Experimental analysis and Levenberg-Marquardt artificial neural network predictions of heat transfer, friction factor, and efficiency of thermosyphon flat plate collector with MgO/water nanofluids

Abstract

Experimental estimations have been made for the thermal efficiency, heat transfer coefficient, and friction factor of solar collector circulated of MgO/water nanofluid at thermosyphon and then predicted with artificial neural network-Levenberg-Marquardt algorithm. The trials were carried out between 10:00 a.m. and 16:30 p.m. at 0.1%, 0.5%, and 1.0% volume loadings, respectively. Results indicate that, under the Reynolds numbers of 143 and 345, a maximum rise in Nusselt number has been observed for 1.0% vol. of nanofluid is to be 21.48% and 37.28%, higher than water data. Maximum friction factor penalties of 1.14-fold and 1.27-fold at 1.0 vol% and at Reynolds numbers of 143 and 345, respectively, were simultaneously observed over water data. Using water and 1.0 vol% of nanofluid in a collector, the efficiencies are 57.15%–65.47%, respectively. The relative variances of the formulae created to assess the factor in friction and Nusselt numbers are within ±2.598%. Meanwhile, the used Levenberg-Marquardt algorithm predicts very high accurate values when compared with the experimental data. The obtained correlation coefficients for Nusselt number, transfer in heat, factor in friction, and efficiency is 0.98795, 0.98627, 0.9991, and 0.98512, respectively, these values indicate that, the used algorithm and its network construction is very congruent with the experimental data.