On the Computational Study of a Fully Wetted Longitudinal Porous Heat Exchanger Using a Machine Learning Approach

Hosam Alhakami; Naveed Ahmad Khan; Muhammad Sulaiman; Wajdi Alhakami; Abdullah Baz

doi:10.3390/e24091280

On the Computational Study of a Fully Wetted Longitudinal Porous Heat Exchanger Using a Machine Learning Approach

Entropy (Basel). 2022 Sep 11;24(9):1280. doi: 10.3390/e24091280.

Authors

Hosam Alhakami¹, Naveed Ahmad Khan², Muhammad Sulaiman², Wajdi Alhakami³, Abdullah Baz⁴

Affiliations

¹ Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah 21955, Saudi Arabia.
² Department of Mathematics, Abdul Wali Khan University, Mardan 23200, Pakistan.
³ Department of Information Technology, College of Computers and Information Technology, Taif University, Taif 26571, Saudi Arabia.
⁴ Department of Computer Engineering, College of Computer and Information Systems, Umm Al-Qura University, Makkah 21955, Saudi Arabia.

Abstract

The present study concerns the modeling of the thermal behavior of a porous longitudinal fin under fully wetted conditions with linear, quadratic, and exponential thermal conductivities surrounded by environments that are convective, conductive, and radiative. Porous fins are widely used in various engineering and everyday life applications. The Darcy model was used to formulate the governing non-linear singular differential equation for the heat transfer phenomenon in the fin. The universal approximation power of multilayer perceptron artificial neural networks (ANN) was applied to establish a model of approximate solutions for the singular non-linear boundary value problem. The optimization strategy of a sports-inspired meta-heuristic paradigm, the Tiki-Taka algorithm (TTA) with sequential quadratic programming (SQP), was utilized to determine the thermal performance and the effective use of fins for diverse values of physical parameters, such as parameter for the moist porous medium, dimensionless ambient temperature, radiation coefficient, power index, in-homogeneity index, convection coefficient, and dimensionless temperature. The results of the designed ANN-TTA-SQP algorithm were validated by comparison with state-of-the-art techniques, including the whale optimization algorithm (WOA), cuckoo search algorithm (CSA), grey wolf optimization (GWO) algorithm, particle swarm optimization (PSO) algorithm, and machine learning algorithms. The percentage of absolute errors and the mean square error in the solutions of the proposed technique were found to lie between 10-4 to 10-5 and 10-8 to 10-10, respectively. A comprehensive study of graphs, statistics of the solutions, and errors demonstrated that the proposed scheme's results were accurate, stable, and reliable. It was concluded that the pace at which heat is transferred from the surface of the fin to the surrounding environment increases in proportion to the degree to which the wet porosity parameter is increased. At the same time, inverse behavior was observed for increase in the power index. The results obtained may support the structural design of thermally effective cooling methods for various electronic consumer devices.

Keywords: functionally graded materials; machine learning techniques; meta-heuristics; thermal analysis; wet porous fin.

Grants and funding

The study was funded by the Deanship of Scientific Research at Umm Al-Qura University, Makkah, Saudi Arabia (Grant Code: 20UQU0067DSR); and Researchers Supporting Project (TURSP-2020/107), Taif University, Taif, Saudi Arabia.