An efficient conjugate gradient method for convex constrained monotone nonlinear equations with applications
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Publication Details
Author list: Abubakar A.B., Kumam P., Mohammad H., Awwal A.M.
Publisher: Springer Verlag (Germany) / Akadémiai Kiadó
Publication year: 2019
Journal: Journal of Thermal Analysis and Calorimetry (1388-6150)
Volume number: 7
Issue number: 9
ISSN: 1388-6150
eISSN: 1588-2926
Languages: English-Great Britain (EN-GB)
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Abstract
In this study, a model is proposed by applying the least squares support vector machine (LSSVM). In addition, genetic algorithm is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. In addition to temperature and concentration of nanoparticles, the parameters which are used in most of the modeling procedures for thermal conductivity, the effect of particle size is considered. By considering the size of nanoparticles as one of the input variables, a more comprehensive model is obtained which is applicable for wider ranges of influential factor on the thermal conductivity of the nanofluid. The coefficient of determination (R 2 ) for the introduced model is equal to 0.9902, and the mean squared error is 8.64 × 10 −4 for the thermal conductivity ratio of Al 2 O 3 /EG. © 2018, Akadémiai Kiadó, Budapest, Hungary.
Keywords
Ethylene glycol, Least squares support vector machine, Thermal conductivity ratio
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