOeW5M0bZrjsvgPBC8K3uJsOu1m6TZr0i7stzfLilfnMgPz3vDmR8gtZzlnvLrf5MNkcegajLlgQuSc81qKMzLjtRKZKvXnOZkxJnRWSl1ckqsYN-xYWszI14LGKQTfwYh0C3btBqQ9Qhjc0NGtb7CnrQ8Umj0MFhvaQcys930aA4Id064LsFu75Lc-II3Q4nigMEB_iC5ek4sW-og3v31OPh4f3pfP2evb08ty8ZrZQosxkxWAkKpdlQApKtOqYaUoauSFLnXJCsVkXamVXFW8bhGUkiupGFOATIKyxZzcne7ugv-eMI5m46eQQkQjVCW0FIVkSaVOKht8jAFbswtuC-FgODNHnGZj_nCaI05zwpmci5MT0xN7h8FE6_CIxAW0o2m8-_fGD9ecgjM</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2762942340</pqid></control><display><type>article</type><title>A surrogate machine learning model for advanced gas-cooled reactor graphite core safety analysis</title><source>Access via ScienceDirect (Elsevier)</source><creator>Jones, H. Rhys ; Mu, Tingting ; Kudawoo, Dzifa ; Brown, Gavin ; Martinuzzi, Philippe ; McLachlan, Neil</creator><creatorcontrib>Jones, H. Rhys ; Mu, Tingting ; Kudawoo, Dzifa ; Brown, Gavin ; Martinuzzi, Philippe ; McLachlan, Neil</creatorcontrib><description>[Display omitted]
•A machine learning model was developed to predict seismic core displacements from crack configurations for the advanced gas-cooled reactor.•The model was trained on a dataset from software which simulates a severe earthquake.•The aim was to increase the computational efficiency of data generation.
A surrogate machine learning model was developed with the aim of predicting seismic graphite core displacements from crack configurations for the advanced gas-cooled reactor. The model was trained on a dataset generated by a software package which simulates the behaviour of the graphite core during a severe earthquake. Several machine learning techniques, such as the use of convolutional neural networks, were identified as highly applicable to this particular problem. Through the development of the model, several observations and insights were garnered which may be of interest from a graphite core analysis and safety perspective. The best performing model was capable of making 95% of test set predictions within a 20 percentage point margin of the ground truth.</description><identifier>ISSN: 0029-5493</identifier><identifier>EISSN: 1872-759X</identifier><identifier>DOI: 10.1016/j.nucengdes.2022.111842</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Advanced gas-cooled reactor ; Artificial neural networks ; Convolutional neural network ; Core analysis ; Data analysis ; Data science ; Earthquakes ; Gas cooled reactors ; Graphite ; Learning algorithms ; Machine learning ; Neural networks ; Nuclear ; Nuclear safety ; Reactors ; Regression ; Seismic activity ; Shaker table ; Supervised learning ; Surrogate model</subject><ispartof>Nuclear engineering and design, 2022-08, Vol.395, p.111842, Article 111842</ispartof><rights>2022 The Author(s)</rights><rights>Copyright Elsevier BV Aug 15, 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c392t-46aa247fb5aa029097d05238e13959503704867b4b618fea774b47007ae04a7c3</citedby><cites>FETCH-LOGICAL-c392t-46aa247fb5aa029097d05238e13959503704867b4b618fea774b47007ae04a7c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.nucengdes.2022.111842$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Jones, H. Rhys</creatorcontrib><creatorcontrib>Mu, Tingting</creatorcontrib><creatorcontrib>Kudawoo, Dzifa</creatorcontrib><creatorcontrib>Brown, Gavin</creatorcontrib><creatorcontrib>Martinuzzi, Philippe</creatorcontrib><creatorcontrib>McLachlan, Neil</creatorcontrib><title>A surrogate machine learning model for advanced gas-cooled reactor graphite core safety analysis</title><title>Nuclear engineering and design</title><description>[Display omitted]
•A machine learning model was developed to predict seismic core displacements from crack configurations for the advanced gas-cooled reactor.•The model was trained on a dataset from software which simulates a severe earthquake.•The aim was to increase the computational efficiency of data generation.
A surrogate machine learning model was developed with the aim of predicting seismic graphite core displacements from crack configurations for the advanced gas-cooled reactor. The model was trained on a dataset generated by a software package which simulates the behaviour of the graphite core during a severe earthquake. Several machine learning techniques, such as the use of convolutional neural networks, were identified as highly applicable to this particular problem. Through the development of the model, several observations and insights were garnered which may be of interest from a graphite core analysis and safety perspective. The best performing model was capable of making 95% of test set predictions within a 20 percentage point margin of the ground truth.</description><subject>Advanced gas-cooled reactor</subject><subject>Artificial neural networks</subject><subject>Convolutional neural network</subject><subject>Core analysis</subject><subject>Data analysis</subject><subject>Data science</subject><subject>Earthquakes</subject><subject>Gas cooled reactors</subject><subject>Graphite</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Nuclear</subject><subject>Nuclear safety</subject><subject>Reactors</subject><subject>Regression</subject><subject>Seismic activity</subject><subject>Shaker table</subject><subject>Supervised learning</subject><subject>Surrogate model</subject><issn>0029-5493</issn><issn>1872-759X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNqFkE9LxDAQxYMouK5-BgOeW5M0bZrjsvgPBC8K3uJsOu1m6TZr0i7stzfLilfnMgPz3vDmR8gtZzlnvLrf5MNkcegajLlgQuSc81qKMzLjtRKZKvXnOZkxJnRWSl1ckqsYN-xYWszI14LGKQTfwYh0C3btBqQ9Qhjc0NGtb7CnrQ8Umj0MFhvaQcys930aA4Id064LsFu75Lc-II3Q4nigMEB_iC5ek4sW-og3v31OPh4f3pfP2evb08ty8ZrZQosxkxWAkKpdlQApKtOqYaUoauSFLnXJCsVkXamVXFW8bhGUkiupGFOATIKyxZzcne7ugv-eMI5m46eQQkQjVCW0FIVkSaVOKht8jAFbswtuC-FgODNHnGZj_nCaI05zwpmci5MT0xN7h8FE6_CIxAW0o2m8-_fGD9ecgjM</recordid><startdate>20220815</startdate><enddate>20220815</enddate><creator>Jones, H. Rhys</creator><creator>Mu, Tingting</creator><creator>Kudawoo, Dzifa</creator><creator>Brown, Gavin</creator><creator>Martinuzzi, Philippe</creator><creator>McLachlan, Neil</creator><general>Elsevier B.V</general><general>Elsevier BV</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7ST</scope><scope>7TB</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><scope>SOI</scope></search><sort><creationdate>20220815</creationdate><title>A surrogate machine learning model for advanced gas-cooled reactor graphite core safety analysis</title><author>Jones, H. Rhys ; Mu, Tingting ; Kudawoo, Dzifa ; Brown, Gavin ; Martinuzzi, Philippe ; McLachlan, Neil</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c392t-46aa247fb5aa029097d05238e13959503704867b4b618fea774b47007ae04a7c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Advanced gas-cooled reactor</topic><topic>Artificial neural networks</topic><topic>Convolutional neural network</topic><topic>Core analysis</topic><topic>Data analysis</topic><topic>Data science</topic><topic>Earthquakes</topic><topic>Gas cooled reactors</topic><topic>Graphite</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Neural networks</topic><topic>Nuclear</topic><topic>Nuclear safety</topic><topic>Reactors</topic><topic>Regression</topic><topic>Seismic activity</topic><topic>Shaker table</topic><topic>Supervised learning</topic><topic>Surrogate model</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jones, H. Rhys</creatorcontrib><creatorcontrib>Mu, Tingting</creatorcontrib><creatorcontrib>Kudawoo, Dzifa</creatorcontrib><creatorcontrib>Brown, Gavin</creatorcontrib><creatorcontrib>Martinuzzi, Philippe</creatorcontrib><creatorcontrib>McLachlan, Neil</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><jtitle>Nuclear engineering and design</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jones, H. Rhys</au><au>Mu, Tingting</au><au>Kudawoo, Dzifa</au><au>Brown, Gavin</au><au>Martinuzzi, Philippe</au><au>McLachlan, Neil</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A surrogate machine learning model for advanced gas-cooled reactor graphite core safety analysis</atitle><jtitle>Nuclear engineering and design</jtitle><date>2022-08-15</date><risdate>2022</risdate><volume>395</volume><spage>111842</spage><pages>111842-</pages><artnum>111842</artnum><issn>0029-5493</issn><eissn>1872-759X</eissn><abstract>[Display omitted]
•A machine learning model was developed to predict seismic core displacements from crack configurations for the advanced gas-cooled reactor.•The model was trained on a dataset from software which simulates a severe earthquake.•The aim was to increase the computational efficiency of data generation.
A surrogate machine learning model was developed with the aim of predicting seismic graphite core displacements from crack configurations for the advanced gas-cooled reactor. The model was trained on a dataset generated by a software package which simulates the behaviour of the graphite core during a severe earthquake. Several machine learning techniques, such as the use of convolutional neural networks, were identified as highly applicable to this particular problem. Through the development of the model, several observations and insights were garnered which may be of interest from a graphite core analysis and safety perspective. The best performing model was capable of making 95% of test set predictions within a 20 percentage point margin of the ground truth.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.nucengdes.2022.111842</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0029-5493 |
| ispartof | Nuclear engineering and design, 2022-08, Vol.395, p.111842, Article 111842 |
| issn | 0029-5493 1872-759X |
| language | eng |
| recordid | cdi_proquest_journals_2762942340 |
| source | Access via ScienceDirect (Elsevier) |
| subjects | Advanced gas-cooled reactor Artificial neural networks Convolutional neural network Core analysis Data analysis Data science Earthquakes Gas cooled reactors Graphite Learning algorithms Machine learning Neural networks Nuclear Nuclear safety Reactors Regression Seismic activity Shaker table Supervised learning Surrogate model |
| title | A surrogate machine learning model for advanced gas-cooled reactor graphite core safety analysis |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T17%3A47%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20surrogate%20machine%20learning%20model%20for%20advanced%20gas-cooled%20reactor%20graphite%20core%20safety%20analysis&rft.jtitle=Nuclear%20engineering%20and%20design&rft.au=Jones,%20H.%20Rhys&rft.date=2022-08-15&rft.volume=395&rft.spage=111842&rft.pages=111842-&rft.artnum=111842&rft.issn=0029-5493&rft.eissn=1872-759X&rft_id=info:doi/10.1016/j.nucengdes.2022.111842&rft_dat=%3Cproquest_cross%3E2762942340%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2762942340&rft_id=info:pmid/&rft_els_id=S0029549322001960&rfr_iscdi=true |