Sensitivity analysis and modeling of thermophysical properties of a hybrid nanofluid by use of different intelligent techniques
Alhuyi Nazari M. Ahmadi M.H. Amooie M.A. Kumar R. Makhanova M.A. Zhansulu U. Blazek V. Prokop L. Misak S.
December 2025Elsevier B.V.
International Journal of Heat and Fluid Flow
2025#116
The thermal conductivity and dynamic viscosity of nanofluids are essential factors in determining heat transfer and fluid flow characteristics. Intelligent methods have demonstrated great effectiveness for the precise estimation and modeling of these properties. The purpose of this study is to model both thermal conductivity and dynamic viscosity of a hybrid nanofluid, TiO2-SiO2/water-ethylene glycol, by application of three intelligent approaches namely Group Method of Data Handling (GMDH), Particle Swarm Optimization-Adaptive Neuro Fuzzy Inference System (PSO-ANFIS) and Genetic Algorithm-Adaptive Neuro Fuzzy Inference System (GA-ANFIS). The outcome of the study shows significant precision of the proposed models in estimation of the thermophysical properties. The most accurate models for thermal conductivity and dynamic viscosity are PSO-ANFIS and GMDH, respectively. R2 & and Average Absolute Relative Deviation (AARD) for the thermal conductivity and dynamic viscosity of the nanofluids with the most accurate models are 0.9907 & 0.41% and 0.9889 & 2.45%, respectively. Furthermore, sensitivity analysis is conducted on both properties of the nanofluid by considering temperature, concentration, and mixture ratio of the hybrid nanofluids and it is found that for both properties, temperature has the highest effect and is followed by the concentration.
ANFIS , Dynamic viscosity , GMDH , Hybrid nanofluid , Thermal conductivity
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Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam
School of Engineering & Technology, Duy Tan University, Da Nang, Viet Nam
Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood, Iran
Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, 70803, LA, United States
Karnavati School of Research, Karnavati University, Gujarat, India
Technical Faculty, Saken Seifullin Kazakh Agrotechnical University, Zhenis Avenue 62, Astana, 010000, Kazakhstan
Department of Power Engineering, ALT University named after Mukhamedzhan Tynyshpayev, 97 Shevchenko Str, Almaty, 050012, Kazakhstan
ENET Centre, CEET, VSB—Technical University of Ostrava, Ostrava, 708 00, Czech Republic
Institute of Research and Development
School of Engineering & Technology
Faculty of Mechanical Engineering
Department of Mechanical and Industrial Engineering
Karnavati School of Research
Technical Faculty
Department of Power Engineering
ENET Centre
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