A correlation development for predicting the pressure drop of various refrigerants during condensation and evaporation in horizontal smooth and micro-fin tubes
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Author list: Balcilar M., Dalkilic A.S., Agra O., Atayilmaz S.O., Wongwises S.
Publisher: Elsevier
Publication year: 2012
Journal: International Communications in Heat and Mass Transfer (0735-1933)
Volume number: 39
Issue number: 7
Start page: 937
End page: 944
Number of pages: 8
ISSN: 0735-1933
eISSN: 1879-0178
Languages: English-Great Britain (EN-GB)
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Abstract
This paper predicts the condensation and evaporation pressure drops of R32, R125, R410A, R134a, R22, R502, R507a, R32/R134a (25/75 by wt%), R407C and R12 flowing inside various horizontal smooth and micro-fin tubes by means of the numerical techniques of artificial neural networks (ANNs) and non-linear least squares (NLS). In its analyses, this paper used experimental data from the National Institute of Standards and Technology (NIST) and Eckels and Pate, as presented in Choi et al.'s study provided by NIST. In their experimental setups, the horizontal test sections have 1.587, 3.78, 3.81 and 3.97. m long countercurrent flow double tube heat exchangers with refrigerant flowing in the inner smooth (8, 8.01 and 11.1. mm i.d.) and micro-fin (4.339, 5.45, 7.43 and 8.443. mm i.d.) copper tubes and cooling water flowing in the annulus. Their test runs cover a wide range saturation temperatures, vapor qualities and mass fluxes. The pressure drops are calculated with 1485 measured data points, together with analyses of artificial neural networks and non-linear least squares numerically. Inputs of the ANNs of the best correlation are the measured values of the test sections, such as mass flux, tube length, inlet and outlet vapor qualities, critical pressure, latent heat of condensation, mass fraction of liquid and vapor phases, dynamic viscosities of liquid and vapor phases, hydraulic diameter, two-phase density and the outputs of the ANNs, which comprise the experimental total pressure drops of the evaporation and condensation data from independent laboratories. The total pressure drops of in-tube condensation and in-tube evaporation tests are modeled using the artificial neural network (ANN) method of multi-layer perceptron (MLP) with 12-40-1 architecture. Its average error rate is 7.085%, which came from the cross validation tests of 1485 evaporation and condensation data points. Dependency of the output of the ANNs from 12 numbers of input values is also shown in detail, and new ANN based empirical pressure drop correlations are developed separately for the conditions of condensation and evaporation in smooth and micro-fin tubes as a result of the analyses. In addition, a single empirical correlation for the determination of both evaporation and condensation pressure drops in smooth and micro-fin tubes is proposed with an error rate of 14.556%. ฉ 2012 Elsevier Ltd.
Keywords
Artificial neural network (ANN), Correlation development, Micro-fin, Multi-layer perceptron (MLP), Non-linear least squares (NLS)
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