International Scientific Journal


It is well-known that nanofluids differ significantly from traditional heat transfer fluids in terms of their thermal and transfer characteristics. Two of CO2 transfer characteristics, its thermal conductivity and its viscosity, are crucial to improved oil retrieval methods and industries refrigeration. By combining molecular modelling with various machine learning algorithms, this study predicts the conduction characteristics of iron oxide CO2 nanofluids. It is possible to evaluate the accuracy of these transfer parameter estimates by applying machine learning methods such as decision tree, K-nearest neighbors, and linear regression. Predicting these transfer qualities requires knowing the size, fraction of nanoparticle volume, and temperature. To determine the characteristics, molecular dynamics simulations are run using the large-scale atom Vastly equivalent simulant. An inter- and intra-variable Pearson correlation was established to confirm that the input variables were reliant on m and thermal conductivity. The results were finally confirmed by using statistical coefficients of determination. For a variety of temperature ranges, volume fractions, and nanoparticle sizes, the study found that the decision tree model was the best at predicting the transport parameters of nanofluids. It has a 99% success rate.
PAPER REVISED: 2023-09-19
PAPER ACCEPTED: 2023-10-15
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2024, VOLUME 28, ISSUE Issue 1, PAGES [717 - 729]
  1. Zhang, Y., Xu, X., Machine Learning Specific Heat Capacities of Nanofluids Containing CuO and Al2O3, AIChE Journal, 67 (2021), 9
  2. Mukesh, P. C., et al., Prediction of Nanofluid Viscosity Using Multilayer Perceptron and Gaussian Process Regression, Journal Therm. Anal. Calorim., 144 (2021), 4, pp. 1151-1160
  3. Kanti, P., et al., Thermal Performance of Hybrid Fly Ash and Copper Nanofluid in Various Mixture Ratios: Experimental Investigation And Application of a Modern Ensemble Machine Learning Approach, International Communications in Heat and Mass Transfer, 129 (2021), 105731
  4. Wadi, V. T., et al., Experimental Study and Computational Intelligence on Dynamic Viscosity and Thermal Conductivity of Hnts Based Nanolubricant, Industrial Lubrication and Tribology, 74 (2022), 1, pp. 102-110, 100501
  5. Kanti, P. K., et al., Thermophysical Profile of Graphene Oxide and MXene Hybrid Nanofluids for Sustainable Energy Applications: Model Prediction with a Bayesian Optimized Neural Network with K-Cross fold Validation, FlatChem, 39 (2023), 100501`
  6. Mukesh Kumar, P. C., Kavitha, R., Regression Analysis for Thermal Properties of Al2O3/H2O Nanofluid Using Machine Learning Techniques, Heliyon, 6 (2020), 6
  7. Adun, H., et al., An Experimental Investigation of Thermal Conductivity and Dynamic Viscosity of Al2O3- ZnO-Fe3O4 Ternary Hybrid Nanofluid and Development of Machine Learning Model, Powder Technol, 394 (2021), Dec., pp. 1121-1140
  8. Wang, X., et al., A Comprehensive Review on the Application of Nanofluid in Heat Pipe Based on the Machine Learning: Theory, Application and Prediction, Renewable and Sustainable Energy Reviews, 150 (2021), 111434
  9. Said, Z., et al., Recent Advances in Machine Learning Research for Nanofluid Heat Transfer in Renewable Energy, in: Advances in Nanofluid Heat Transfer, Elsevier, Amsterdam, The Netherlands, 2022, Chapter 7, pp. 203-228
  10. Zhang, Z., et al., Optimized ANFIS Models Based on Grid Partitioning, Subtractive Clustering, And Fuzzy C-Means to Precise Prediction of Thermophysical Properties of Hybrid Nanofluids, Chemical En­gineering Journal, 471 (2023), 144362
  11. Said, Z., et al., Experimental Analysis of Novel Ionic Liquid-MXene Hybrid Nanofluid's Energy Storage Properties: Model-Prediction Using Modern Ensemble Machine Learning Methods, Journal Energy Storage, 52 (2022), 104858
  12. Sahin, F., et al., From Experimental Data to Predictions: Artificial intelligence Supported New Mathematical Approaches for Estimating Thermal Conductivity, Viscosity and Zeta Potential in Fe3O4-Water Magnetic Nanofluids, Powder Technol, 430 (2023), 118974
  13. Adun, H., et al., Estimation of Thermophysical Property of Hybrid Nanofluids for Solar Thermal Applications: Implementation of Novel Optimizable Gaussian Process Regression (O-GPR) Approach for Viscosity Prediction, Neural Comput. Appl., 34 (2022), 13, pp. 11233-11254
  14. Jamei, M., Said, Z., Recent Advances in the Prediction of Thermophysical Properties of Nanofluids Using Artificial Intelligence, in: Hybrid Nanofluids: Preparation, ChArcerization and Applications, Elsevier, Amsterdam, The Netherlands, 2022, Chapter 9, pp. 203-232
  15. Shi, L., et al., Thermo-Physical Properties Prediction of Carbon-Based Magnetic Nanofluids Based on an Artificial Neural Network, Renewable and Sustainable Energy Reviews, 149 (2021), 111341
  16. Li, J., et al., Prediction and Optimization of the Thermal Properties of TiO2/Water Nanofluids in the Framework of a Machine Learning Approach, Fluid Dynamics and Materials Processing, 19 (2023), 8, Chapter 9, pp. 2181-2200
  17. Bhanuteja, S., et al., Prediction of Thermophysical Properties of Hybrid Nanofluids Using Machine Learn­ing Algorithms, International Journal on Interactive Design and Manufacturing, On-line first,, 2023
  18. Onyiriuka, E., Modelling the Thermal Conductivity of Nanofluids Using a Novel Model of Models Approach, Journal Therm. Anal. Calorim., 148 (2023), Nov., pp. 13569-13585
  19. Colak, A. B., et al., Prediction of Nanofluid-Flows' Optimum Velocity in Finned Tube-in-Tube Heat Exchangers Using Artificial Neural Network, Kerntechnik, 88 (2023), 1, pp. 100-113
  20. Girimurugan, R., et al., Application of Deep Learning to the Prediction of Solar Irradiance through Missing Data, International Journal of Photoenergy, 2023 (2023), 4717110
  21. Alghamdi, W., et al., Turbulence Modelling through Deep Learning: An in-Depth Study of Wasserstein GAN, Proceedings, 4th Int. Conf. on Smart Electronics and Communication, Trichy, India, 2023, pp. 793-797
  22. Malek, N. A., et al., Low-Dimensional Nanomaterials For Nanofluids: A Review of Heat Transfer Enhancement, Journal Therm. Anal. Calorim., 148 (2023), 19, pp. 9785-9811
  23. Ganga, S., et al., Modelling of Viscosity and Thermal Conductivity of Water-Based Nanofluids Using Machine-Learning Techniques, International Journal of Mathematical, Engineering and Management Sciences, 8 (2023), 5, pp. 817-840

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence