Estimation of thermal and flow fields due to natural convection using support vector machines (SVM) in a porous cavity with discrete heat sources

Varol, Yasin; Öztop, Hakan F.; Avcı, Engin

doi:10.1016/j.icheatmasstransfer.2008.04.016

Cited by 33 publications

(13 citation statements)

References 36 publications

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“…In heat transfer applications, flow and thermal variables in every location of the system is not of interest in general, but average values are important. ANN is an effective tool to develop correlations for the desired input-out relations by using the experimental or numerical simulation data [17][18][19][20][21][22]. ANNs have been extensively used in energy related systems for modeling and predictions purposes as reviewed in [23].…”

Section: Introductionmentioning

confidence: 99%

Performance Predictions of Air-Cooled Condensers Having Circular and Elliptic Cross-Sections With Artificial Neural Networks

Selimefendigil¹

2019

Journal of Thermal Engineering

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In this study, mathematical models of air cooled condensers with circular and elliptic cross-sections were developed and performances were evaluated with artificial neural networks. Air velocity, orientation angle and ambient temperature were used as the input to the neural network structure while heat transfer rate to the air was used as the output. The data sets were generated from high fidelity, computationally inefficient expensive three dimensional computational fluid dynamics simulations. It was observed that artificial neural network model replaces computational fluid dynamics model and based on the mathematical model with artificial neural network, elliptic condensers perform better in terms of heat transfer compared to circular ones.

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Section: Introductionmentioning

confidence: 99%

Performance Predictions of Air-Cooled Condensers Having Circular and Elliptic Cross-Sections With Artificial Neural Networks

Selimefendigil¹

2019

Journal of Thermal Engineering

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“…Varol, et al [7] used SVM to estimate the temperature and flow fields in a square porous enclosure heated discretely by three isothermal heaters from the side wall. The results demonstrated that SVM was a useful technique on the estimation of streamlines and isotherms.…”

Section: Introductionmentioning

confidence: 99%

“…However, there is rare study about this issue using SVM, the closest study to this proposition is reference [7], in which, the heat transfer process is a steady process with constant boundary conditions. However, in this paper, the heat transfer is an unsteady process, in which the boundary conditions are changing with time.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Temperature Fields induced by Natural Convention in a Cylindrical Enclosure using Fuzzy LS-SVM

Zhou¹,

Cao²,

Du³

et al. 2018

Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018)

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Abstract-Support Vector Machines (SVM) is a machine learning algorithm basing on the statistical learning theory. In this study, SVM is used for the prediction of temperature field induced by natural convection in a cylindrical enclosure. Because of the large amount of computing and poor real-time characteristics of conventional SVM, and also to solve the non optimal problem in the whole situation and the over-fitting phenomenon, Fuzzy LS-SVM is adopted. The heat transfer in the enclosure is an unsteady process. The heat transfer process is firstly simulated with CFD software, then part of simulated data is picked for training of LS-SVM model and the rest of data is used for validation of the model. The prediction results are successfully validated from the mean relative error (MRE), max relative error (MAE), mean square error (MSE) and absolute fraction of variance (R 2 ). Besides, by comparison with artificial neural network based on back propagation (BP-ANN), the fuzzy LS-SVM shows more superior performance in both prediction accuracy and computation efficiency.

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“…A number of excellent introductions of SVM have been published [54,63,[67][68][69][70], and the theory of LSSVM was also described clearly by Suykens and Vandewalle [71] and Suykens et al [63], Niazi et al [72]. In addition, the application of LSSVM in quantification and classification was summarized by Niazi et al [72].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the binary surface tension of mixtures containing ionic liquids using Support Vector Machine algorithms

Hashemkhani

Soleimani

Fazeli

et al. 2015

Journal of Molecular Liquids

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Estimation of thermal and flow fields due to natural convection using support vector machines (SVM) in a porous cavity with discrete heat sources

Cited by 33 publications

References 36 publications

Performance Predictions of Air-Cooled Condensers Having Circular and Elliptic Cross-Sections With Artificial Neural Networks

Performance Predictions of Air-Cooled Condensers Having Circular and Elliptic Cross-Sections With Artificial Neural Networks

Prediction of Temperature Fields induced by Natural Convention in a Cylindrical Enclosure using Fuzzy LS-SVM

Prediction of the binary surface tension of mixtures containing ionic liquids using Support Vector Machine algorithms

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