THERMAL SCIENCE

International Scientific Journal

Authors of this Paper

External Links

S. Manikandan

,

A.J.D. Nanthakumar

DEVELOPMENT OF A PREDICTIVE MODEL FOR THERMAL CONDUCTIVITY IN GRAPHENE NANOPLATELETS-INFUSED DAMPER OIL USING ANN/RSM

ABSTRACT
This study aims to develop a predictive model for the thermal conductivity of graphene nanoplatelets/SAE10W oil nanofluids using artificial neural networks (ANN) and response surface methodology (RSM). Generally, the property of thermal conductivity has been measured to enhance the heat transfer efficiency of traditional heat transfer fluids. Experiments were conducted using a thermal constants analyzer at different operating conditions, such as varying the volume concentrations of nanoparticles from 0.050% to 0.150% and increasing the temperature from 20°C to 80°C. Results showed an improvement in the thermal conductivity of the nanofluid, ranging from 19% to 41%. A single hidden layer with 12 neurons was found to be the most effective architecture for the ANN model. Additionally, a response surface was closely fitted to experimental data points in the RSM. Then, mean squared error, root mean square error, and R2 values were employed to validate the accuracy of the predicted models. The correlation coefficients of the ANN and RSM models were 0.99761 and 0.9877, respectively. Also, the accuracy of the models was assessed in terms of margins of deviation. The margin of deviation for the ANN model ranged between +0.3926% and –0.4640%, whereas for the RSM model, it was between +0.4137% and –0.4166%. The comparison of the ANN model indicates greater accuracy than the RSM technique. This method for predicting the thermal conductivity of graphene nanoplatelets/SAE10W oil nanofluids is both cost-effective and inventive, minimizing experimental re-search durations.
KEYWORDS
thermal conductivity, Artificial Neural Network, response surface methodology, graphene nanoplatelets
PAPER SUBMITTED: 2023-11-30
PAPER REVISED: 2023-12-16
PAPER ACCEPTED: 2024-02-16
PUBLISHED ONLINE: 2024-08-18
DOI REFERENCE: https://doi.org/10.2298/TSCI231130152M
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2024, VOLUME 28, ISSUE Issue 5, PAGES [4235 - 4247]
REFERENCES
  1. Shah, Z., et al., Heat Transfer Intensification of Nanomaterial with Involve of Swirl Flow Device Concerning Entropy Generation, Sci. Rep., 11 (2021), 1, pp. 1-15
  2. Goncalves, I., et al., Thermal Conductivity of Nanofluids: A Review on Prediction Models, Controversies And Challenges, Appl. Sci., 11 (2021), 6, pp.1-26
  3. Hamze, S., et al., Few-Layer Graphene-Based Nanofluids with Enhanced Thermal Conductivity, Nanomaterials, 10 (2020), 7, pp. 1-21
  4. Hemmat Esfe, M., et al., Thermal Conductivity of MWCNT-TiO2/Water-EG Hybrid Nanofluids: Calculating The Price Performance Factor (PPF) Using Statistical And Experimental Methods (RSM), Case Stud. Therm. Eng., 48 (2023), 103094
  5. Khan, Y., et al., Classification, Synthetic, and Characterization Approaches to Nanoparticles, and Their Applications in Various Fields of Nanotechnology: A Review, Catalysts, 12 (2022), 11, 12111386
  6. Qiu, L., et al., A Review of Recent Advances in Thermophysical Properties at The Nanoscale: From Solid State to Colloids, Phys. Rep., 843 (2020), Feb., pp. 1-81
  7. Hemmat Esfe, M., et al., Rheological Behavior of 10W40 Base Oil Containing Different Combinations of MWCNT-Al2O3 Nanoparticles and Determination of The Target Nano-Lubricant For Industrial Applications, Micro Nano Syst. Lett., 11 (2023), 1
  8. Nfawa, S.R., et al., Novel use of MgO Nanoparticle Additive For Enhancing The Thermal Conductivity of CuO/Water Nanofluid, Case Stud. Therm. Eng., 27 (2021), 101279
  9. Yildiz, G., et al., A Review of Stability, Thermophysical Properties and Impact of Using Nanofluids on The Performance of Refrigeration Systems, Int. J. Refrig., 129 (2021), Sept., pp. 342-364
  10. Barai, D. P., et al., Artificial Neural Network For Prediction of Thermal Conductivity of RGO-Metal Oxide Nanocomposite-Based Nanofluids, Neural Comput. Appl., 34 (2022), 1, pp. 271-282
  11. Nawaz, A., Kumar, P., Optimization of Process Parameters of Lagerstroemia Speciosa Seed Hull Pyrolysis Using a Combined Approach of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) For Renewable Fuel Production, Bioresour. Technol. Reports, 18 (2022), 101110
  12. Nawaz, A., Kumar, P., Thermal Degradation of Hazardous 3-Layered COVID-19 Face Mask Through Pyrolysis: Kinetic, Thermodynamic, Prediction Modelling Using ANN and Volatile Product Characterization, J. Taiwan Inst. Chem. Eng., 139 (2022), 104538
  13. Alhadri, M., et al., Response Surface Methodology (RSM) and Artificial Neural Network (ANN) Simulations For Thermal Flow Hybrid Nanofluid Flow with Darcy-Forchheimer Effects, J. Indian Chem. Soc., 99 (2022), 8, 100607
  14. Alfaleh, A., et al., Predicting Thermal Conductivity and Dynamic Viscosity of Nanofluid By Employment of Support Vector Machines: A Review, Energy Reports, 10 (2023), Nov., pp. 1259-1267
  15. Li, L., et al., Stability, Thermal Performance and Artificial Neural Network Modeling of Viscosity and Thermal Conductivity of Al2O3-Ethylene Glycol Nanofluids, Powder Technol., 363 (2020), Mar., pp. 360-368
  16. Akhgar, A., et al., Developing Dissimilar Artificial Neural Networks (ANN) To Prediction The Thermal Conductivity of MWCNT-TiO2/Water-Ethylene Glycol Hybrid Nanofluid, Powder Technol., 355 (2019), Oct., pp. 602-610
  17. Pare, A., Ghosh, S.K., A Unique Thermal Conductivity Model (ANN) For Nanofluid Based on Experimental Study, Powder Technol., 377 (2021), Jan., pp. 429-438
  18. Nguyen, Q., et al., A Novel Correlation To Calculate Thermal Conductivity of Aqueous Hybrid Graphene Oxide/Silicon Dioxide Nanofluid: Synthesis, Characterizations, Preparation, and Artificial Neural Network Modeling, Arab. J. Sci. Eng., 45 (2020), 11, pp. 9747-9758
  19. Rostami, S., et al., Measurement of The Thermal Conductivity of MWCNT-CuO/Water Hybrid Nanofluid Using Artificial Neural Networks (ANN), J. Therm. Anal. Calorim., 143 (2021), 2, pp. 1097-1105
  20. Arani, A.A.A., et al., Statistical Analysis of Enriched Water Heat Transfer with Various Sizes of MgO Nanoparticles Using Artificial Neural Networks Modeling, Phys. A Stat. Mech. its Appl., 554 (2020), 123950
  21. Chu, Y.-M., et al., Examining Rheological Behavior of MWCNT-TiO2/5W40 Hybrid Nanofluid Based on Experiments and RSM/ANN Modeling, J. Mol. Liq., 333 (2021), 115969
  22. Tian, S., et al., Using Perceptron Feed-Forward Artificial Neural Network (ANN) For Predicting The Thermal Conductivity of Graphene Oxide-Al2O3/Water-Ethylene Glycol Hybrid Nanofluid, Case Stud. Therm. Eng., 26 (2021), 101055
  23. Dinesh, R., et al., An Experimental Investigation and Comprehensive Modelling of Thermal and Rheological Behaviour of Graphene Oxide Nano Platelets Suspended Mineral Oil Nano Lubricant, J. Mol. Liq., 347 (2022), 118357
  24. Jamei, M., et al., On The Thermal Conductivity Assessment of Oil-Based Hybrid Nanofluids Using Extended Kalman Filter Integrated with Feed-Forward Neural Network, Int. J. Heat Mass Transf., 172 (2021), 121159
  25. Salameh, T., et al., Fuzzy Modeling And Particle Swarm Optimization of Al2O3/SiO2 Nanofluid, Int. J. Thermofluids, 10 (2021), 100084
  26. Jiang, Y., et al., Propose A New Approach of Fuzzy Lookup Table Method to Predict Al2O3/Deionized Water Nanofluid Thermal Conductivity Based on Achieved Empirical Data, Phys. A Stat. Mech. its Appl., 527 (2019), 121177
  27. Barewar, S. D., et al., Experimental Investigation of Thermal Conductivity and Its ANN Modeling For Glycol-Based Ag/ZnO Hybrid Nanofluids with Low Concentration, J. Therm. Anal. Calorim., 139 (2020), 3, pp. 1779-1790
  28. Sun, C., et al., Liquid Paraffin Thermal Conductivity with Additives Tungsten Trioxide Nanoparticles: Synthesis and Propose A New Composed Approach of Fuzzy Logic/Artificial Neural Network, Arab. J. Sci. Eng., 46 (2021), 3, pp. 2543-2552
  29. Elsheikh, A. H., et al., An Artificial Neural Network Based Approach For Prediction The Thermal Conductivity of Nanofluids, SN Appl. Sci., 2 (2020), 2, pp. 1-11
  30. Souza, R. R., et al., Recent Advances on The Thermal Properties and Applications of Nanofluids: From Nanomedicine to Renewable Energies, Appl. Therm. Eng., 201 (2022), 117725
  31. Yilmaz, D., Guru, M., Nanofluids: Preparation, Stability, Properties, and Thermal Performance in Terms of Thermo-Hydraulic, Thermodynamics and Thermo-Economic Analysis, Journal of Thermal Analysis and Calorimetry, 147 (2022), 14. pp. 7631-7664
  32. Le Ba, T., et al., Review on the Recent Progress in the Preparation and Stability of Graphene-Based Nanofluids, Journal of Thermal Analysis and Calorimetry, 142 (2020), 3, pp. 1145-1172
  33. Lemmon, E. W., et al., NIST Standard Reference Database 23: Reference Fluid Thermodynamic And Transport Properties-REFPROP, Version 9.1, Standard Reference Data Program, Natl. Inst. Stand. Technol. Gaithersburg, MD, (2013), pp. 1-3
  34. Ibrahim, M., et al., Study of Capabilities of The ANN And RSM Models to Predict The Thermal Conductivity of Nanofluids Containing SiO2 Nanoparticles, J. Therm. Anal. Calorim., 145 (2021), 4, pp. 1993-2003
  35. Hema, M., et al., Prediction of Viscosity of MWCNT-Al2O3 (20:80)/SAE40 Nano-Lubricant Using Multi-Layer Artificial Neural Network (MLP-ANN) Modeling, Eng. Appl. Artif. Intell., 121 (2023), 105948
  36. Esfe, M. H., et al., Optimization and Design of ANN with Levenberg-Marquardt Algorithm to Increase The Accuracy in Predicting the Viscosity of SAE40 Oil-Based Hybrid Nano-Lubricant, Powder Technol., 415 (2023), 118097
  37. Adun, H., et al., A Neural Network-Based Predictive Model For The Thermal Conductivity of Hybrid Nanofluids, Int. Commun. Heat Mass Transf., 119 (2020), 104930
  38. Eneren, P., et al., Experiments on Single-Phase Nanofluid Heat Transfer Mechanisms in Microchannel Heat Sinks: A Review, Energies, 15 (2022), 7, 15072525
  39. Iacobazzi, F., et al., An Explanation of The Al2O3 Nanofluid Thermal Conductivity Based on The Phonon Theory of Liquid, Energy, 116 (2016), Part 1, pp. 786-794
  40. Milanese, M., et al., An Investigation of Layering Phenomenon at The Liquid-Solid Interface in Cu And CuO Based Nanofluids, Int. J. Heat Mass Transf., 103 (2016), Dec., pp. 564-571
  41. Gupta, J., et al., Three-Level Homogeneous Model For The Study of Heat Transfer Mechanism in Metallic Nanofluids, Phys. B Condens. Matter, 662 (2023), 414973
PDF VERSION [DOWNLOAD]
Development of a predictive model for thermal conductivity in graphene nanoplatelets-infused damper oil using ANN/RSM

© 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