Abstract
In this paper, we developed an adaptive neuro-fuzzy inference system (ANFIS) to predict the thermal and hydrodynamic properties of two types of Newtonian nanomodules in the outer layer of shell and tube heat exchanger (STHE). The input data for the ANFIS model were the apparent density of the nanoparticles, the Reynolds number, the thermal conductivity of the nanoparticles, and the brand number. According to a particle swarm optimization (PSO) algorithm, multi-component optimization was performed to reduce the overall pressure, increase the heat transfer coefficient, and increase the number of nanofluid cores in the STHE. During the optimization, the pressure of the nanofluids decreased and the number of noses (tube side) was calculated using the ANFIS model. The best ANFIS was a combination of spatial neural network and phase organization. Despite the stability of the nanofluids, the heat transfer during cooking was significantly reduced owing to its resistance to minerals. The formation and laceration of the nanoparticles was experimentally studied. The comparison of experimental thermal conductivity coefficients between the results of the relationship with the ANFIS shows high efficiency and accuracy of the synthetic neural network provided in thermal conductivity data.
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