期刊
INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID
卷 85, 期 -, 页码 292-302出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ijrefrig.2017.10.007
关键词
R407C; Evaporation; Artificial network neural; Pressure drop; Smooth horizontal tubes
资金
- CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico)
- Capes (Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior)
- Fapemig (Fundacao de Amparo a Pesquisa do Estado de Minas Gerais) project [APQ-00188-14/2016]
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 similar to 10% for 90% of all data, and similar to 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. (C) 2017 Elsevier Ltd and IIR. All rights reserved.
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