eprintid: 879
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/08/79
datestamp: 2023-11-09 15:49:01
lastmod: 2023-11-09 15:49:01
status_changed: 2023-11-09 15:38:38
type: conference_item
metadata_visibility: show
creators_name: Hidayat, M.I.P.
creators_name: Ariwahjoedi, B.
title: Radial basis function neural networks for velocity-field reconstruction in fluid-structure interaction problem
ispublished: pub
keywords: Analytical solutions; Basis functions; Compressible fluids; Elastic solids; Eulerian-Lagrangian Riemann problem; Fluid-solid interfaces; Fluid-structure interaction problem; Gaussian basis functions; Gaussians; MLP; MLP model; Model accuracy; Multi-quadric and inverse multi-quadric basis functions; Numerical simulation; Radial basis function neural networks; Simulation result; Step velocity; Velocity field; Velocity-field reconstruction, Computer simulation; Fluid structure interaction; Fluids; Gaussian distribution; Industrial electronics; Neural networks; Velocity; Wave effects, Radial basis function networks
note: cited By 1; Conference of 2010 International Conference on Computer Applications and Industrial Electronics, ICCAIE 2010 ; Conference Date: 5 December 2010 Through 7 December 2010; Conference Code:84532
abstract: 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. © 2010 IEEE.
date: 2010
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-79953837838&doi=10.1109%2fICCAIE.2010.5735133&partnerID=40&md5=a9325379f1581713b2475690bb35852b
id_number: 10.1109/ICCAIE.2010.5735133
full_text_status: none
publication: ICCAIE 2010 - 2010 International Conference on Computer Applications and Industrial Electronics
place_of_pub: Kuala Lumpur
pagerange: 506-510
refereed: TRUE
isbn: 9781424490554
citation:   Hidayat, M.I.P. and Ariwahjoedi, B.  (2010) Radial basis function neural networks for velocity-field reconstruction in fluid-structure interaction problem.  In: UNSPECIFIED.