Accelerating parametric studies in computational dynamics: Selective modal re-orthogonalization versus model order reduction methods

被引:5
作者
Garcia-Martinez, J. [1 ]
Herrada, F. J. [2 ]
Hermanns, L. K. H. [3 ]
Fraile, A. [3 ]
Montans, F. J. [4 ]
机构
[1] INTA, Struct & Mech Div, Ctra Torrejon Ajalvir Km 4, Torrejon De Ardoz 28850, Spain
[2] Minist Econ Ind & Competitividad, Subdirecc Gen Grandes Instalac Cientif Tecn, Paseo Castellana 162, Madrid 28046, Spain
[3] Univ Politecn Madrid, Escuela Tecn Super Ingn Ind, C Jose Gutierrez Abascal 2, E-28006 Madrid, Spain
[4] Univ Politecn Madrid, Escuela Tecn Super Ingn Aeronaut & Espacio, Plaza Cardenal Cisneros 3, E-28040 Madrid, Spain
关键词
Parametric analysis; Model order reduction methods; Static condensation; Dynamic condensation; Structural dynamics; Large models; STRUCTURAL MODIFICATION; EIGENVALUES; STIFFNESS; SYSTEMS; MASS;
D O I
10.1016/j.advengsoft.2017.02.006
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In the dynamic analysis of a structure, it is frequent the use of parametric studies to consider several design configurations or possible modifications of the structure. These changes modify the physical properties of the structure, and therefore, finite element models need updates in order to compute the response of the modified structure. A wide variety of model order reduction methods which may be suitable for this task has been developed, either static or dynamic, which also consider non-classical damping, which is especially relevant in the design of vibration absorption devices. In this paper, we compare the use of selective reorthogonalization with other model order reduction techniques, both in terms of computational time and in accuracy, using three computer architectures. The proposed reorthogonalization method allows for parametric structural modifications and evaluates the solution using a modified complex modal domain only along a selection of a few degrees of freedom that are relevant for the dynamic analysis of the system. This acceleration method does not result in any significative decrease of the quality of the results of interest due to approximations, whereas remains very competitive when compared to usual model order reduction techniques. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:24 / 36
页数:13
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