A Finite Element based Large Increment Method for Nonlinear Structural Dynamic Analysis

2009 ◽  
Author(s):  
W. Barham ◽  
A.J. Aref ◽  
G.F. Dargush
Keyword(s):  
Finite Element ◽  
Dynamic Analysis ◽  
Structural Dynamic ◽  
Nonlinear Structural ◽  
Large Increment

FE Meshing Tool for the Dynamic Analysis of Beam-Like Structures

1991 ◽  
Author(s):  
M. De Smet ◽  
H. Van Brussel ◽  
P. Sas
Keyword(s):  
Finite Element ◽  
Dynamic Analysis ◽  
Adaptive Mesh Refinement ◽  
Dynamic Behaviour ◽  
A Priori ◽  
Adaptive Mesh ◽  
Structural Dynamic ◽  
Resonant Frequencies ◽  
Tapered Beam ◽  
Mesh Density

Abstract This paper outlines a methodology for the automatic creation of finite element meshes used for calculating structural dynamic behaviour. Starting from the idealized geometry of the structure, the proposed Intelligent Meshing Tool determines a mesh density that a priori guarantees the resonant frequencies to be calculated within predetermined accuracy limits. Unlike most existing techniques for automatic meshing, this technique is not inherently adaptive, but does not exclude an extra adaptive mesh refinement step, either. Implementation of this methodology is discussed for structures composed of straight uniform, curved and tapered beam-like components. The feasibility of the meshing strategy is illustrated by means of an example.

Efficient Uncertainty Quantification in Structural Dynamic Analysis Using Two-Level Gaussian Processes

2015 ◽  
Author(s):  
Kai Zhou ◽  
Pei Cao ◽  
Jiong Tang
Keyword(s):  
Finite Element ◽  
Dynamic Analysis ◽  
Uncertainty Quantification ◽  
Large Scale ◽  
Computational Cost ◽  
Response Prediction ◽  
Element Model ◽  
High Fidelity ◽  
Structural Dynamic ◽  
Rapid Sampling

Uncertainty quantification is an important aspect in structural dynamic analysis. Since practical structures are complex and oftentimes need to be characterized by large-scale finite element models, component mode synthesis (CMS) method is widely adopted for order-reduced modeling. Even with the model order-reduction, the computational cost for uncertainty quantification can still be prohibitive. In this research, we utilize a two-level Gaussian process emulation to achieve rapid sampling and response prediction under uncertainty, in which the low- and high-fidelity data extracted from CMS and full-scale finite element model are incorporated in an integral manner. The possible bias of low-fidelity data is then corrected through high-fidelity data. For the purpose of reducing the emulation runs, we further employ Bayesian inference approach to calibrate the order-reduced model in a probabilistic manner conditioned on multiple predicted response distributions of concern. Case studies are carried out to validate the effectiveness of proposed methodology.

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