A Novel Learning Approach for Different Profile Shapes of Convecting–Radiating Fins Based on Shifted Gegenbauer LSSVM

Elyas Shivanian, Zeinab Hajimohammadi (), Fatemeh Baharifard (), Kourosh Parand and Ramin Kazemi ()
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Elyas Shivanian: �Department of Applied Mathematics, Imam Khomeini International University, Qazvin, Iran
Zeinab Hajimohammadi: Department of Data and Computer Science, Faculty of Mathematical Sciences, Shahid Beheshti University, G. C. Tehran, Iran
Fatemeh Baharifard: ��School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
Kourosh Parand: Department of Data and Computer Science, Faculty of Mathematical Sciences, Shahid Beheshti University, G. C. Tehran, Iran‡Department of Cognitive Modeling, Institute for Cognitive and Brain Sciences, Shahid Beheshti University, G. C. Tehran, Iran§Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada
Ramin Kazemi: ��Department of Statistics, Imam Khomeini International University, Qazvin, Iran

New Mathematics and Natural Computation (NMNC), 2023, vol. 19, issue 01, 195-215

Abstract: The purpose of this paper is to introduce a novel learning approach to solve the heat transfer problem from convecting-radiating fin model. This model is a nonlinear differential equation in which different boundary conditions cause different profile shapes including rectangular, triangular, trapezoidal and concave parabolic. We consider one-dimensional, steady conduction in the fin and neglect radiative exchange between adjacent fins and between the fin and its primary surface. Our method is based on using the quasilinearization method to linearize the nonlinear models and applying shifted Gegenbauer polynomials as new kernel in least squares support vector machines method. The results of fin efficiency and heat transfer rate of the problems which compared with available previous results indicate better efficiency and accuracy of the proposed approach.

Keywords: Convecting-radiating fin; Heat transfer rate; Fin efficiency; Least squares support vector machines method; Shifted Gegenbauer polynomials (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S1793005723500060

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