Finite Element Method

Bathe, Klaus‐Jürgen

doi:10.1002/9780470050118.ecse159

Cited by 167 publications

(109 citation statements)

References 27 publications

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“…Second, the solutions of the structural equations and the flow equations have been obtained using a direct sparse solver with full Newton-Raphson iterations. However, different solver schemes, in particular much more efficient for the fluid equations when the number of elements becomes very large, are frequently used, in ADINA and otherwise, see for example, [43]. The performance comparisons may look different when different problems are solved and other solver schemes are used.…”

Section: Discussionmentioning

confidence: 99%

Performance of a new partitioned procedure versus a monolithic procedure in fluid–structure interaction

Degroote

Bathe

Vierendeels

2009

Computers & Structures

390

391

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a b s t r a c tFluid-structure interaction (FSI) can be simulated in a monolithic way by solving the flow and structural equations simultaneously and in a partitioned way with separate solvers for the flow equations and the structural equations. A partitioned quasi-Newton technique which solves the coupled problem through nonlinear equations corresponding to the interface position is presented and its performance is compared with a monolithic Newton algorithm. Various structural configurations with an incompressible fluid are solved, and the ratio of the time for the partitioned simulation, when convergence is reached, to the time for the monolithic simulation is found to be between 1/2 and 4. However, in this comparison of the partitioned and monolithic simulations, the flow and structural equations have been solved with a direct sparse solver in full Newton-Raphson iterations, only relatively small problems have been solved and this ratio would likely change if large industrial problems were considered or if other solution strategies were used.

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Section: Discussionmentioning

confidence: 99%

Performance of a new partitioned procedure versus a monolithic procedure in fluid–structure interaction

Degroote

Bathe

Vierendeels

2009

Computers & Structures

390

391

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“…While problems of various characteristics were solved, still, only specific problems were considered and in general rather small problems in number of equations. Moreover, the solutions of the structural equations and the flow equations were calculated using a direct sparse solver with full Newton-Raphson iterations although different solver schemes, in particular much more efficient for the fluid equations when the number of elements becomes very large [42], are frequently used in the partitioned approach. The performance comparisons may consequently look different when different problems are solved and other solver schemes are used.…”

Section: Introductionmentioning

confidence: 99%

Performance of partitioned procedures in fluid–structure interaction

Degroote¹,

Haelterman²,

Annerel³

et al. 2010

Computers & Structures

146

115

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Partitioned simulations of fluid-structure interaction can be solved for the interface's position with Newton-Raphson iterations but obtaining the exact Jacobian is impossible if the solvers are "black boxes". It is demonstrated that only an approximate Jacobian is needed, as long as it describes the reaction to certain components of the error on the interface's position. Based on this insight, a quasiNewton coupling algorithm with an approximation for the inverse of the Jacobian (IQN-ILS) has been developed and compared with a monolithic solver in previous work. Here, IQN-ILS is compared with other partitioned schemes such as IBQN-LS, Aitken relaxation and Interface-GMRES(R).

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“…Recourse is always made to some type of mathematical model, usually a set of partial differential equations (PDEs). The resulting problem is solved numerically using a wide variety of discretisation methods including finite element methods [22][23][24][25][26], finite differences, meshfree methods [27], isogeometric approaches [28,29], geometry independent field approximation [30,31], scaled-boundary finite elements [32][33][34][35][36], boundary element approaches [37], enriched boundary elements [38] or combinations thereof [39][40][41].…”

Section: Case Study 2: Digital Twins In Engineering and Personalised mentioning

confidence: 99%

What makes Data Science different? A discussion involving Statistics2.0 and Computational Sciences

Ley

Bordas

2018

Int J Data Sci Anal

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Data Science is today one of the main buzzwords, be it in business, industrial or academic settings. Machine learning, experimental design, data-driven modelling are all, undoubtedly, rising disciplines if one goes by the soaring number of research papers and patents appearing each year. The prospect of becoming a "Data Scientist" appeals to many. A discussion panel organised as part of the European Data Science Conference (European Association for Data Science (EuADS)) https:// euads.org/edsc/ asked the question: "What makes Data Science different?" In this paper, we give our own, personal and multi-faceted view on this question, from a Statistics and an Engineering perspective. In particular, we compare Data Science to Statistics and discuss the connection between Data Science and Computational Sciences.

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Finite Element Method

Cited by 167 publications

References 27 publications

Performance of a new partitioned procedure versus a monolithic procedure in fluid–structure interaction

Performance of a new partitioned procedure versus a monolithic procedure in fluid–structure interaction

Performance of partitioned procedures in fluid–structure interaction

What makes Data Science different? A discussion involving Statistics2.0 and Computational Sciences

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