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

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. </jats:p

Prediction of Particle Damping Parameters Using RBF Neural Network

AbstractParticle damping is one of the recent passive damping methods used for effective vibration suppression. This paper discusses two different Artificial Neural Networks - Feed Forward Back Propagation Network and Radial Basis Function - applied to determine the relationship between the damping ratio and system parameters based on extensive experiments carried out on an aluminium alloy beam. The experiments are carried out with different combinations of system parameters for the estimation of damping ratio. Based on the Neural Network predictions, the factors which affect the damping performances are studied in detail for the given combination of system parameters

FIRST LASER RANGING RESULTS FROM THE LUNAR RECONNAISSANCE ORBITER TO THE MINIATURE LASER RETROREFLECTOR ARRAY ON CHANDRAYAAN-3

5th Lunar and Planetary Science Conference (LPSC), Woodlands, Texas and virtually, March 11–15, 2024A laser range measurement from the Lunar Reconnaissance Orbiter (LRO) to the Chandrayaan-3 lunar lander has been accomplished. The Lunar Orbiter Laser Altimeter (LOLA) on LRO has successfully detected the signal reflected by the miniaturelaser retroreflector array (LRA) on Chandrayaan-3 of the Indian Space Research Organisation (ISRO).https://www.hou.usra.edu/meetings/lpsc2024/pdf/2259.pd