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- MakaleAçık Erişim2024Scopus SJRQ2Investigation on the heat transfer and pressure loss of flow boiling in smooth and microfin tubes using machine learning methods
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.
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İncele - MakaleAçık Erişim2018Scopus SJRQ2Experimental analysis for heat transfer of nanofluid with wire coil turbulators in a concentric tube heat exchanger
Akyürek, Eda Feyza; Geliş, Kadir; Şahin, Bayram; Manay, Eyüphan
Nanofluids are a novel class of heat transfer suspensions of metallic or nonmetallic nanopowders with a size of less than 100 nm in base fluids and they can increase heat transfer potential of the base fluids in various applications. In the last decade, nanofluids have become an intensive research topic because of their improved thermal properties and possible heat transfer applications. For comparison, an experiment using water as the working fluid in the heat exchanger without wire coils was also performed. Turbulent forced convection heat transfer and pressure drop characteristics of Al2O3-water nanofluids in a concentric tube heat exchanger with and without wire coil turbulators were experimentally investigated in this research. Experiments effected particle volume concentrations of 0.4–0.8 to 1.2–1.6 vol% in the Reynolds number range from 4000 to 20,000. Two turbulators with the pitches of 25 mm and 39 mm were used. The average Nusselt number increased with increasing the Reynolds number and particle concentrations. Moreover, the pressure drop of the Al2O3-water nanofluid showed nearly equal to that of pure water at the same Reynolds number range. As a result, nanofluids with lower particle concentrations did not show an important influence on pressure drop change. Nonetheless, when the wire coils used in the heat exchanger, it increased pressure drop as well as the heat transfer coefficient.
AtıfScopus121WoS110OpenAlex126İncele