TY - GEN
T1 - Radial basis function neural networks for velocity-field reconstruction in fluid-structure interaction problem
AU - Hidayat, Mas Irfan P.
AU - Ariwahjoedi, Bambang
PY - 2010
Y1 - 2010
N2 - We report the utilization of radial basis function neural networks (RBFNN) with multi-quadric (MQ) and inverse multi-quadric (EVIQ) basis functions for numerical simulation of velocity-field reconstruction in fluid-structure interaction (FSI) problem with the presence of a very step velocity jump at the fluid-solid interface. The NN models were developed and utilized as approaches of investigation to fully reconstruct the velocity-field at the fluid-solid interface. One-dimensional compressible fluid coupled with elastic solid under strong impact, which belongs to an Eulerian-Lagrangian Riemann problem, was simulated. When the resolution in the vicinity of the interface was further investigated and analyzed, the RBFNN-EVIQ models have shown better performance than the RBFNN-MQ and the RBFNN with Gaussian basis function, in which the RBFNN with Gaussian basis function has been previously shown to produce better accuracy compared to the MLP model for the problem considered. Meanwhile, the RBFNN with Gaussian basis function models were better than the RBFNN-MQ models for the problem considered. The NN model accuracies were validated to the problem analytical solution and the simulation results were further presented and discussed.
AB - We report the utilization of radial basis function neural networks (RBFNN) with multi-quadric (MQ) and inverse multi-quadric (EVIQ) basis functions for numerical simulation of velocity-field reconstruction in fluid-structure interaction (FSI) problem with the presence of a very step velocity jump at the fluid-solid interface. The NN models were developed and utilized as approaches of investigation to fully reconstruct the velocity-field at the fluid-solid interface. One-dimensional compressible fluid coupled with elastic solid under strong impact, which belongs to an Eulerian-Lagrangian Riemann problem, was simulated. When the resolution in the vicinity of the interface was further investigated and analyzed, the RBFNN-EVIQ models have shown better performance than the RBFNN-MQ and the RBFNN with Gaussian basis function, in which the RBFNN with Gaussian basis function has been previously shown to produce better accuracy compared to the MLP model for the problem considered. Meanwhile, the RBFNN with Gaussian basis function models were better than the RBFNN-MQ models for the problem considered. The NN model accuracies were validated to the problem analytical solution and the simulation results were further presented and discussed.
KW - Fluid-structure interaction
KW - Gaussian
KW - MLP
KW - Multi-quadric and inverse multi-quadric basis functions
KW - Velocity-field reconstruction
UR - http://www.scopus.com/inward/record.url?scp=79953837838&partnerID=8YFLogxK
U2 - 10.1109/ICCAIE.2010.5735133
DO - 10.1109/ICCAIE.2010.5735133
M3 - Conference contribution
AN - SCOPUS:79953837838
SN - 9781424490554
T3 - ICCAIE 2010 - 2010 International Conference on Computer Applications and Industrial Electronics
SP - 506
EP - 510
BT - ICCAIE 2010 - 2010 International Conference on Computer Applications and Industrial Electronics
T2 - 2010 International Conference on Computer Applications and Industrial Electronics, ICCAIE 2010
Y2 - 5 December 2010 through 7 December 2010
ER -