Dataset on fuzzy logic based-modelling and optimization of thermophysical properties of nanofluid mixture
Zafar Said,
Mohammad Ali Abdelkareem,
Hegazy Rezk,
Ahmed M. Nassef
Affiliations
Zafar Said
University of Sharjah, College of Engineering, Sustainable and Renewable Energy Engineering Department, Sharjah, P. O. Box 27272, United Arab Emirates; Corresponding author. University of Sharjah, College of Engineering, Sustainable and Renewable Energy Engineering Department, Sharjah, P. O. Box 27272, United Arab Emirates.
Mohammad Ali Abdelkareem
University of Sharjah, College of Engineering, Sustainable and Renewable Energy Engineering Department, Sharjah, P. O. Box 27272, United Arab Emirates; Center for Advanced Materials Research, University of Sharjah, PO Box 27272, Sharjah, United Arab Emirates; Chemical Engineering Department, Faculty of Engineering, Minia University, Egypt
Hegazy Rezk
College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Saudi Arabia; Electrical Engineering Department, Faculty of Engineering, Minia University, Egypt; Corresponding author. College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Saudi Arabia.
Ahmed M. Nassef
College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Saudi Arabia; Computers and Automatic Control Engineering Department, Faculty of Engineering, Tanta University, Egypt
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
