Sezer, Sukru; Sezer, Cihan; Celen, Ali; Bacak, Aykut; Dalkiliç, Ahmet Selim

The estimation of heat transfer coefficients (HTC) and pressure drop (Delta P) in flow boiling processes is essential for the effective design and operation of refrigeration systems. In this study, the artificial neural network (ANN), locally weighted regression (LWR), and gradient boosted machine (GBM) methods are employed to predict the boiling heat transfer coefficient (HTC) and pressure drop (Delta P\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}Δ P\end{document}) in flow boiling of R134a. The study focuses on horizontally positioned both straight and microfin tubes. The ANN, LWR, and GBM methodologies are utilized to ascertain the parameters of boiling HTC and Delta P as outputs. These parameters are determined by considering the mass flux, saturation pressure, heat flux, vapor quality, Reynolds number, Lockhart-Martinelli parameter, Froud number, Weber number, and Bond number as inputs. The training dataset is partitioned into 5 sections for the purpose of hyperparameter tweaking for each model. Out of these sections, 4 parts, consisting of approximately 111 samples, are utilized for training, while 1 part, including around 27 samples, is allocated for validation. The optimal hyperparameters are determined by calculating the average R2 score over the 5 validation sets. Using raw measurements, HTC and Delta P are successfully modeled using a relatively much smaller dataset of 174 measurements, with 82.4% R2 score and 0.7% weighted average relative deviation for HTC, and 88.9% R2 score and 4.1% weighted average relative deviation for Delta P across multiple tube types, achieved by LWR algorithm. Model performances are validated with an extrapolation test and found to be consistent with traditional train-validation-test sampling scheme with 75.9% R2 score and -6.2% weighted average relative deviation for HTC, and 89.3% R2 score and -3.9% weighted average relative deviation for Delta P, showing the consistency of the hypotheses created by a hybrid of parametric and nonparametric model families even outside the observed measurement range for multiple tube types. Local weighted regression models are the most performant, especially for limited data availability. However, calculated measurements increase error rates, suggesting that HTC and Delta P models work best with raw measurements.

Çebi, Alican; Celen, Ali; Donmez, Aydin Haci; Karakoyun, Yakup; Celen, Pinar; Cellek, Mehmet Salih; Dalkiliç, Ahmet Selim; Taner, Tolga; Wongwises, Somchai

Flow boiling researches in enhanced geometries practiced recently have been summarized in this review. Classical approximations in macroscale flows have not applied to the flows with small hydraulic diameters, so, this subject has been paid attention by the researchers. Application areas of these structures are increasing continuously due to cooling need in small confined spaces. The related studies have been reviewed in a chronological manner and their parameters have been given in a table in order for the researchers to observe the scientific duration regarding the progresses on this subject. Almost all papers on this subject have been produced after 2000, therefore, this subject is considered as an actual one to improve and worth to study in the literature. This study can not only be evaluated as the beginning argument for the researchers involved in flow boiling process in mini and microchannel, but it also consists of new works on the investigated subject.