A novel comparative approach on inverse heat transfer analysis of an experimental setup of an extended surface

Singhal, Meenal; Singla, Rohit; Goyal, Kavita

doi:10.1016/j.icheatmasstransfer.2020.104822

Cited by 7 publications

(1 citation statement)

References 42 publications

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“…The details of the implementation, validation and numerical (statistical) analysis of the suggested (ANN-TTA-SQP) paradigm to explore the effects of different parameters (e.g., wet porous parameter, non-dimensional ambient temperature, convection parameter, in-homogeneity index, radiation, and power index) on the thermal distribution of a fin with linear, quadratic and exponential thermal conductivities are discussed in this section. A detailed comparison is presented between the results obtained using the suggested technique (ANN-TTA-SQP) and those obtained using the particle swarm optimization (PSO) algorithm [ 55 , 56 ], the grey wolf optimization (GWO) algorithm [ 57 ], the whale optimization algorithm (WOA) [ 58 ], the cuckoo search algorithm (CSA) [ 59 ], and a data-fitting-based machine learning strategy [ 60 ], as shown in Table 1 . The accuracy of the results is tested by the values of mean square error.…”

Section: Resultsmentioning

confidence: 99%

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

Alhakami

Khan

Sulaiman

et al. 2022

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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.

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