10.57647/jsm.2026.1801.04

ANN simulation and thermo-flow optimization of a Non-Polar Hybrid CuO nanofluid

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  • Mohammad Ali Fazilati1,
  • Ali Mokhtarian,
  • Mojtaba Rahimi*,
  • Mohammad Hashemian,
  1. Efficiency and Smartization of Energy Systems Research Center, Kho.C., Islamic Azad University, Khomeinishahr, Iran

Received: 2025-10-01

Revised: 2026-02-20

Accepted: 2026-02-25

Published in Issue 2026-03-31

Copyright (c) 2026 Mohammad Ali Fazilati, Ali Mokhtarian, Mojtaba Rahimi, Mohammad Hashemian (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Fazilati, M. A., Mokhtarian, A., Rahimi, M., & Hashemian, M. (2026). ANN simulation and thermo-flow optimization of a Non-Polar Hybrid CuO nanofluid. Journal of Solid Mechanics, 18(1). https://doi.org/10.57647/jsm.2026.1801.04
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Abstract

Nanoparticles (NPs) can improve the thermo-physical properties of the fluids and increase the effectiveness of heat transfer systems. In this way, finding the optimal properties of nanofluids (NFs) is very important. The present work aims to model and dynamic viscosity (DV) and thermal conductivity (TC) of CuO / 1-4 dioxane + Diethyl amine (DEA) as the non-polar NF with binary base fluids. The input parameters include the temperature and the solid volume fraction (SVF) of NPs. Based on available experimental data, the weight ratio of the NPs is 0.01 % to 0.06 % with temperatures ranging from 25oC to 45oC. The NF is modeled by the design and training of two separate two-layer feedforward artificial neural networks (ANNs) for the prediction of DV and TC at desired inputs of temperature and SVF. The average/maximum relative errors for test datasets are 0.3387/0.5078 and 0.3230/0.6871 for DV and TC prediction networks, respectively. Based on the designed model, a multi-objective optimization problem is defined to specify the maximum TC and minimum DV simultaneously and solved using the MOPSO method. Finally, the optimal values of the objective functions and the corresponding input parameters are plotted along with the Pareto optimal points.

Keywords

  • Nanofluid,
  • Dynamic viscosity,
  • Thermal conductivity,
  • Artificial Neural Network,
  • Multi-Objective Optimization
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References

  1. Miansari, M., Jafari, S. S., Alizadeh, A. A., & Fazilati, M. A. (2023). Hydrothermal behavior of different hybrid nanofluids in a dimpled tube heat exchanger. Engineering Analysis with Boundary Elements, 157, 21-33. https://doi.org/10.1016/j.enganabound.2023.08.035
  2. Zeeshan, A., Shehzad, N., Ellahi, R., & Alamri, S. Z. (2018). Convective Poiseuille flow of Al2O3-EG nanofluid in a porous wavy channel with thermal radiation. Neural Computing and Applications, 30, 3371-3382. https://doi.org/10.1007/s00521-017-2924-9
  3. Irandoost Shahrestani, M., Maleki, A., Safdari Shadloo, M., & Tlili, I. (2020). Numerical investigation of forced convective heat transfer and performance evaluation criterion of Al2O3/water nanofluid flow inside an axisymmetric microchannel. Symmetry, 12(1), Article 120. https://doi.org/10.3390/sym12010120
  4. Xuan, Y., Li, Q., & Tie, P. (2013). The effect of surfactants on heat transfer feature of nanofluids. Experimental Thermal and Fluid Science, 46, 259-262. https://doi.org/10.1016/j.expthermflusci.2012.12.004
  5. Cherecheş, E. I., Bejan, D., & Minea, A. A. (2023). Experimental on viscosity and isobaric heat capacity of [C4mim][BF4] ionic liquid with MWCNT nanoparticles. Journal of Molecular Liquids, 391, Article 123214. https://doi.org/10.1016/j.molliq.2023.123214
  6. Yaw, C. T., Koh, S., Sandhya, M., Kadirgama, K., Tiong, S. K., Ramasamy, D., Sudhakar, K., Samykano, M., Benedict, F., & Tan, C. H. (2023). Heat transfer enhancement by hybrid nano additives graphene nanoplatelets/cellulose nanocrystal for the automobile cooling system (radiator). Nanomaterials, 13(5), Article 808. https://doi.org/10.3390/nano13050808
  7. López-Domínguez, P., Díez-Sierra, J., Schäfer, S., Schunk, S. A., Bäcker, M., De Buysser, K., Van Driessche, I., & Rijckaert, H. (2024). Size-controlled oxide perovskite nanocrystal synthesis: Design, optimization, and mechanism insights. Crystal Growth & Design, 24(6), 2362-2369. https://doi.org/10.1021/acs.cgd.3c01273
  8. Zhang, H., Qing, S., Xu, J., Zhang, X., & Zhang, A. (2022). Stability and thermal conductivity of TiO2/water nanofluids: A comparison of the effects of surfactants and surface modification. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 641, Article 128492. https://doi.org/10.1016/j.colsurfa.2022.128492
  9. Yang, B., & Han, Z. (2006). Temperature-dependent thermal conductivity of nanorod-based nanofluids. Applied Physics Letters, 89(8), Article 083111. https://doi.org/10.1063/1.2338424
  10. Ahmadipour, M., Ardani, M. R., Pang, A. L., Wee, M. F. M. R., & Satgunam, M. (2024). Removal of antibiotic sulfamethoxazole in water using rapid growing ZnO nanorod. Ceramics International, 50(16), 27645-27653. https://doi.org/10.1016/j.ceramint.2024.04.405
  11. Murshed, S. S. (2025). Thermal properties and features of nanofluids. MDPI.
  12. Sang, L., Ai, W., Wu, Y., & Ma, C. (2020). Enhanced specific heat and thermal conductivity of ternary carbonate nanofluids with carbon nanotubes for solar power applications. International Journal of Energy Research, 44(1), 334-343. https://doi.org/10.1002/er.4923
  13. Carrillo-Berdugo, I., Midgley, S. D., Grau-Crespo, R., Zorrilla, D., & Navas, J. (2020). Understanding the specific heat enhancement in metal-containing nanofluids for thermal energy storage: Experimental and ab initio evidence for a strong interfacial layering effect. ACS Applied Energy Materials, 3(9), 9246-9256. https://doi.org/10.1021/acsaem.0c01556
  14. Mirahmad, A., Kumar, R. S., Doldán, B. P., Rios, C. P., & Díez-Sierra, J. (2025). Beyond thermal conductivity: A review of nanofluids for enhanced energy storage and heat transfer. Nanomaterials, 15(4), Article 302. https://doi.org/10.3390/nano15040302
  15. Selvan, P., Jebakani, D., Jeyasubramanian, K., & Jones Joseph Jebaraj, D. (2022). Enhancement of thermal conductivity of water based individual and hybrid SiO2/Ag nanofluids with the usage of calcium carbonate nano particles as stabilizing agent. Journal of Molecular Liquids, 345, Article 117846. https://doi.org/10.1016/j.molliq.2021.117846
  16. Ahsan, M., Qamar, A., Shaukat, R., Siddiqi, H.-u.-R., Anwar, Z., Farooq, M., Amjad, M., Imran, S., Ahmed, M., Mujtaba, M. A., Fayaz, H., & Souayeh, B. (2024). Thermal and hydraulic performance of ZnO/EG based nanofluids in mini tubes of different diameters: An experimental investigation. Heliyon, 10(5), Article e26493. https://doi.org/10.1016/j.heliyon.2024.e26493
  17. Gangadhar, K., Naga Chandrika, G., & Dinarvand, S. (2025). Investigation into silver-engine oil nanoliquid convinced by Riga surface: Deviations in three binary nanofluids. Modern Physics Letters B, 39(2), Article 2450385. https://doi.org/10.1142/s0217984924503858
  18. Li, Z., Cui, L., Li, B., & Du, X. (2021). Mechanism exploration of the enhancement of thermal energy storage in molten salt nanofluid. Physical Chemistry Chemical Physics, 23(23), 13181-13189. https://doi.org/10.1039/d1cp00125f
  19. Minea, A. A., & Cherecheş, E. I. (2024). Experimental studies on thermal conductivity and heat transfer of 1-Butyl-3-methylimidazolium tetrafluoroborate ionic liquid and its nanocolloids. International Communications in Heat and Mass Transfer, 154, Article 107406. https://doi.org/10.1016/j.icheatmasstransfer.2024.107406
  20. Strandberg, R., Ray, D., & Das, D. K. (2024). Experimental characterization of hydronic air coil performance with aluminum oxide nanofluids of three concentrations. Applied Nano, 5(2), 84-107. https://doi.org/10.3390/applnano5020008
  21. Rodríguez-Laguna, M. R., Castro-Alvarez, A., Sledzinska, M., Maire, J., Costanzo, F., Ensing, B., Pruneda, M., Ordejón, P., Sotomayor Torres, C. M., Gómez-Romero, P., & Chávez-Ángel, E. (2018). Mechanisms behind the enhancement of thermal properties of graphene nanofluids. Nanoscale, 10(32), 15402-15409. https://doi.org/10.1039/c8nr02762e
  22. De los Santos, D., Gallardo, J. J., Carrillo-Berdugo, I., Alcántara, R., Estellé, P., Gragera, S., Gragera, M., & Navas, J. (2024). Nanofluids based on Pd nanoparticles and a linear silicone-based fluid: Toward highly efficient heat transfer fluids for concentrated solar power. ACS Sustainable Chemistry & Engineering, 12(6), 2375-2385. https://doi.org/10.1021/acssuschemeng.3c07285
  23. Trisaksri, V., & Wongwises, S. (2009). Nucleate pool boiling heat transfer of TiO2–R141b nanofluids. International Journal of Heat and Mass Transfer, 52(5-6), 1582-1588. https://doi.org/10.1016/j.ijheatmasstransfer.2008.07.041
  24. Ramezanizadeh, M., Ahmadi, M. A., Ahmadi, M. H., & Alhuyi Nazari, M. (2019). Rigorous smart model for predicting dynamic viscosity of Al2O3/water nanofluid. Journal of Thermal Analysis and Calorimetry, 137, 307-316. https://doi.org/10.1007/s10973-018-7916-1
  25. Komeilibirjandi, A., Raffiee, A. H., Maleki, A., Alhuyi Nazari, M., & Safdari Shadloo, M. (2020). Thermal conductivity prediction of nanofluids containing CuO nanoparticles by using correlation and artificial neural network. Journal of Thermal Analysis and Calorimetry, 139, 2679-2689. https://doi.org/10.1007/s10973-019-08838-w
  26. Maheshwary, P., Handa, C., & Nemade, K. (2017). A comprehensive study of effect of concentration, particle size and particle shape on thermal conductivity of titania/water based nanofluid. Applied Thermal Engineering, 119, 79-88. https://doi.org/10.1016/j.applthermaleng.2017.03.054
  27. Shamaeil, M., Firouzi, M., & Fakhar, A. (2016). The effects of temperature and volume fraction on the thermal conductivity of functionalized DWCNTs/ethylene glycol nanofluid: An experimental study. Journal of Thermal Analysis and Calorimetry, 126, 1455-1462. https://doi.org/10.1007/s10973-016-5548-x
  28. Jeong, J., Li, C., Kwon, Y., Lee, J., Kim, S. H., & Yun, R. (2013). Particle shape effect on the viscosity and thermal conductivity of ZnO nanofluids. International Journal of Refrigeration, 36(8), 2233-2241. https://doi.org/10.1016/j.ijrefrig.2013.07.024
  29. Rashidi, M. M., Nazari, M. A., Mahariq, I., Assad, M. E. H., Ali, M. E., Almuzaiqer, R., Nuhait, A., & Murshid, N. (2021). Thermophysical properties of hybrid nanofluids and the proposed models: An updated comprehensive study. Nanomaterials, 11(11), Article 3084. https://doi.org/10.3390/nano11113084
  30. Ayoob, H. W., Omar, I., Ghanim, W. K., Fares, M. N., Fazilati, M. A., Salahshour, S., & Esmaeili, S. (2025). The thermal-flow performance of water-Al2O3 nanofluid flow in an elliptical duct heat exchanger equipped with two rotating twisted tapes. Case Studies in Chemical and Environmental Engineering, 11, Article 101094. https://doi.org/10.1016/j.cscee.2025.101094
  31. Kshirsagar, D. P., & Venkatesh, M. (2021). A review on hybrid nanofluids for engineering applications. Materials Today: Proceedings, 44(Part 1), 744-755. https://doi.org/10.1016/j.matpr.2020.10.637
  32. Taherialekouhi, R., Rasouli, S., & Khosravi, A. (2019). An experimental study on stability and thermal conductivity of water-graphene oxide/aluminum oxide nanoparticles as a cooling hybrid nanofluid. International Journal of Heat and Mass Transfer, 145, Article 118751. https://doi.org/10.1016/j.ijheatmasstransfer.2019.118751
  33. de Oliveira, L. R., Ribeiro, S. R. F. L., Reis, M. H. M., Cardoso, V. L., & Bandarra Filho, E. P. (2019). Experimental study on the thermal conductivity and viscosity of ethylene glycol-based nanofluid containing diamond‑silver hybrid material. Diamond and Related Materials, 96, 216-230. https://doi.org/10.1016/j.diamond.2019.05.004
  34. Venkatesh, R., Hossain, I., Mohanavel, V., Soudagar, M. E. M., Alharbi, S. A., & Al Obaid, S. (2024). Flat plate solar collector activated with alumina-silicon dioxide-copper oxide hybrid nanofluid: Thermal performance study. Applied Thermal Engineering, 257(Part B), Article 124496. https://doi.org/10.1016/j.applthermaleng.2024.124496
  35. Maleki, A., Haghighi, A., Irandoost Shahrestani, M., & Abdelmalek, Z. (2021). Applying different types of artificial neural network for modeling thermal conductivity of nanofluids containing silica particles. Journal of Thermal Analysis and Calorimetry, 144, 1613-1622. https://doi.org/10.1007/s10973-020-09541-x
  36. Rostamzadeh-Renani, R., Jasim, D. J., Baghoolizadeh, M., Rostamzadeh-Renani, M., Andani, H. T., Salahshour, S., & Baghaei, S. (2024). Multi-objective optimization of rheological behavior of nanofluids containing CuO nanoparticles by NSGA II, MOPSO, and MOGWO evolutionary algorithms and Group Method of Data Handling Artificial neural networks. Materials Today Communications, 38, Article 107709. https://doi.org/10.1016/j.mtcomm.2023.107709
  37. Esfe, M. H., Esfandeh, S., Amoozadkhalili, F., & Toghraie, D. (2023). Increasing the accuracy of estimating the dynamic viscosity of hybrid nano-lubricants containing MWCNT-MgO by optimizing using an artificial neural network. Arabian Journal of Chemistry, 16(1), Article 104405. https://doi.org/10.1016/j.arabjc.2022.104405
  38. Petrudi, A. M., & Rahmani, M. (2020). Validation and optimization of thermophysical properties for thermal conductivity and viscosity of nanofluid engine oil using neural network. Journal of Modeling and Simulation of Materials, 3(1), 53-60. https://doi.org/10.21467/jmsm.3.1.53-60
  39. Alsaady, M. (2021). Modeling and multi-objective optimization of thermophysical parameters of hybrid nanodiamond–Ni-based nanofluids via response surface methodology. Frontiers in Mechanical Engineering, 7, Article 675511. https://doi.org/10.3389/fmech.2021.675511
  40. Esfe, M. H., Motallebi, S. M., & Toghraie, D. (2022). A novel experimental and statistical study on ethylene glycol-based nanofluid enriched by MWCNT and CuO nanoparticles. Annals of Nuclear Energy, 177, Article 109283. https://doi.org/10.1016/j.anucene.2022.109283
  41. Hai, T., Basem, A., Alizadeh, A. A., Singh, P. K., Rajab, H., Maatki, C., Becheikh, N., Kolsi, L., Singh, N. S. S., & Maleki, H. (2025). Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: A computational intelligence strategy. Scientific Reports, 15, Article 1986. https://doi.org/10.1038/s41598-025-19598-z
  42. Akhgar, A., Toghraie, D., Sina, N., & Afrand, M. (2019). Developing dissimilar artificial neural networks (ANNs) to prediction the thermal conductivity of MWCNT-TiO2/ethylene glycol hybrid nanofluid. Powder Technology, 355, 602-610. https://doi.org/10.1016/j.powtec.2019.07.086
  43. Hemmat Esfe, M., Hatami, H., Kiannejad Amiri, M., Alidoust, S., Toghraie, D., & Esfandeh, S. (2023). Multi-objective optimization of viscosity and thermal conductivity of TiO2/BioGlycol-Water nanofluids with sorting non-dominated genetic algorithm II coupled with response surface methodology. Materials Today Communications, 36, Article 106718. https://doi.org/10.1016/j.mtcomm.2023.106718
  44. Ru, Y., Ali, A. B., Qader, K. H., Abdulaali, H. K., Jhala, R., Ismailov, S., Salahshour, S., & Mokhtarian, A. (2025). Accurate prediction of the rheological behavior of MWCNT-Al2O3/water-ethylene glycol nanofluid with metaheuristic-optimized machine learning models. International Journal of Thermal Sciences, 211, Article 109691. https://doi.org/10.1016/j.ijthermalsci.2025.109691
  45. Rohini, B., Jeevaraj, A. K. S., Suvitha, A., Sangeetha, R., & Revathi, V. (2024). Optimization of thermal and rheological performance in non-polar binary fluids using hybrid CuO nanoparticles. Journal of Physics: Conference Series, 012010. https://doi.org/10.1088/1742-6596/2886/1/012010
  46. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117. https://doi.org/10.1016/j.neunet.2014.09.003
  47. Abed, H., Ali Fazilati, M., Toghraie, D., Pirmoradian, M., & Mehmandoust, B. (2022). Molecular dynamics study of the thermal behavior of ammonia refrigerant in the presence of copper nanoparticles at different volume ratios and initial temperatures. Journal of Molecular Modeling, 28(6), Article 157. https://doi.org/10.1007/s00894-022-05156-1
  48. Hashempour, S., Toghraie, D., & Fazilati, M. A. (2023). Investigation of the thermal characteristics of water-based hybrid and mono nanofluids containing cerium oxide-CuO-MWCNT nanoparticles at various temperatures and volume fraction of nanoparticles and propose a new relationship. Journal of Materials Research and Technology, 26, 1276-1292. https://doi.org/10.1016/j.jmrt.2023.07.224
  49. Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256-279. https://doi.org/10.1109/tevc.2004.826067
  50. Coello, C. A. C., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems (2nd ed.). Springer. https://doi.org/10.1007/978-1-4757-5184-0