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Performance predictions of adiabatic flow of isobutane inside a helically coiled capillary tube: artificial neural network.

Number: 1121

Author(s) : DUBBA S. K., MENDA V. R., DHURANDHER B. K., KUMAR R.

Summary

This paper presents an artificial neural network correlation for predicting the mass flow rate of R-600a inside a helically coiled capillary tube. 480 sets of experimental measured mass flow rate data of R-600a inside straight and helically coiled capillary tube covering wide range of inlet sub-cooling degree of 3-15°C, inlet pressure 600-750 kPa, capillary geometry (capillary tube diameter: 1.12-1.52 mm and length: 2.8-4.6 m) and coil diameter of 40, 60 & 80, collected from the literature to train the neural network model. The artificial neural network model of an adiabatic straight capillary tube shows the variation from the experimental data with ±20 percentage error. In addition, the artificial neural network model of an adiabatic coiled capillary tube predicts the mass flow rate data within ±20 percentage of experimental data.

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Pages: 7

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Details

  • Original title: Performance predictions of adiabatic flow of isobutane inside a helically coiled capillary tube: artificial neural network.
  • Record ID : 30027649
  • Languages: English
  • Source: IIR Rankine Conference 2020.
  • Publication date: 2020/07/31
  • DOI: http://dx.doi.org/10.18462/iir.rankine.2020.1121

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