Accelerating structural dynamics simulations with localised phenomena through matrix compression and projection‐based model order reduction
In this work, a novel approach is introduced for accelerating the solution of structural dynamics problems in the presence of localised phenomena, such as cracks. For this category of problems, conventional projection‐based Model Order Reduction (MOR) methods are either limited with respect to the r...
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Published in | International journal for numerical methods in engineering Vol. 125; no. 10 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Hoboken, USA
John Wiley & Sons, Inc
30.05.2024
Wiley Subscription Services, Inc Wiley Blackwell (John Wiley & Sons) |
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Online Access | Get full text |
ISSN | 0029-5981 1097-0207 |
DOI | 10.1002/nme.7445 |
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Abstract | In this work, a novel approach is introduced for accelerating the solution of structural dynamics problems in the presence of localised phenomena, such as cracks. For this category of problems, conventional projection‐based Model Order Reduction (MOR) methods are either limited with respect to the range of system configurations that can be represented or require frequent solutions of the Full Order Model (FOM) to update the low‐dimensional spaces, in which solutions are represented. In the proposed approach, low‐dimensional spaces, constructed for the healthy structure, are enriched with appropriately selected columns of the flexibility matrix of the system. It can be shown that these spaces contain the solution to the original problem for the static case, while their dimension is much smaller. In order to allow their online construction for arbitrary localised features, the full flexibility matrix of the system should be available. To this end, a hierarchical representation is used for the matrices involved, allowing to compute the flexibility matrix efficiently and with reduced memory requirements. The resulting method offers significant speedups, without sacrificing the flexibility and accuracy of the full order model. The performance and limitations of the approach are studied through a series of examples in structural dynamics. |
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AbstractList | In this work, a novel approach is introduced for accelerating the solution of structural dynamics problems in the presence of localised phenomena, such as cracks. For this category of problems, conventional projection‐based Model Order Reduction (MOR) methods are either limited with respect to the range of system configurations that can be represented or require frequent solutions of the Full Order Model (FOM) to update the low‐dimensional spaces, in which solutions are represented. In the proposed approach, low‐dimensional spaces, constructed for the healthy structure, are enriched with appropriately selected columns of the flexibility matrix of the system. It can be shown that these spaces contain the solution to the original problem for the static case, while their dimension is much smaller. In order to allow their online construction for arbitrary localised features, the full flexibility matrix of the system should be available. To this end, a hierarchical representation is used for the matrices involved, allowing to compute the flexibility matrix efficiently and with reduced memory requirements. The resulting method offers significant speedups, without sacrificing the flexibility and accuracy of the full order model. The performance and limitations of the approach are studied through a series of examples in structural dynamics. Abstract In this work, a novel approach is introduced for accelerating the solution of structural dynamics problems in the presence of localised phenomena, such as cracks. For this category of problems, conventional projection‐based Model Order Reduction (MOR) methods are either limited with respect to the range of system configurations that can be represented or require frequent solutions of the Full Order Model (FOM) to update the low‐dimensional spaces, in which solutions are represented. In the proposed approach, low‐dimensional spaces, constructed for the healthy structure, are enriched with appropriately selected columns of the flexibility matrix of the system. It can be shown that these spaces contain the solution to the original problem for the static case, while their dimension is much smaller. In order to allow their online construction for arbitrary localised features, the full flexibility matrix of the system should be available. To this end, a hierarchical representation is used for the matrices involved, allowing to compute the flexibility matrix efficiently and with reduced memory requirements. The resulting method offers significant speedups, without sacrificing the flexibility and accuracy of the full order model. The performance and limitations of the approach are studied through a series of examples in structural dynamics. |
Author | Agathos, Konstantinos Vlachas, Konstantinos Chatzi, Eleni Garland, Anthony |
Author_xml | – sequence: 1 givenname: Konstantinos surname: Agathos fullname: Agathos, Konstantinos email: k.agathos@exeter.ac.uk organization: University of Exeter – sequence: 2 givenname: Konstantinos surname: Vlachas fullname: Vlachas, Konstantinos organization: ETH Zurich – sequence: 3 givenname: Anthony surname: Garland fullname: Garland, Anthony organization: Sandia National Laboratories – sequence: 4 givenname: Eleni surname: Chatzi fullname: Chatzi, Eleni organization: ETH Zurich |
BackLink | https://www.osti.gov/biblio/2301819$$D View this record in Osti.gov |
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Snippet | In this work, a novel approach is introduced for accelerating the solution of structural dynamics problems in the presence of localised phenomena, such as... Abstract In this work, a novel approach is introduced for accelerating the solution of structural dynamics problems in the presence of localised phenomena,... |
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SubjectTerms | Columns (structural) Dynamic structural analysis Flexibility Flexibility matrix fracture mechanics hierarchically off‐diagonal low‐rank matrices model order reduction Model reduction |
Title | Accelerating structural dynamics simulations with localised phenomena through matrix compression and projection‐based model order reduction |
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