Application of NARX neural networks in thermal dynamics identification of a pulsating heat pipe

Ya Wei Lee, Tien Li Chang

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

The pulsating heat pipe (PHP) receiving much attention in industries is a novel type of cooling device. The distinguishing feature of PHPs is the unsteady flow oscillations formed by the passing non-uniform distributions of vapour plugs and liquid slugs. This study introduces a methodology of a non-linear auto-regressive with exogenous (NARX) neural network to analyze the thermal dynamics of a PHP in both the time and frequency domains. Three heating powers: 30, 70, and 110 W are tested, and all the predicted results are presented in quite good agreement with the measured results. Herein, the harmonic analysis of the non-linear structure can be equivalently conducted with generalized frequency response functions (GFRFs). Based on the non-linear coupling between the various input spectral components, the interpretations of the higher order GFRFs have been extensively presented for demonstrating the non-linear effects on the heat transfer of a PHP at different operating conditions. Crown

Original languageEnglish
Pages (from-to)1069-1078
Number of pages10
JournalEnergy Conversion and Management
Volume50
Issue number4
DOIs
Publication statusPublished - 2009 Apr 1
Externally publishedYes

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Keywords

  • Generalized frequency response functions
  • Heat transfer
  • NARX
  • Pulsating heat pipe
  • Thermal dynamics

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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