scholarly journals Finite element analysis of true and pseudo surface acoustic waves in one-dimensional phononic crystals

2016 ◽  
Vol 119 (2) ◽  
pp. 025308 ◽  
Author(s):  
B. Graczykowski ◽  
F. Alzina ◽  
J. Gomis-Bresco ◽  
C. M. Sotomayor Torres
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Acoustic Waves ◽  
Surface Acoustic Waves ◽  
Phononic Crystals ◽  
Element Analysis ◽  
One Dimensional

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

Finite-Element Analysis of Periodic Waveguides for Acoustic Waves

1984 ◽  
Vol 23 (S1) ◽  
pp. 139 ◽  
Author(s):  
Masanori Koshiba ◽  
Seiichi Mitobe ◽  
Michio Suzuki
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Acoustic Waves ◽  
Element Analysis
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