Training based, moving digital filter method for real time heat flux function estimation

Farshad Kowsary, Morteza Mohammadzaheri, Saeed Irano

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

In this paper the neural networks is utilized to estimate the "filter coefficients" needed to estimate heat flux in a particular system. In developing the training phase of the network inspiration is drawn from the Burgraff's exact solution of the IHCP as well as the filter method. Thus, the estimation phase neither requires any temperature field nor the sensitivity coefficients calculations. The neural network used in this work is a 2-layer perceptron. It is shown via classical triangular heat flux test cases that the method can yield very accurate, very efficient as well as stable estimations.

Original languageEnglish
Pages (from-to)1291-1298
Number of pages8
JournalInternational Communications in Heat and Mass Transfer
Volume33
Issue number10
DOIs
Publication statusPublished - Dec 2006

Keywords

  • Artificial neural networks
  • Heat flux estimation
  • Inverse heat conduction

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Chemical Engineering(all)
  • Condensed Matter Physics

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