Model order reduction based on proper generalized decomposition for the propagation of uncertainties in structural dynamics

被引:51
作者
Chevreuil, Mathilde [1 ]
Nouy, Anthony [1 ]
机构
[1] Univ Nantes, GeM, LUNAM Univ,CNRS,UMR 6183, Inst Rech Genie Civil & Mecan,Ecole Cent Nantes, F-44322 Nantes 3, France
关键词
uncertainty propagation; spectral stochastic methods; structural dynamics; model reduction; proper generalized decomposition; tensor product approximation; separated representations; PARTIAL-DIFFERENTIAL-EQUATIONS; POLYNOMIAL CHAOS METHOD; FINITE-ELEMENT-METHOD; SPECTRAL DECOMPOSITION; ELLIPTIC PROBLEMS;
D O I
10.1002/nme.3249
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A priori model reduction methods based on separated representations are introduced for the prediction of the low frequency response of uncertain structures within a parametric stochastic framework. The proper generalized decomposition method is used to construct a quasi-optimal separated representation of the random solution at some frequency samples. At each frequency, an accurate representation of the solution is obtained on reduced bases of spatial functions and stochastic functions. An extraction of the deterministic bases allows for the generation of a global reduced basis yielding a reduced order model of the uncertain structure, which appears to be accurate on the whole frequency band under study and for all values of input random parameters. This strategy can be seen as an alternative to traditional constructions of reduced order models in structural dynamics in the presence of parametric uncertainties. This reduced order model can then be used for further analyses such as the computation of the response at unresolved frequencies or the computation of more accurate stochastic approximations at some frequencies of interest. Because the dynamic response is highly nonlinear with respect to the input random parameters, a second level of separation of variables is introduced for the representation of functions of multiple random parameters, thus allowing the introduction of very fine approximations in each parametric dimension even when dealing with high parametric dimension. Copyright (C) 2011 John Wiley & Sons, Ltd.
引用
收藏
页码:241 / 268
页数:28
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