Thermal Resistance Modeling of Oscillating Heat Pipes for Nanofluids by Artificial Intelligence Approach

Mohammad Malekan, Ali Khosravi, Hamid Reza Goshayeshi, Mamdouh El Haj Assad, Juan Jose Garcia Pabon

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)

Abstract

In this study, thermal resistance of a closed-loop OHP is investigated using experimental tests and artificial intelligence methods. For this target, γFe2O3 and Fe3O4 nanoparticles are mixed with the base fluid. Also, intelligent models are developed to predict the thermal resistance of the OHP. These models are developed based on the heat input into evaporator section, the thermal conductivity of working fluids, and the ratio of the inner diameter to length of OHP. The intelligent methods are multilayer feed-forward neural network (MLFFNN), adaptive neuro-fuzzy inference system (ANFIS) and group method of data handling (GMDH) type neural network. Thermal resistance of the heat pipe (as a measure of thermal performance) is considered as the target. The results presented that using the nanofluids as working fluid in the OHP can decrease the thermal resistance, where this decreasing for Fe3O4/water nanofluid is more than γFe2O3/water. Also, the intelligent models successfully predicted the thermal resistance of OHP with a correlation coefficient close to 1. The root mean square error (RMSE) for MLFFNN, ANFIS, and GMDH models was obtained as 0.0508, 0.0556, and 0.0569 (℃/W) (for the test data), respectively.
Original languageEnglish
Article number072402
Number of pages12
JournalJOURNAL OF HEAT TRANSFER: TRANSACTIONS OF THE ASME
Volume141
Issue number7
DOIs
Publication statusPublished - 1 Jul 2019
MoE publication typeA1 Journal article-refereed

Keywords

  • Oscillating heat pipe
  • Nanofluids
  • adaptive neuro-fuzzy inference system (ANFIS)
  • Group method of data handling
  • Multilayer feed-forward neural network
  • oscillating heat pipe
  • group method of data handling
  • nanofluids
  • adaptive neuro-fuzzy inference system
  • multilayer feed-forward neural network
  • MACHINE LEARNING ALGORITHMS
  • FLUID-FLOW
  • FERROFLUID
  • PREDICTION
  • TRANSPORT
  • TRANSFER PERFORMANCE
  • ENHANCEMENT
  • NEURAL-NETWORK
  • WIND-SPEED

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