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710	2		_aSpringerLink (Online service) _9106996
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008			190326s2019 gw a o |||| 0|eng d
020			_a9783030128197
024	7		_a10.1007/978-3-030-12819-7 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_a T57.62 _b2019 EB
100	1		_aJensen, Hector _eautor _4aut _9673182
245	1	0	_aSub-structure Coupling for Dynamic Analysis : _bApplication to Complex Simulation-Based Problems Involving Uncertainty _cby Hector Jensen, Costas Papadimitriou.
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2019.
300			_a1 recurso en línea (XIII, 227 páginas) _b106 ilustraciones, 47 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aEngineering (Springer-11647)
490	0		_aLecture Notes in Applied and Computational Mechanics _x1613-7736 ; _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	3		_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.
988			_aPrimersemestre_2019_Engineering
650		7	_2embne _9138672 _aMétodos de simulación
700	1		_aPapadimitriou, Costas. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _998730
776	0	8	_iPrinted edition: _z9783030128180
776	0	8	_iPrinted edition: _z9783030128203
776	0	8	_iPrinted edition: _z9783030128210
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-12819-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_dz _feng _ggw _h0 _b04/2020 _ea _zSI