A general heat transfer correlation for flow condensation in single port mini and macro channels using genetic programming

doi:10.1016/j.ijrefrig.2020.06.021

International Journal of Refrigeration

Volume 119, November 2020, Pages 376-389

https://doi.org/10.1016/j.ijrefrig.2020.06.021 Get rights and content

Highlights

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Evaluated previous flow condensation heat transfer coefficient correlations.
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Proposed a new correlation for condensation heat transfer in mini and macro channels.
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Verified the model with 6521 data points from 40 published papers.
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Discussed the effects of the input parameters on the heat transfer coefficient.

Abstract

A new general explicit correlation is proposed to predict the heat transfer coefficient of fluids condensing in conventional and mini channels. The expression has been developed by correlating the Nu_mix number with Re_mix, Pr_mix, phase density ratio, P_res, We_GT, and Fr_L using genetic programming for the two-phase flow. The model has been validated with a big dataset consisting of 6521 data samples, covering a wide range of fluids used in refrigeration and heat pump industries, cross-sectional geometries (different diameters), mass fluxes, and saturation temperatures. The new generalized correlation fits the wide range of data points used with an average relative error of 17.82 %. The same database has been used to compare predictions of eight correlations available in the literature, but they failed to give a reasonable estimation of the present experimental results.

Introduction

The process of flow condensation in mini or micro channels occurs in many industries, such as cooling apparatus, air conditioning systems, power production facilities, refrigeration systems, and heat pipes. The behaviors of gravity, surface tension, inertia, and buoyancy in micro condensers differ from those of the conventional condensers that contain regular channels due to the compact geometry. This difference causes a different flow pattern and condensation heat transfer performance in micro condensers. Therefore, the models and correlations that have been developed for regular channels may not be valid for mini or micro channels. In this case, it is essential to study the heat transfer performance of flow condensation inside the mini or micro channels to enhance the performance of the micro condensers. A summary of condensation in micro channels is found in Wang and Rose (2011).

There are plenty of studies dealing with flow condensation inside and outside mini channels, and several well-known condensation heat transfer correlations have been proposed (Cavallini et al., 2003; Tao and Infante Ferreira, 2019). Although flow condensation inside mini channels has been subjected to extensive research, studies concerning the development of dimensionless models that cover a wide range of two-phase flows (especially refrigerants), cross-sectional geometries (different diameters), mass fluxes, and saturation temperatures are rather few. Zhang et al. (2015) presented a comprehensive review of the well-known available correlations and assessed their estimation accuracies based on the literature experimental data samples in horizontal channels. According to their assessment, most available correlations presented a high deviation from the experimental values. The literature information presents a non-linear trend for the condensation heat transfer coefficient in the mini channel heat exchangers. Almost all these empirical correlations (Cavallini et al., 2003; Tao and Infante Ferreira, 2019) have been developed by the well-known technique of least square fitting method (LSFM), and intelligent approaches have been rarely applied for estimating the condensation heat transfer in the mini channel heat exchangers.

With respect to the limitations of the available published correlations, accurate and reliable correlations are still needed for estimating the condensation heat transfer performance in micro condensers. In particular, a new correlation with acceptable prediction is needed to cover a wide range of measured data considering almost all dimensionless parameters. The intelligent models have shown powerful strength in non-linear modeling. Owing to the complexity of condensation flow in mini channels, intelligent models can be implemented to establish reliable predictive models that precisely describe the condensation heat transfer performance.

In this paper, a database of flow condensation heat transfer in mini/micro and macro channels is gathered from 40 published papers, which contains 6521 data points, covering a wide range of geometries, thermophysical properties, and flow parameters. The evaluation of eight well-known flow condensation heat transfer correlations is carried out using the entire dataset. The intelligent non-linear based method of the genetic algorithm has been used to develop a new general correlation for estimating the heat transfer coefficient for fluids condensing in mini and macro channels. Ultimately, the effects of the input parameters on the heat transfer coefficient are discussed by utilizing the developed predictive model.

Section snippets

Genetic programming

Managing high amounts of data and extracting information on their trends has become a challenge. The traditional techniques, such as least square fitting methods, do not perform efficiently when dealing with data involving non-linear based phenomena (Duong et al., 2018). To overcome this problem, intelligent methods inspired by natural processes have attracted researchers’ attention. Some of these well-known methods are Artificial Neural Networks, Fuzzy Logic, Support Vector Machine and

Results and discussion

In the following, empirical correlations have been obtained by applying the least square fitting method (LSFM) and GP to the collected 5809 data samples, and the accuracy of the correlations has been evaluated.

The two-phase models based on non-dimensional parameters have been developed considering the maximum number of factors influencing the HTC for fluids condensing in mini channels. Thus, a non-linear equation containing the maximum number of dimensionless parameters has been first analyzed

Conclusions

A new model is developed based on genetic programming (GP) to predict the condensation heat transfer coefficient in horizontal mini and macro channels considering a wide range of fluids, different cross-sectional geometries (different diameters), mass fluxes, and saturation temperatures. Almost 6500 experimental data from 40 different sources are gathered to enable deep analysis of flow condensation and to develop a general model capable of estimating condensation heat transfer coefficient in

Declaration of Competing Interest

There is no conflict of interest.

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A general heat transfer correlation for flow condensation in single port mini and macro channels using genetic programmingCorrélation générale de transfert de chaleur pour la condensation en écoulement dans les mini et macro-canaux à port unique en utilisant la programmation génétique

Highlights

Abstract

Introduction

Section snippets

Genetic programming

Results and discussion

Conclusions

Declaration of Competing Interest

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