Iterative Component Mode Synthesis Using a Priori and a Posteriori Criteria
An iterative algorithm of component mode synthesis for a rational reduced-order modeling is proposed. The present algorithm consists of three steps: initial mode selection with a priori criterion, model reduction using the selected modes, and a posteriori error evaluation. The algorithm is based on...
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Veröffentlicht in: | AIAA journal 2019-05, Vol.57 (5), p.2145-2157 |
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description | An iterative algorithm of component mode synthesis for a rational reduced-order modeling is proposed. The present algorithm consists of three steps: initial mode selection with a priori criterion, model reduction using the selected modes, and a posteriori error evaluation. The algorithm is based on the Craig–Bampton method, which is one of the most widely used techniques. Specifically, the present algorithm uses, in a judicious manner, both an a priori mode selection criterion employing the moment-matching strategy and an a posteriori error estimation technique in evaluating the relative eigenvalue errors. If desired, an optional step of boundary interface reduction offers an additional possibility of obtaining a more compact model. The proposed iterative algorithm is recommended to researchers as well as commercialized software users in developing linear reduced-order structural dynamics models. Numerical examples are presented for its practical usage and performance. |
doi_str_mv | 10.2514/1.J057661 |
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C ; Chae, Soo-Won</creator><creatorcontrib>Kim, Soo Min ; Kim, Jin-Gyun ; Park, K. C ; Chae, Soo-Won</creatorcontrib><description>An iterative algorithm of component mode synthesis for a rational reduced-order modeling is proposed. The present algorithm consists of three steps: initial mode selection with a priori criterion, model reduction using the selected modes, and a posteriori error evaluation. The algorithm is based on the Craig–Bampton method, which is one of the most widely used techniques. Specifically, the present algorithm uses, in a judicious manner, both an a priori mode selection criterion employing the moment-matching strategy and an a posteriori error estimation technique in evaluating the relative eigenvalue errors. If desired, an optional step of boundary interface reduction offers an additional possibility of obtaining a more compact model. The proposed iterative algorithm is recommended to researchers as well as commercialized software users in developing linear reduced-order structural dynamics models. Numerical examples are presented for its practical usage and performance.</description><identifier>ISSN: 0001-1452</identifier><identifier>EISSN: 1533-385X</identifier><identifier>DOI: 10.2514/1.J057661</identifier><language>eng</language><publisher>Virginia: American Institute of Aeronautics and Astronautics</publisher><subject>Algorithms ; Commercialization ; Component mode synthesis ; Criteria ; Eigenvalues ; Iterative algorithms ; Iterative methods ; Modal choice ; Model reduction ; Reduced order models</subject><ispartof>AIAA journal, 2019-05, Vol.57 (5), p.2145-2157</ispartof><rights>Copyright © 2019 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. All requests for copying and permission to reprint should be submitted to CCC at ; employ the eISSN to initiate your request. See also AIAA Rights and Permissions .</rights><rights>Copyright © 2019 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. All requests for copying and permission to reprint should be submitted to CCC at www.copyright.com; employ the eISSN 1533-385X to initiate your request. 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The proposed iterative algorithm is recommended to researchers as well as commercialized software users in developing linear reduced-order structural dynamics models. Numerical examples are presented for its practical usage and performance.</description><subject>Algorithms</subject><subject>Commercialization</subject><subject>Component mode synthesis</subject><subject>Criteria</subject><subject>Eigenvalues</subject><subject>Iterative algorithms</subject><subject>Iterative methods</subject><subject>Modal choice</subject><subject>Model reduction</subject><subject>Reduced order models</subject><issn>0001-1452</issn><issn>1533-385X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNplkE9LAzEQxYMoWKsHv0FAEDxsnSSb3exRFv9UKwpa8Bamm0RT7KYmW6Hf3q0tePA084bfvAePkFMGIy5ZfslG9yDLomB7ZMCkEJlQ8m2fDACAZSyX_JAcpTTvFS8VG5CHcWcjdv7b0joslqG1bUcfg7H0Zd12Hzb5RKfJt-8U6XP0IXqKrdmIkPrP30Md_WbFY3Lg8DPZk90ckunN9Wt9l02ebsf11SRDrlSXiZnInXHNTMpcVlVhZVUil7nLlQNwTTWTlRAgjTOWMds0pgJVKNdgYRyWSgzJ2dZ3GcPXyqZOz8Mqtn2k5n2CAig59NTFlmpiSClap5fRLzCuNQO96UozveuqZ8-3LHrEP7f_4A90g2cB</recordid><startdate>201905</startdate><enddate>201905</enddate><creator>Kim, Soo Min</creator><creator>Kim, Jin-Gyun</creator><creator>Park, K. 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C ; Chae, Soo-Won</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a288t-3b34fdfcb5545996e597a254f48f00fc9b593305dfde11eccd90868fca6dfa783</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Commercialization</topic><topic>Component mode synthesis</topic><topic>Criteria</topic><topic>Eigenvalues</topic><topic>Iterative algorithms</topic><topic>Iterative methods</topic><topic>Modal choice</topic><topic>Model reduction</topic><topic>Reduced order models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Soo Min</creatorcontrib><creatorcontrib>Kim, Jin-Gyun</creatorcontrib><creatorcontrib>Park, K. 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Specifically, the present algorithm uses, in a judicious manner, both an a priori mode selection criterion employing the moment-matching strategy and an a posteriori error estimation technique in evaluating the relative eigenvalue errors. If desired, an optional step of boundary interface reduction offers an additional possibility of obtaining a more compact model. The proposed iterative algorithm is recommended to researchers as well as commercialized software users in developing linear reduced-order structural dynamics models. Numerical examples are presented for its practical usage and performance.</abstract><cop>Virginia</cop><pub>American Institute of Aeronautics and Astronautics</pub><doi>10.2514/1.J057661</doi><tpages>13</tpages></addata></record> |
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subjects | Algorithms Commercialization Component mode synthesis Criteria Eigenvalues Iterative algorithms Iterative methods Modal choice Model reduction Reduced order models |
title | Iterative Component Mode Synthesis Using a Priori and a Posteriori Criteria |
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