Open AccessCORRELATIONS AND NEURAL NETWORKS AS MODELS FOR THE LOCAL HEAT TRANSFER COEFFICIENT ON CONDENSATION FOR R1234YF IN Ø 0.96MM DUCTS
Author(s) -
René Mendes,
J. G. Pabon,
Daniel Leon Ferreira Pottie,
L. V. S. Martins,
Luiz Machado
Publication year - 2021
Publication title -
engenharia térmica
Language(s) - English
Resource type - Journals
ISSN - 1676-1790
DOI - 10.5380/reterm.v20i1.80461
Subject(s) - refrigerant , heat transfer coefficient , artificial neural network , condensation , thermodynamics , heat exchanger , work (physics) , heat flux , saturation (graph theory) , materials science , environmental science , heat transfer , mechanics , computer science , mathematics , physics , artificial intelligence , combinatorics
Due to global warming considerations, the European Union has banned the use of refrigerants with a GWP greater than 150 in new passenger cars (air-conditioning systems) and 750 for fluids used in residential heat exchangers starting on January 1, 2017 (E. UNION, 2006). In this sense, the R1234yf was developed which consists of a hydrofluorolefin derived from alkenes and commercialized with the name of Opteon YF. Given the need for research related to the use of this fluid, this work has the objective of comparing the data of the local heat transfer coefficient in condensation extracted from the work of Del Col et al. (2010) for flow in a mini channel of 0.96 mm internal diameter, with mass flux of 200, 300, 400, 600, 800 and 1000 kg·(m²·s)-1 at saturation temperature of 40ºC with ten different correlations from literature as well as one neural network. It is verified that among the correlations analyzed the one which best suited the experimental data was presented by Cavallini and Zecchin (1974), with MRD, MARD, and Accuracy values equal to 5.42%, 7.81%, and 96.96%, respectively. The neural network used as a prediction model presents values of MRD, MARD, and Accuracy equals to 2.53%, 3.66%, and 100%, respectively