Thermal conductivity and dynamic viscosity modeling of Fe2O3/water nanofluid by applying various connectionist approaches |
| |
| Authors: | Mohammad Hossein Ahmadi Afshin Tatar Parinaz Seifaddini Mahyar Ghazvini Roghayeh Ghasempour Mikhail A Sheremet |
| |
| Affiliation: | 1. Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood, Iran;2. mhosein.ahmadi@shahroodut.ac.ir;4. Young Researchers and Elite Club, North Tehran Branch, Islamic Azad University, Tehran, Iran;5. MEMS &6. NEMS Laboratory, Faculty of New Sciences &7. Technologies, University of Tehran, Tehran, Iran;8. Department of Renewable Energy and Environmental Engineering, University of Tehran, Tehran, Iran;9. Laboratory on Convective Heat and Mass Transfer, Tomsk State University, Tomsk, Russia;10. Department of Nuclear and Thermal Power Plants, Tomsk Polytechnic University, Tomsk, Russia |
| |
| Abstract: | AbstractThermal conductivity and dynamic viscosity play key role in heat transfer capacity of nanofluids. In the present study, thermal conductivity and dynamic viscosity of Fe2O3/water are modeled by applying various artificial neural network algorithms. The applied algorithms are MLP, GA-RBF, LSSVM, and CHPSO ANFIS algorithms. The data for modeling procedure are extracted from several experimental studies. Obtained results by the different algorithms are compared and it was concluded that the highest R-squared values belonged to GA-RBF algorithm which were equal to 0.9962 and 0.9982 for thermal conductivity ratio and dynamic viscosity, respectively. |
| |
| Keywords: | |
|
|
