A Comparison of the Effectiveness of Elastomeric Tuned Mass Dampers and Particle Dampers

Aubert, Allan C.; Green, Edward R.; Chen, Gregory Z.

doi:10.4271/2003-01-1419

Cited by 10 publications

(4 citation statements)

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“…In this it is seen that particle damping is effective over a range of excitation, but it has poor control if excitation is too high or too low. [12] Yanchen Du done experimental setup on spring-supported fine particle impact damper in which they integrates the effects of elastic deformation and plastic deformation on cantilever beam. In this experiment they found that particle impact damper reduces 80% of the maximum amplitude of cantilever beam.…”

Section: IImentioning

confidence: 99%

Effect of Cavity Size on Performance of Particle Damper

Huddar¹,

Katekar²,

Aradhye³

2023

IJRASET

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Particle damping is a damping technique that has found wide applications in engineering systems. It is a form of damping where the energy dissipation is achieved through the motion of small particles within a cavity. This technique has several unique features that make it an attractive option for many engineering applications. Cantilever beam with FFT analyzer is used to find the response parameters. The readings are taken with varying cavity dimensions, size of ball, packing ratio and ball material. The readings in the form of acceleration are noted by varying one experimental parameter. From the experimentation it is found that 24 mm diameter and height 37 mm cavity with 50% Packing ratio is effective for the applied experimental set-up.

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Section: IImentioning

confidence: 99%

Effect of Cavity Size on Performance of Particle Damper

Huddar¹,

Katekar²,

Aradhye³

2023

IJRASET

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“…In literature comparisons of different damping devices with a PD can be found. A TMD was compared to a PD for a SDOFS in [16]. A well structured detailed analysis for various excitation methods was performed in [17].…”

Section: Introductionmentioning

confidence: 99%

Broadband Damping Properties of Particle Dampers Mounted to Dynamic Structures

2022

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Background The background of this work is the classification of the broadband properties of particle dampers (PDs). This broadband characteristic has experienced little systematic investigation in experiments. Objective So the primary objective of this paper is to find a measure to quantify the broadband damping properties of PDs. Also the demonstration of applicability to technical structures is a desired goal and the experiments provide a sound basis. Methods The methods for evaluating the performance of particle dampers and tuned mass dampers target the reduction of vibration amplitudes over the frequency range. The test bench consists of a mechanical frame structure with multiple eigenfreqencies up to 200 Hz harmonically excited with an electrodynamic shaker. From the differences in the dynamic behaviour the performance metric will be derived and evaluated. Results As a result, a dynamic structure is set up as an effective test bench for different damper configurations. Differences of the tested concepts in regard to the dynamic behaviour over a wide frequency range are observed. From the experimental data a performance metric is deduced to quantify these differences. Conclusion The conclusions drawn from this paper are, that PDs provide high damping over a wide frequency range. Furthermore, with a suitable performance metric this broadband damping properties can be quantified for the use in further development of PDs.

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“…An elementary analytical model of PID was also developed. The performance comparison between particle dampers and tuned mass dampers was studied by Aubert et al (2003) for an equal mass of dampers. The tuned mass damper was effective in suppressing the vibrations at a particular resonance frequency, whereas particle dampers were better over a wide frequency range.…”

Section: Introductionmentioning

confidence: 99%

Application of RBF neural network in prediction of particle damping parameters from experimental data

Veeramuthuvel

Shankar

Sairajan

2016

Journal of Vibration and Control

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Particle damping is one of the recent passive damping methods and its relevance in space structural applications is increasing. This paper presents the novel application of a radial basis function (RBF) neural network to accurately predict the modal damping ratio of a particle damping system using system input parameters such as particle size, particle density, packing ratio, and their effect at different modes of vibration. The prediction of particle damping using the RBF neural network is studied in comparison with the back propagation neural (BPN) network on an aluminum alloy beam structure with extensive experimental tests. The prediction accuracy of the RBF neural network is significant with 9.83% error compared to 12.22% obtained by the BPN network for a best case. Limited experiments were also carried out on a mild steel beam to study and compare the trends predicted in earlier studies. The relationships obtained by the proposed method readily provide useful guidelines in the design of particle dampers for space applications. The RBF neural network provides superior accuracy with reduced computational effort.

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A Comparison of the Effectiveness of Elastomeric Tuned Mass Dampers and Particle Dampers

Cited by 10 publications

References 0 publications

Effect of Cavity Size on Performance of Particle Damper

Effect of Cavity Size on Performance of Particle Damper

Broadband Damping Properties of Particle Dampers Mounted to Dynamic Structures

Application of RBF neural network in prediction of particle damping parameters from experimental data

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