Artificial neural networks for thermopiezoelectric systems

Mehmet Sunar*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

A radial basis function-artificial neural network modelling of thermopiezoelectric systems is presented. The neural network model can emulate the electrical response of two thermopiezoelectric layers bonded on a cantilever beam structure. The electrical outputs of thermopiezoelectric layers are due to sudden changes in temperature of thermo piezoelectric/beam system and vertical step force at the free end of the beam. The neural network is trained so that it mimics the electrical response of the system for different thermopiezoelectric layer locations. The test results of neural network are shown together with the actual system results to illustrate the accuracy of the network in predicting the thermopiezoelectric system behaviour to temperature and force effects.

Original languageEnglish
Pages (from-to)1123-1131
Number of pages9
JournalInternational Journal of Energy Research
Volume23
Issue number13
DOIs
StatePublished - 25 Oct 1999

Keywords

  • Artificial neural network
  • Energy functional
  • Finite element method
  • Generalized heat conduction
  • Radial basis function
  • Thermopiezoelectric

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Nuclear Energy and Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology

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