Prediction of pressure drop during evaporation of R407C in horizontal tubes using artificial neural networks

This work presents a discussion on pressure drop of R-407C during two-phase flows, and the application of artificial neural network (ANN) to predict these pressure drops in a smooth copper tube, for 4.5 mm and 8.0 mm inner diameter. The ANN was trained using data from 127 experiments encountered in...

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Autores:
Garcia, Juan Jose
Garcia, Franklin
Bermúdez, José
Machado, Luiz
Tipo de recurso:
Article of journal
Fecha de publicación:
2018
Institución:
Universidad Francisco de Paula Santander
Repositorio:
Repositorio Digital UFPS
Idioma:
eng
OAI Identifier:
oai:repositorio.ufps.edu.co:ufps/1349
Acceso en línea:
http://repositorio.ufps.edu.co/handle/ufps/1349
https://doi.org/10.1016/j.ijrefrig.2017.10.007
Palabra clave:
R407C
Evaporation
Artificial network neural
Pressure drop
Smooth horizontal tubes
Rights
openAccess
License
© 2017 Elsevier Ltd and IIR. All rights reserved.
Description
Summary:This work presents a discussion on pressure drop of R-407C during two-phase flows, and the application of artificial neural network (ANN) to predict these pressure drops in a smooth copper tube, for 4.5 mm and 8.0 mm inner diameter. The ANN was trained using data from 127 experiments encountered in the literature. Diameter, mass flux, saturation pressure and local vapor quality were used as inputs, whereas the pressure drop was considered as output. The number of neurons and hidden layers were determined based on the accuracy of results. The trained ANN was able to estimate the experimental data with a MAPE (Mean Absolute Percentage Error) of 6.11%, and a correlation coefficient (R) of 0.999 for all data, using a configuration with 14 neurons in the hidden layer. The obtained results were within ±10% for 90% of all data, and ±30% for 99% of all data. Compared to the well established literature correlations for pressure drop, the ANN demonstrates how important this tool is to predict pressure drop accurately.