ARRID: ANN-based Rotordynamics for Robust and Integrated Design
The purpose of this study is to introduce ANN-based software for the fast evaluation of rotordynamics in the context of robust and integrated design. It is based on a surrogate model made of ensembles of artificial neural networks running in a Bokeh web application. The use of a surrogate model has...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | Massoudi, Soheyl Schiffmann, Jürg |
| description | The purpose of this study is to introduce ANN-based software for the fast
evaluation of rotordynamics in the context of robust and integrated design. It
is based on a surrogate model made of ensembles of artificial neural networks
running in a Bokeh web application. The use of a surrogate model has sped up
the computation by three orders of magnitude compared to the current models.
ARRID offers fast performance information, including the effect of
manufacturing deviations. As such, it helps the designer to make optimal design
choices early in the design process. The designer can manipulate the parameters
of the design and the operating conditions to obtain performance information in
a matter of seconds. |
| doi_str_mv | 10.48550/arxiv.2208.12640 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2208_12640</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2208_12640</sourcerecordid><originalsourceid>FETCH-LOGICAL-a670-a215d8a7c0d6e051e317b4468029f899c2cec6dc841b03190d047eb888e38e693</originalsourceid><addsrcrecordid>eNotz8tqwzAUBFBtuihpP6Cr6gfsXj0sS9kUk_RhCCmY7M2VdB0MiV1ktzR_3zTNamAYBg5jDwJybYsCnjD99N-5lGBzIY2GW_ZcNU29XvJqu808ThR5M85jiqcBj32YeDemc-O_ppnjEHk9zLRPOJ93a5r6_XDHbjo8THR_zQXbvb7sVu_Z5uOtXlWbDE0JGUpRRItlgGgICkFKlF5rY0G6zjoXZKBgYrBaeFDCQQRdkrfWkrJknFqwx__bi6D9TP0R06n9k7QXifoFr45Bkg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>ARRID: ANN-based Rotordynamics for Robust and Integrated Design</title><source>arXiv.org</source><creator>Massoudi, Soheyl ; Schiffmann, Jürg</creator><creatorcontrib>Massoudi, Soheyl ; Schiffmann, Jürg</creatorcontrib><description>The purpose of this study is to introduce ANN-based software for the fast
evaluation of rotordynamics in the context of robust and integrated design. It
is based on a surrogate model made of ensembles of artificial neural networks
running in a Bokeh web application. The use of a surrogate model has sped up
the computation by three orders of magnitude compared to the current models.
ARRID offers fast performance information, including the effect of
manufacturing deviations. As such, it helps the designer to make optimal design
choices early in the design process. The designer can manipulate the parameters
of the design and the operating conditions to obtain performance information in
a matter of seconds.</description><identifier>DOI: 10.48550/arxiv.2208.12640</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Neural and Evolutionary Computing</subject><creationdate>2022-08</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,777,882</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2208.12640$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2208.12640$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Massoudi, Soheyl</creatorcontrib><creatorcontrib>Schiffmann, Jürg</creatorcontrib><title>ARRID: ANN-based Rotordynamics for Robust and Integrated Design</title><description>The purpose of this study is to introduce ANN-based software for the fast
evaluation of rotordynamics in the context of robust and integrated design. It
is based on a surrogate model made of ensembles of artificial neural networks
running in a Bokeh web application. The use of a surrogate model has sped up
the computation by three orders of magnitude compared to the current models.
ARRID offers fast performance information, including the effect of
manufacturing deviations. As such, it helps the designer to make optimal design
choices early in the design process. The designer can manipulate the parameters
of the design and the operating conditions to obtain performance information in
a matter of seconds.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Neural and Evolutionary Computing</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz8tqwzAUBFBtuihpP6Cr6gfsXj0sS9kUk_RhCCmY7M2VdB0MiV1ktzR_3zTNamAYBg5jDwJybYsCnjD99N-5lGBzIY2GW_ZcNU29XvJqu808ThR5M85jiqcBj32YeDemc-O_ppnjEHk9zLRPOJ93a5r6_XDHbjo8THR_zQXbvb7sVu_Z5uOtXlWbDE0JGUpRRItlgGgICkFKlF5rY0G6zjoXZKBgYrBaeFDCQQRdkrfWkrJknFqwx__bi6D9TP0R06n9k7QXifoFr45Bkg</recordid><startdate>20220825</startdate><enddate>20220825</enddate><creator>Massoudi, Soheyl</creator><creator>Schiffmann, Jürg</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20220825</creationdate><title>ARRID: ANN-based Rotordynamics for Robust and Integrated Design</title><author>Massoudi, Soheyl ; Schiffmann, Jürg</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a670-a215d8a7c0d6e051e317b4468029f899c2cec6dc841b03190d047eb888e38e693</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Neural and Evolutionary Computing</topic><toplevel>online_resources</toplevel><creatorcontrib>Massoudi, Soheyl</creatorcontrib><creatorcontrib>Schiffmann, Jürg</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Massoudi, Soheyl</au><au>Schiffmann, Jürg</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>ARRID: ANN-based Rotordynamics for Robust and Integrated Design</atitle><date>2022-08-25</date><risdate>2022</risdate><abstract>The purpose of this study is to introduce ANN-based software for the fast
evaluation of rotordynamics in the context of robust and integrated design. It
is based on a surrogate model made of ensembles of artificial neural networks
running in a Bokeh web application. The use of a surrogate model has sped up
the computation by three orders of magnitude compared to the current models.
ARRID offers fast performance information, including the effect of
manufacturing deviations. As such, it helps the designer to make optimal design
choices early in the design process. The designer can manipulate the parameters
of the design and the operating conditions to obtain performance information in
a matter of seconds.</abstract><doi>10.48550/arxiv.2208.12640</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.2208.12640 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_2208_12640 |
| source | arXiv.org |
| subjects | Computer Science - Artificial Intelligence Computer Science - Neural and Evolutionary Computing |
| title | ARRID: ANN-based Rotordynamics for Robust and Integrated Design |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-19T02%3A48%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=ARRID:%20ANN-based%20Rotordynamics%20for%20Robust%20and%20Integrated%20Design&rft.au=Massoudi,%20Soheyl&rft.date=2022-08-25&rft_id=info:doi/10.48550/arxiv.2208.12640&rft_dat=%3Carxiv_GOX%3E2208_12640%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |