scholarly journals Finite Element Analysis of Sound Transmission Loss in One-Dimensional Solids

2013 ◽  
Vol 03 (04) ◽  
pp. 110-119
Author(s):  
S. D. Yu ◽  
J. G. Kawall
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Transmission Loss ◽  
Sound Transmission ◽  
Sound Transmission Loss ◽  
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.

Analytical and Experimental Investigation on Sound Transmission of Double Thin Plates with Magnetic Negative Stiffness

2018 ◽  
Vol 10 (05) ◽  
pp. 1850054 ◽  
Author(s):  
Akintoye Olumide Oyelade ◽  
Yi Chen ◽  
Ruojun Zhang ◽  
Gengkai Hu
Keyword(s):  
Finite Element ◽  
Transmission Loss ◽  
Sound Transmission ◽  
Interaction Force ◽  
Negative Stiffness ◽  
Thin Plates ◽  
Acoustic Metamaterials ◽  
Element Analysis ◽  
Theoretical Formulation ◽  
Wave Condition

Transmission loss of acoustic metamaterials (AM) made of double thin plates with magnetic (negative) stiffness was analyzed using theory, finite element analysis and experimental techniques. The theoretical formulation was done using a rectangular duct below the first cut off frequency, the model is then validated against finite element method and experiment. Two cubic magnets were used, their interaction force and the resulted magnetic stiffness were calculated. The sound transmission loss (STL) of the structure is calculated for plane wave condition, the addition of magnetic mass shifts STL peaks to the lower frequency compared to a structure without mass. The slight increase in STL for small negative stiffness in experiment is not enough to cancel the effect of air compressibility. However, a significant enhancement could be expected if negative stiffness can be made large enough in the double thin plates. The developed AM can be employed as a prospective sound engineering control at low frequency.

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