Statistical review of studies on the estimation of thermophysical properties of nanofluids using artificial neural network (ANN)

The purpose of this study was to evaluate the conducted studies in the field of estimating the thermophysical properties of nanofluids using the artificial neural network (ANN) technique. In this research, first, the research method used to determine the target population and collect articles is presented. Then, after identifying the selected research, the most frequent keywords are identified. However, after the introduction of the keywords, the articles are categorized according to the number of publication in each year. The study shows that studies have had the highest rates during 2019, 2020, and 2021. In addition, commonly used nanoparticles were identified, including Al2O3, TiO2, and CuO. The most common ANN algorithms were also identified, including Multilayer Perceptron (MLP), Back Propagation Neural Network (BPNN), and Genetic Algorithm (GA). In the next step, the share of different countries in terms of the number of studies was examined; so that countries such as India, Iran, Vietnam and China were the countries with the most published studies, respectively. Also, by examining the statistical population on the thermophysical properties of nanofluids, it was determined the three thermophysical properties of viscosity, thermal conductivity and heat transfer are the most frequent post-processing studies with ANN. Fig. 2. Frequency results of the year of publication of articles. [Display omitted]