scholarly journals Correlation analysis of PCB and comparison of test-analysis model reduction methods

2014 ◽  
Vol 27 (4) ◽  
pp. 835-845 ◽  
Author(s):  
Fei Xu ◽  
Chuanri Li ◽  
Tongmin Jiang ◽  
Shuanglong Rong
Keyword(s):  
Correlation Analysis ◽  
Model Reduction ◽  
Analysis Model ◽  
Test Analysis ◽  
Reduction Methods

Absorption, Wicking and Drying Characteristics of Compression Garments

2015 ◽  
Vol 10 (3) ◽  
pp. 155892501501000 ◽  
Author(s):  
Canan Saricam
Keyword(s):  
Correlation Analysis ◽  
T Test ◽  
Test Analysis ◽  
Drying Characteristics ◽  
Compression Garments ◽  
Production Parameters ◽  
The Relationship

This paper discusses the influence of the production parameters on the moisture related comfort characteristics of the compression garments that differ according to the tension applied during the production and elastane count. Correlation analysis, two sided independent t-test analysis and ANOVA tests were applied to analyze the relationship between the production parameters and comfort characteristics which are absorption, vertical and transfer wicking and drying. It was found that tension and elastane composition affect the comfort characteristics by changing the porosity, thickness and the pathways within the fabric.

scholarly journals Implementing model reduction to the JuliaFEM platform

2018 ◽  
Vol 51 (1) ◽  
pp. 36-54 ◽  
Author(s):  
Marja Liisa Rapo ◽  
Jukka Aho ◽  
Hannu Koivurova ◽  
Tero Frondelius
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Model Reduction ◽  
Open Source ◽  
Degrees Of Freedom ◽  
Mass Matrices ◽  
Dynamic Condensation ◽  
Dynamic Analyses ◽  
Reduction Methods ◽  
Large Model

JuliaFEM is an open source finite element method solver written in the Julia language. This paper presents an implementation of two common model reduction methods: the Guyan reduction and the Craig-Bampton method. The goal was to implement these algorithms to the JuliaFEM platform and demonstrate that the code works correctly. This paper first describes the JuliaFEM concept briefly after which it presents the theory of model reduction, and finally, it demonstrates the implemented functions in an example model. This paper includes instructions for using the implemented algorithms, and reference the code itself in GitHub. The reduced stiness and mass matrices give the same results in both static and dynamic analyses as the original matrices, which proves that the code works correctly. The code runs smoothly on relatively large model of 12.6 million degrees of freedom. In future, damping could be included in the dynamic condensation now that it has been shown to work.

An effective method for damage assessment based on limited measured locations in skeletal structures

2020 ◽  
Vol 24 (1) ◽  
pp. 183-195 ◽  
Author(s):  
Parsa Ghannadi ◽  
Seyed Sina Kourehli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Degrees Of Freedom ◽  
Truss Structures ◽  
Mode Shapes ◽  
Analysis Model ◽  
Test Analysis ◽  
Modal Test ◽  
Artificial Neural

This article proposes a new damage detection method using Modal Test Analysis Model and artificial neural networks. A challenge in damage detection problems is lack of measured degrees of freedom, as well as limitations of attached sensors. Modal Test Analysis Model has been used in order to estimate unmeasured degrees of freedom. An experimental cantilever beam was used to show Modal Test Analysis Model’s efficiency in estimation of unmeasured mode shapes. To solve the inverse problem of damage detection, mode shapes estimated by Modal Test Analysis Model were used as inputs, and characteristics of the damage served as outputs of the artificial neural network. The sensitivity analysis carried out for each example showing the performance of artificial neural network after mode shape expansion was efficiently improved. Three numerical examples for plane and space truss structures are considered, in order to verify effectiveness of the proposed method. Results demonstrate a high accuracy of Modal Test Analysis Model and artificial neural network for structural damage detection.

Model Reduction Methods for Magnetic Fields Based on Modal Analysis

2014 ◽  
Vol 50 (11) ◽  
pp. 1-4 ◽  
Author(s):  
Michael Kirschneck ◽  
Daniel Rixen ◽  
Henk Polinder
Keyword(s):  
Magnetic Fields ◽  
Modal Analysis ◽  
Model Reduction ◽  
Reduction Methods
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