000 04113nam a22005655i 4500
001 978-3-030-12819-7
003 DE-He213
005 20220801214940.0
007 cr nn 008mamaa
008 190326s2019 sz | s |||| 0|eng d
020 _a9783030128197
_9978-3-030-12819-7
024 7 _a10.1007/978-3-030-12819-7
_2doi
050 4 _aTA349-359
072 7 _aTGMD
_2bicssc
072 7 _aSCI096000
_2bisacsh
072 7 _aTGMD
_2thema
082 0 4 _a620.105
_223
100 1 _aJensen, Hector.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_941428
245 1 0 _aSub-structure Coupling for Dynamic Analysis
_h[electronic resource] :
_bApplication to Complex Simulation-Based Problems Involving Uncertainty /
_cby Hector Jensen, Costas Papadimitriou.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXIII, 227 p. 106 illus., 47 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Applied and Computational Mechanics,
_x1860-0816 ;
_v89
505 0 _aModel 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 _aThis 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 _aMechanics, Applied.
_93253
650 0 _aSolids.
_93750
650 0 _aMathematics—Data processing.
_931594
650 0 _aProbabilities.
_94604
650 1 4 _aSolid Mechanics.
_931612
650 2 4 _aComputational Science and Engineering.
_941429
650 2 4 _aProbability Theory.
_917950
700 1 _aPapadimitriou, Costas.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_941430
710 2 _aSpringerLink (Online service)
_941431
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030128180
776 0 8 _iPrinted edition:
_z9783030128203
776 0 8 _iPrinted edition:
_z9783030128210
830 0 _aLecture Notes in Applied and Computational Mechanics,
_x1860-0816 ;
_v89
_941432
856 4 0 _uhttps://doi.org/10.1007/978-3-030-12819-7
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c76936
_d76936