Abstract
This article presents the dataset generated during the process of enhancing the thermophysical properties of nanofluid mixture through fuzzy logic based-modelling and particle swarm optimization (PSO) algorithm. The details of fuzzy model and optimization phases were discussed in our work entitled “Fuzzy modeling and optimization for experimental thermophysical properties of water and ethylene glycol mixture for Al2O3 and TiO2 based nanofluids” (Said et al., 2019). In (Said et al., 2019), the detail of the numerical data has not been clearly presented. However, in this article the inputs’ data values for the density, viscosity, and thermal conductivity, used for training and testing of the fuzzy model, have been mentioned which is very essential if the model has to be rebuilt again. Furthermore, the resulting data variation of the cost function for the 100 runs during the optimization process that had not been presented in (Said et al., 2019) is presented in this work. These data sets can be used as references to analyze the data resulting from any other optimization technique. The datasets are provided in the supplementary materials in Tables 1–4
Highlights
Afterwards, the numerical acquired simulation was conducted by MATLAB/Simulink software package
This article presents the numerical datasets extracted during the improving process of the thermophysical properties of nanofluid mixture employing fuzzy logic based-modelling and particle swarm optimization (PSO) algorithm
Based on the experimental data from [1], an accurate fuzzy model is created to simulate the performance of nanofluid mixture
Summary
Zafar Said a, *, Mohammad Ali Abdelkareem a, b, c, Hegazy Rezk d, e, **, Ahmed M. F a University of Sharjah, College of Engineering, Sustainable and Renewable Energy Engineering Department, Sharjah, P. This article presents the dataset generated during the process of enhancing the thermophysical properties of nanofluid mixture through fuzzy logic based-modelling and particle swarm optimization (PSO) algorithm. The resulting data variation of the cost function for the 100 runs during the optimization process that had not been presented in (Said et al, 2019) is presented in this work. These data sets can be used as references to analyze the data. University of Sharjah, College of Engineering, Sustainable and Renewable Energy Engineering Department, Sharjah, P.
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