Comparison of inverse modelling and optimization-based methods in the heat flux estimation problem of an irradiative dryer/furnace

Ali Mirsepahi, Arash Mehdizadeh, Lei Chen, Brian O'Neill, Morteza Mohammadzaheri

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

There are two major approaches in sequential (real-time) heat flux estimation problems using measured temperatures: (i) development of inverse heat transfer models that directly estimate heat flux and (ii) use of a combination of a direct heat transfer model (which estimates temperature using heat flux information) and an optimization algorithm. In physics-based solutions, using thermodynamics and heat transfer laws, the first approach is considered ill-posed and challenging, and the second approach is more popular. However, the use of artificial intelligence (AI) techniques has recently facilitated heat transfer inverse modelling, even for complex irradiative systems. Many of the claimed advantages of AI inverse models of irradiative systems result from the use of AI techniques rather than the inverse modelling approach. This research presents a rational comparison between the aforementioned approaches for an irradiative thermal system, both using AI techniques, for the first time. The results show that inverse models are superior because of their higher accuracy and shorter estimation delay time.

Original languageEnglish
Pages (from-to)77-85
Number of pages9
JournalJournal of Computational Science
Volume19
DOIs
Publication statusPublished - Mar 1 2017

Keywords

  • Artificial neural networks
  • Intelligent techniques
  • Inverse heat transfer problems
  • Inverse radiation
  • Irradiative furnaces
  • SISO
  • Temperature control

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)
  • Modelling and Simulation

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