Machine learning based approach for the prediction of flow boiling/condensation heat transfer performance in mini channels with serrated fins

Guangya Zhu, Tao Wen, Dalin Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)

Abstract

Two phase flow boiling and condensation heat transfer in mini channels are promising technologies in handling the challenges of modern thermal management in various fields. In this paper, experimental systems were developed to generate data for flow boiling and condensation of refrigerant R134a in two mini channels with serrated fins under various working conditions. Based on these data, machine learning based Artificial Neural Network (ANN) models were trained to predict the heat transfer performance for both flow boiling and condensation. The proposed ANN models predict the heat transfer coefficient satisfactorily with a Mean Absolute Relative Deviation (MARD) of 11.41% and 6.06% for boiling and condensation respectively compared with experimental data. The test results also indicate that using the most important parameters rather than all available parameters is able to formulate a reasonable ANN model. It also reveals that the developed ANN model can be transferred in a different dataset from the same domains but shows an unacceptable performance for different domains. The proposed ANN model can illuminate future study on two phase flow and be used in practical heat exchanger design, which will benefit the advanced thermal management system.

Original languageEnglish
Article number120783
JournalInternational Journal of Heat and Mass Transfer
Volume166
DOIs
Publication statusPublished - Feb 2021

Keywords

  • Artificial Neural Network (ANN)
  • Flow boiling
  • Flow condensation
  • Influential factor analysis
  • Machine learning
  • Mini channel with serrated fins

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes

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