A non-obstructive particle damping model of DEM

Li, Hu; Huang, Qibai; Liu, Zhanxin

doi:10.1007/s10999-007-9053-z

Cited by 25 publications

(14 citation statements)

References 15 publications

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“…A similar observation is seen for all the other modes but is not shown here for brevity. Hence, it can be concluded from Figure 19(c) that the damping performance is better at higher particle densities for all the bending modes, which agrees with the reported literature (Panossian, 1991;Xu et al, 2004a,c;Hu et al, 2007) and indicates that the energy dissipation increases with increases in particle density. This has been validated in both predictions from RBF network and experimental investigation.…”

Section: Veeramuthuvel Et Alsupporting

confidence: 89%

“…The DEM technique was used by many researchers to carry out studies of particle dampers (Saluena et al, 1999;Friend and Kinra, 2000;Chen et al, 2001;Mao et al, 2003Mao et al, , 2004. A DEM was used to model particle damping by accounting for the effects of particle size, friction between two particles, and number of particles (Hu et al, 2007). It has been observed that the damping performance depends on mass ratio and material density.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Veeramuthuvel Et Alsupporting

confidence: 89%

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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“…The vibration energy of the main structure is dissipated by collisions between particles and container wall and between particles. It has been widely investigated and used in the aviation and mechanic area, but relevant research in civil engineering is just in the early stage and is only limited in the earthquake‐induced vibration control area . Research and engineering practice show that the damping performance of particle damper under seismic load is favorable.…”

Section: Introductionmentioning

confidence: 99%

Experimental parametric study on wind-induced vibration control of particle tuned mass damper on a benchmark high-rise building

Wang

Zhou

2017

Struct Design Tall Spec Build

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Summary A particle tuned mass damper system is an integration of tuned mass damper and particle damper. The damping performance of such device is investigated by an aero‐elastic wind tunnel test on a benchmark high‐rise building. The robustness of the system is studied by comparing the damping performance to that of a traditional tuned mass damper, and the results show that the damper has excellent and steady wind‐induced vibration control effects. Meanwhile, the parameters (filling ratio, mass ratio, and mass ratio of the container to particles), which have great influence on the vibration reduction performance of the system, are also analyzed, and it is found that the particles filling ratio plays the most important role in deciding the damping effects of the dampers. There exists an optimum filling ratio and mass ratios in which the damper can reach the best damping state. Proper parameter selections can greatly improve the damping performance.

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“…Particle dampers are passive control systems that absorb the incoming energy through impact of particles with each other and with the walls of the container resulting in exchange of momentum between the particles and the primary system. Such devices have been used for years to reduce the vibrations of machines and structures [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. Papalou et al [14][15][16][17] reported that a properly designed particle damper replacing one of the top drums of a classical multi-drum column without capital can achieve substantial reduction of the motion.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Particle Damper’s Position on the Dynamic Response of Classical Columns

Papalou

2017

Period. Polytech. Civil Eng.

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A non-obstructive particle damping model of DEM

Cited by 25 publications

References 15 publications

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

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

Experimental parametric study on wind-induced vibration control of particle tuned mass damper on a benchmark high-rise building

The Effect of Particle Damper’s Position on the Dynamic Response of Classical Columns

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