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Artificial neural network method to predict the boiling heat transfer coefficient of alternative refrigerants of R22 inside the multiport mini-channel tube.

Number: 1170

Author(s) : AGUSTIARINI N., HOANG H. N., OH J. T.

Summary

The prediction of heat transfer coefficient, especially on flow boiling, has been done with several analysis methods. One of them is machine learning. The ANN (Artificial Neural Network) as a subset of machine learning which a data-based model method to predict the boiling heat transfer coefficient of alternative refrigerants on replacement of R22 is introduced on this study. The experimental study of alternative refrigerants is conducted inside the multiport mini-channel tube. The mass flux ranges set up to 500 kg/m2s, heat flux ranges from 3 to 12 kW/m2, vapor quality range up to 1, and saturation temperature set to 6°C. The input parameter is consisted of dimensionless number and experimental condition, otherwise the heat transfer coefficient used as output parameter on ANN. The ANN setting parameter is taking place to predict the boiling heat transfer coefficient. Therefore, the ANN model prediction could be used to predict the boiling heat transfer coefficient of alternative refrigerant of R22.

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Pages: 5 p.

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Details

  • Original title: Artificial neural network method to predict the boiling heat transfer coefficient of alternative refrigerants of R22 inside the multiport mini-channel tube.
  • Record ID : 30029520
  • Languages: English
  • Subject: Technology
  • Source: 7th IIR International Conference on Sustainability and the Cold Chain (Online). Proceedings: April 11-13 2022
  • Publication date: 2022/04/11
  • DOI: http://dx.doi.org/10.18462/iir.iccc2022.1170
  • Document available for consultation in the library of the IIR headquarters only.

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