Complexity of band structures: Semi-analytical finite element analysis of one-dimensional surface phononic crystals

2012 ◽  
Vol 86 (10) ◽  
Author(s):  
Istvan A. Veres ◽  
Thomas Berer
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Phononic Crystals ◽  
Band Structures ◽  
Element Analysis ◽  
One Dimensional ◽  
Dimensional Surface

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 valence band structures in quantum wires

2004 ◽  
Vol 96 (4) ◽  
pp. 2055-2062 ◽  
Author(s):  
Seoung-Hwan Park ◽  
Doyeol Ahn ◽  
Yong-Tak Lee
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Valence Band ◽  
Quantum Wires ◽  
Band Structures ◽  
Element Analysis

The influence of lateral spreading on settlements beneath a fill

1994 ◽  
Vol 31 (2) ◽  
pp. 145-150
Author(s):  
C.B. Crawford ◽  
H. Jitno ◽  
P.M. Byrne
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Close Agreement ◽  
Lateral Spreading ◽  
Two Dimensional ◽  
Element Analysis ◽  
Vertical Movements ◽  
One Dimensional ◽  
Dimensional Finite Element ◽  
Earth Embankment

The discrepancy between calculated consolidation settlements and measured settlements under a 3.8 m high section of an earth embankment is investigated in this paper. A conventional one-dimensional analysis underestimated the observed settlement by 40%. A two-dimensional finite-element analysis was carried out to assess the effects of lateral spreading on vertical movements, and the results were in close agreement with the measured values. Key words : case history, consolidation, finite element analysis, settlement.

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