國立虎尾科技大學 |

Sub-structure Coupling for Dynamic Analysis = Application to Complex Simulation-Based Problems Involving Uncertainty /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Sub-structure Coupling for Dynamic Analysis/ by Hector Jensen, Costas Papadimitriou.
Reminder of title:	Application to Complex Simulation-Based Problems Involving Uncertainty /
Author:	Jensen, Hector.
other author:	Papadimitriou, Costas.
Description:	XIII, 227 p. 106 illus., 47 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Mechanics. -
Online resource:	https://doi.org/10.1007/978-3-030-12819-7
ISBN:	9783030128197

Sub-structure Coupling for Dynamic Analysis = Application to Complex Simulation-Based Problems Involving Uncertainty /
Jensen, Hector.

Sub-structure Coupling for Dynamic AnalysisApplication to Complex Simulation-Based Problems Involving Uncertainty /[electronic resource] :by Hector Jensen, Costas Papadimitriou. - 1st ed. 2019. - XIII, 227 p. 106 illus., 47 illus. in color.online resource. - Lecture Notes in Applied and Computational Mechanics,891613-7736 ;. - Lecture Notes in Applied and Computational Mechanics,75.

Model Reduction Techniques for Structural Dynamic Analyses -- Parametrization of Reduced-Order Models Based on Normal Modes -- Parametrization of Reduced-Order Models Based on Global Interface Reduction -- Reliability Analysis of Dynamical Systems -- Reliability Sensitivity Analysis of Dynamical Systems -- Reliability-Based Design Optimization -- Bayesian Finite Element Model Updating.

This book combines a model reduction technique with an efficient parametrization scheme for the purpose of solving a class of complex and computationally expensive simulation-based problems involving finite element models. These problems, which have a wide range of important applications in several engineering fields, include reliability analysis, structural dynamic simulation, sensitivity analysis, reliability-based design optimization, Bayesian model validation, uncertainty quantification and propagation, etc. The solution of this type of problems requires a large number of dynamic re-analyses. To cope with this difficulty, a model reduction technique known as substructure coupling for dynamic analysis is considered. While the use of reduced order models alleviates part of the computational effort, their repetitive generation during the simulation processes can be computational expensive due to the substantial computational overhead that arises at the substructure level. In this regard, an efficient finite element model parametrization scheme is considered. When the division of the structural model is guided by such a parametrization scheme, the generation of a small number of reduced order models is sufficient to run the large number of dynamic re-analyses. Thus, a drastic reduction in computational effort is achieved without compromising the accuracy of the results. The capabilities of the developed procedures are demonstrated in a number of simulation-based problems involving uncertainty.

ISBN: 9783030128197

Standard No.: 10.1007/978-3-030-12819-7doiSubjects--Topical Terms:

527684
Mechanics.

LC Class. No.: TA349-359

Dewey Class. No.: 531

Sub-structure Coupling for Dynamic Analysis = Application to Complex Simulation-Based Problems Involving Uncertainty /
LDR:03410nam a22004095i 4500 001 1012788
003 DE-He213
005 20200706213740.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783030128197 $9 978-3-030-12819-7
024 7 $a 10.1007/978-3-030-12819-7 $2 doi
035 $a 978-3-030-12819-7
050 4 $a TA349-359
072 7 $a TGMD $2 bicssc
072 7 $a SCI096000 $2 bisacsh
072 7 $a TGMD $2 thema
082 0 4 $a 531 $2 23
100 1 $a Jensen, Hector. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1307075
245 1 0 $a Sub-structure Coupling for Dynamic Analysis $h [electronic resource] : $b Application to Complex Simulation-Based Problems Involving Uncertainty / $c by Hector Jensen, Costas Papadimitriou.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a XIII, 227 p. 106 illus., 47 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Lecture Notes in Applied and Computational Mechanics, $x 1613-7736 ; $v 89
505 0 $a Model Reduction Techniques for Structural Dynamic Analyses -- Parametrization of Reduced-Order Models Based on Normal Modes -- Parametrization of Reduced-Order Models Based on Global Interface Reduction -- Reliability Analysis of Dynamical Systems -- Reliability Sensitivity Analysis of Dynamical Systems -- Reliability-Based Design Optimization -- Bayesian Finite Element Model Updating.
520 $a This book combines a model reduction technique with an efficient parametrization scheme for the purpose of solving a class of complex and computationally expensive simulation-based problems involving finite element models. These problems, which have a wide range of important applications in several engineering fields, include reliability analysis, structural dynamic simulation, sensitivity analysis, reliability-based design optimization, Bayesian model validation, uncertainty quantification and propagation, etc. The solution of this type of problems requires a large number of dynamic re-analyses. To cope with this difficulty, a model reduction technique known as substructure coupling for dynamic analysis is considered. While the use of reduced order models alleviates part of the computational effort, their repetitive generation during the simulation processes can be computational expensive due to the substantial computational overhead that arises at the substructure level. In this regard, an efficient finite element model parametrization scheme is considered. When the division of the structural model is guided by such a parametrization scheme, the generation of a small number of reduced order models is sufficient to run the large number of dynamic re-analyses. Thus, a drastic reduction in computational effort is achieved without compromising the accuracy of the results. The capabilities of the developed procedures are demonstrated in a number of simulation-based problems involving uncertainty.
650 0 $a Mechanics. $3 527684
650 0 $a Mechanics, Applied. $3 596630
650 0 $a Computer mathematics. $3 1199796
650 0 $a Probabilities. $3 527847
650 1 4 $a Solid Mechanics. $3 1211586
650 2 4 $a Computational Science and Engineering. $3 670319
650 2 4 $a Probability Theory and Stochastic Processes. $3 593945
700 1 $a Papadimitriou, Costas. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1109093
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030128180
776 0 8 $i Printed edition: $z 9783030128203
776 0 8 $i Printed edition: $z 9783030128210
830 0 $a Lecture Notes in Applied and Computational Mechanics, $x 1613-7736 ; $v 75 $3 1255102
856 4 0 $u https://doi.org/10.1007/978-3-030-12819-7
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)