Use of Artificial Neural Networks for Prediction of the Convective Heat Transfer Coefficient in Evaporative Mini-Tubes

Romero‐Méndez, Ricardo; Oviedo-Tolentino, F.; Durán-García, Héctor Martín; Pérez‐Gutiérrez, Francisco G.; Pacheco-Vega, Arturo

doi:10.1016/j.riit.2016.01.003

Cited by 14 publications

(8 citation statements)

References 16 publications

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“…Artificial neural network (ANN) is a computing system that solves problems by imitating mechanisms of the human brain. It has been applied to various engineering applications, including convective heat transfer [48][49][50][51][52] and boiling [53][54][55][56][57][58] to estimate desired performance parameters with adequate experimental data. Scalabrin et al [53,54] modeled heat transfer of flow boiling in horizontal tubes for different fluids using ANN and found that the flow boiling heat transfer model does not fundamentally depend on the flow pattern.…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional Flow Boiling Characteristics With Wettability Surface in Microgap Heat Sink and Heat Transfer Prediction Using Artificial Neural Network

Karim

Kim

2021

Journal of Heat Transfer

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As technology becomes increasingly miniaturized, thermal management becomes challenging to keep devices away from overheating due to extremely localized heat dissipation. Two-phase cooling or flow-boiling in micro-spaces utilizes the highly efficient thermal energy transport of phase change from liquid to vapor. However, the excessive consumption of liquid-phase by highly localized heat source causes the two-phase flow maldistribution, leading to a significantly reduced heat transfer coefficient, high-pressure loss, and limited flow rate. In this study, flow-boiling in a two-dimensional microgap heat sink with a hydrophilic coating is investigated with bubble morphology, heat transfer, and pressure drop for conventional (non-hydrophilic) and hydrophilic heat sinks. The experiments are carried out on a stainless steel plate, having a micro gap depth of 170 µm using deionized water at room temperature. Two different hydrophilic surfaces (partial and full channel shape) are fabricated on the heated surface to compare the thermal performance with the conventional surface. Vapor films and slugs are flushed quickly on the hydrophilic surfaces, resulting in heat transfer enhancement on the hydrophilic heat sink compared to the conventional heat sink. The channel hydrophilic heat sink shows better cooling performance and pressure stability as it provides a smooth route for the incoming water to cool the hot spot. Moreover, the artificial neural network prediction of heat transfer coefficient shows a good agreement with the experimental results as data fits within ±5% average error.

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Section: Introductionmentioning

confidence: 99%

Two-Dimensional Flow Boiling Characteristics With Wettability Surface in Microgap Heat Sink and Heat Transfer Prediction Using Artificial Neural Network

Karim

Kim

2021

Journal of Heat Transfer

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“…Several predictive models have been developed in the literature for estimating the convective heat transfer coefficient [31]. Artificial neural networks are frequently used in the creation of these models [32][33][34][35]. Another classification and estimated model method using regression is decision trees [36].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Heat Transfer Coefficient in Solar Greenhouse Type Drying Systems

Çerçi

Daş

2019

Sustainability

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As a sustainable energy source, solar energy is used in many applications. A greenhouse type dryer, which is a food drying system, directly benefits from solar energy. Convective heat transfer coefficient (hc) is an important parameter in food drying systems, in terms of system design and performance. Many parameters and equations are used to determine hc. However, as it is difficult to manually process and analyze large amounts of data and different formulations, machine learning algorithms are preferred. In this study, natural and forced convective solar greenhouse type dryers were designed. In a solar greenhouse type dryer, grape is dried in natural (GDNC) and forced convection (GDFC). For convective heat transfer coefficient (hc), predictive models were created using a multilayer perceptron (MLP)—which has many uses in drying applications, as mentioned in the literature—and decision tree (DT), which has not been used before in food drying applications. The machine learning algorithms and results of the estimated models are compared in this study. Error analyses were performed to determine the accuracy rates of the obtained models. As a result, the hc value of the dried grape product in a natural convective solar greenhouse type dryer was 11.3% higher than that of the forced type. The DT algorithm was found to be a more successful model than the MLP algorithm in estimating hc values in HDFC according to Root Mean Square Error. (RMSE = 0.0903). On the contrary, the MLP algorithm was more successful than the DT algorithm in estimating hc values in GDNC (RMSE = 0.0815).

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“…Artificial neural networks are usually used to construct these models [7][8][9][10]. Predictive models of h c values were created by using SVM regression in different topics [11,12].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Pear Drying Performance by Different Methods and Regression of Convective Heat Transfer Coefficient with Support Vector Machine

Daş

Akpınar

2018

Applied Sciences

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Abstract:In this study, an air heated solar collector (AHSC) dryer was designed to determine the drying characteristics of the pear. Flat pear slices of 10 mm thickness were used in the experiments. The pears were dried both in the AHSC dryer and under the sun. Panel glass temperature, panel floor temperature, panel inlet temperature, panel outlet temperature, drying cabinet inlet temperature, drying cabinet outlet temperature, drying cabinet temperature, drying cabinet moisture, solar radiation, pear internal temperature, air velocity and mass loss of pear were measured at 30 min intervals. Experiments were carried out during the periods of June 2017 in Elazig, Turkey. The experiments started at 8:00 a.m. and continued till 18:00. The experiments were continued until the weight changes in the pear slices stopped. Wet basis moisture content (MCw), dry basis moisture content (MCd), adjustable moisture ratio (MR), drying rate (DR), and convective heat transfer coefficient (h c ) were calculated with both in the AHSC dryer and the open sun drying experiment data. It was found that the values of h c in both drying systems with a range 12.4 and 20.8 W/m 2 • C. Three different kernel models were used in the support vector machine (SVM) regression to construct the predictive model of the calculated h c values for both systems. The mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE) and root relative absolute error (RRAE) analysis were performed to indicate the predictive model's accuracy. As a result, the rate of drying of the pear was examined for both systems and it was observed that the pear had dried earlier in the AHSC drying system. A predictive model was obtained using the SVM regression for the calculated h c values for the pear in the AHSC drying system. The normalized polynomial kernel was determined as the best kernel model in SVM for estimating the h c values.

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Use of Artificial Neural Networks for Prediction of the Convective Heat Transfer Coefficient in Evaporative Mini-Tubes

Cited by 14 publications

References 16 publications

Two-Dimensional Flow Boiling Characteristics With Wettability Surface in Microgap Heat Sink and Heat Transfer Prediction Using Artificial Neural Network

Two-Dimensional Flow Boiling Characteristics With Wettability Surface in Microgap Heat Sink and Heat Transfer Prediction Using Artificial Neural Network

Modeling of Heat Transfer Coefficient in Solar Greenhouse Type Drying Systems

Investigation of Pear Drying Performance by Different Methods and Regression of Convective Heat Transfer Coefficient with Support Vector Machine

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