Component Mode Synthesis Order-Reduction for Dynamic Analysis of Structure Modeled With NURBS Finite Element

2016 ◽  
Vol 138 (2) ◽  
Author(s):  
K. Zhou ◽  
G. Liang ◽  
J. Tang
Keyword(s):  
Finite Element ◽  
Dynamic Analysis ◽  
Large Scale ◽  
Order Reduction ◽  
Reduction Technique ◽  
Component Mode Synthesis ◽  
Original Structure ◽  
Surface Geometry ◽  
Compatibility Conditions ◽  
B Splines

Nonuniform rational B-splines (NURBS) finite element has advantages in analyzing the structure with curved surface geometry. In this research, we develop a component mode synthesis (CMS) based order-reduction technique which can be applied to large-scale NURBS finite element dynamic analysis. In particular, we establish a new substructure division scheme. The underlying idea is to optimally construct interface between adjacent substructures that can maximize the geometry consistency between the original structure and the divided substructures and at the meantime facilitate the compatibility conditions needed in mode synthesis. Case studies are carried out to validate the performance of the order-reduction formulation.

Selection of Degrees of Freedom for Dynamic Analysis

1987 ◽  
Vol 109 (1) ◽  
pp. 65-69 ◽  
Author(s):  
K. W. Matta
Keyword(s):  
Finite Element ◽  
Dynamic Analysis ◽  
Degrees Of Freedom ◽  
General Purpose ◽  
Component Mode Synthesis ◽  
Complex Structures ◽  
Large Complex ◽  
Static Condensation ◽  
Basis Vectors ◽  
Selection Of

A technique for the selection of dynamic degrees of freedom (DDOF) of large, complex structures for dynamic analysis is described and the formulation of Ritz basis vectors for static condensation and component mode synthesis is presented. Generally, the selection of DDOF is left to the judgment of engineers. For large, complex structures, however, a danger of poor or improper selection of DDOF exists. An improper selection may result in singularity of the eigenvalue problem, or in missing some of the lower frequencies. This technique can be used to select the DDOF to reduce the size of large eigenproblems and to select the DDOF to eliminate the singularities of the assembled eigenproblem of component mode synthesis. The execution of this technique is discussed in this paper. Examples are given for using this technique in conjunction with a general purpose finite element computer program GENSAM[1].

Analysis of vibration levels of large structural system with recursive component mode synthesis method: Theory and convergence

2006 ◽  
Vol 220 (9) ◽  
pp. 1339-1345 ◽  
Author(s):  
C W Kim
Keyword(s):  
Finite Element ◽  
Eigenvalue Problem ◽  
Large Scale ◽  
Synthesis Method ◽  
Lanczos Method ◽  
Component Mode Synthesis ◽  
Structural System ◽  
Accurate Analysis ◽  
Satisfactory Performance ◽  
And Performance

The component mode synthesis (CMS) method has been extensively used in industries. However, industry finite-element (FE) models need a more efficient CMS method for satisfactory performance since the size of FE models needs to be increased for a more accurate analysis. Recently, the recursive component mode synthesis (RCMS) method was introduced to solve large-scale eigenvalue problem efficiently. This article focuses on the convergence of the RCMS method with respect to different parameters, and evaluates the accuracy and performance compared with the Lanczos method.

The Finite Element Analysis and Comparison of Wind Turbine Tower under Static Wind Load and Fluctuating Wind Load

2013 ◽  
Vol 446-447 ◽  
pp. 733-737
Author(s):  
Chi Chen ◽  
Hao Yuan Chen ◽  
Tian Lu
Keyword(s):  
Finite Element ◽  
Dynamic Analysis ◽  
Wind Turbine ◽  
Modal Analysis ◽  
Large Scale ◽  
Wind Load ◽  
Element Analysis ◽  
Finite Element Software ◽  
Wind Turbine Tower ◽  
Fluctuating Wind

In this paper, a 1.5 MW wind turbine tower in Dali, Yunnan Province is used as the research object, using large-scale finite element software Ansys to carry on the dynamic analysis. These natural frequencies and natural vibration type of the first five of tower are obtained by modal analysis and are compared with natural frequency to determine whether the resonance occurs. Based on the modal analysis, the results of the transient dynamic analysis are obtained from the tower, which is loaded by the static wind load and fluctuating wind load in two different forms. By comparing the different results, it provides the basis for the dynamic design of wind turbine tower.

The Application of Finite Element Methods to the Dynamic Analysis of Flexible Spatial and Co-Planar Linkage Systems

1981 ◽  
Vol 103 (3) ◽  
pp. 643-651 ◽  
Author(s):  
W. Sunada ◽  
S. Dubowsky
Keyword(s):  
Finite Element ◽  
Dynamic Analysis ◽  
High Speed ◽  
Robotic Manipulators ◽  
Component Mode Synthesis ◽  
System Of Equations ◽  
Flexible Links ◽  
Analysis Techniques ◽  
Spatial Mechanisms ◽  
Coordinate Reduction

An analytical method is presented for the dynamics of spatial mechanisms containing complex-shaped, flexible links with application to both high-speed industrial machines and robotic manipulators. Existing NASTRAN-type finite element structural analysis programs are combined with 4 × 4 matrix dynamic analysis techniques and Component Mode Synthesis coordinate reduction to yield a procedure capable of analyzing complex, non-linear spatial mechanisms with irregularly shaped links in great detail, yet producing a system of equations small enough for efficient numerical integration. The method is applied to two examples.

scholarly journals A New MEMS Stochastic Model Order Reduction Method: Research and Application

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Bian Xiangjuan ◽  
Youping Gong ◽  
Chen Guojin ◽  
Lv Yunpeng
Keyword(s):  
Finite Element ◽  
Stochastic Model ◽  
Model Order Reduction ◽  
Large Scale ◽  
Reduction Method ◽  
Order Reduction ◽  
Fast Time ◽  
Random Input ◽  
Model Order ◽  
Order Reduction Method

Modeling and simulation of MEMS devices is a very complex tasks which involve the electrical, mechanical, fluidic, and thermal domains, and there are still some uncertainties that need to be accounted for during the robust design of MEMS actuators caused by uncertain material and/or geometric parameters. According to these problems, we put forward stochastic model order reduction method under random input conditions to facilitate fast time and frequency domain analyses; the method makes use of polynomial chaos expansions in terms of the random input variables for the matrices of a finite element model of the system and then uses its transformation matrix to reduce the model; the method is independent of the MOR algorithm, so it is seamlessly compatible with MOR method used in popular finite element solvers. The simulation results verify the method is effective in large scale MEMS design process.

scholarly journals Behavioral Analysis of Various Techniques of Model Order Reduction Used in the Reduction of Large Scale Control System

2019 ◽  
Vol 9 (2) ◽  
pp. 4894-4899
Keyword(s):  
Transfer Function ◽  
Model Order Reduction ◽  
Large Scale ◽  
Transfer Functions ◽  
Order Approximation ◽  
Order Reduction ◽  
Higher Order ◽  
Reduction Technique ◽  
Order System ◽  
Model Order

It is very important task to study the behavior of the processes occurring in the industry. To attain this task, the knowledge of the transfer function of the system should be there. When working in robust environment, these transfer functions becomes so tedious that it becomes very difficult to obtain these transfer functions and hence affects the study of the behavior of these system. Due to this, the requirement for reduction of these transfer function becomes a necessity to analyze the behavior of foresaid systems and it becomes easy to do the desired modifications in the system i.e addition of any feature, desired changes in the behavior etc., furthermore the thing to be kept in consideration while doing the reduction in transfer function that the behavior viz. peak overshoot, settling time, steady state error of the two systems (reduced and the original system) should be approximately same, so it is prime importance that the applied model order reduction technique should provide a more accurate approximation of original higher order system. The paper presents here the different categories of model order reduction techniques that can be applied to achieve the motto of model order reduction of higher order systems. The techniques presented are categorized into the four different categories to understand them and their merits and demerits and these will help in proper selection of the model order reduction technique to obtain the most accurate reduced order approximation of large scale system.

A Meta-Modeling Procedure for Updating the Finite Element Model of an Arch Bridge Model

2013 ◽  
Vol 540 ◽  
pp. 79-86
Author(s):  
De Jun Wang ◽  
Yang Liu
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Dynamic Analysis ◽  
Large Scale ◽  
Model Updating ◽  
Element Model ◽  
Fe Model ◽  
Large Scale Structures ◽  
Bridge Model ◽  
Scale Structures

Finite element (FE) model updating of structures using vibration test data has received considerable attentions in recent years due to its crucial role in fields ranging from establishing a reality-consistent structural model for dynamic analysis and control, to providing baseline model for damage identification in structural health monitoring. Model updating is to correct the analytical finite element model using test data to produce a refined one that better predict the dynamic behavior of structure. However, for real complex structures, conventional updating methods is difficult to be utilized to update the FE model of structures due to the heavy computational burden for the dynamic analysis. Meta-model is an effective surrogate model for dynamic analysis of large-scale structures. An updating method based on the combination between meta-model and component mode synthesis (CMS) is proposed to improve the efficiency of model updating of large-scale structures. The effectiveness of the proposed method is then validated by updating a scaled suspender arch bridge model using the simulated data.

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