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

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.

A design of novel Gudermannian neural networks for the nonlinear multi-pantograph delay differential singular model

“…The heuristic techniques using the stochastic solvers are presented by functioning the strengths of neural networks along with the practical versions of evolutionary computing submissions [30][31][32][33]. For instance, some of the recent possible applications are Thomas-Fermi atom's model [34], polytropic gas spheres and electric circuits [35], heat conduction model of human head [36], prey-predator models [37], heat transfer in micropolar fluid [38], vector-born viral plant disorders [39], singular Lane-Emden equation [40], Fredholm integral models [41], spherical cloud of gas model [42], nonlinear model for financial market forecasting [43], control systems [44], bilinear programming models [45], doubly singular model [46], wetted longitudinal porous heat exchanger model [47], singular functional differential model [48,49], plasma physics problems [50], mosquito dispersal model in a heterogeneous environment [51], Bagley-Torvik models [52], marine ecosystems [53], inclined longitudinal porous fin of trapezoidal [54], singular periodic model [55], power [56] and HIV infection model of CD4+ T cells [57]. These influences have been proven the stochastic solver in terms of convergence, robustness, and accuracy.…”