Prediction of the thermophysical properties of Ag-reduced graphene oxide-water/ethylene-glycol hybrid nanofluids using different machine learning methods
Background: Because of their enhanced thermophysical characteristics, namely greater thermal conductivity, viscosity control, and long-term stability than traditional nanofluids, hybrid nanofluids drew interest. Such properties make them suitable candidates for many industrial applications such as s...
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| Format: | Article |
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Elsevier
2025-05-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X25002989 |
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| author | Huaguang Li Ali B.M. Ali Rasha Abed Hussein Narinderjit Singh Sawaran Singh Barno Abdullaeva Zubair Ahmad Soheil Salahshour Mohammadreza Baghoolizadeh Mostafa Pirmoradian |
| author_facet | Huaguang Li Ali B.M. Ali Rasha Abed Hussein Narinderjit Singh Sawaran Singh Barno Abdullaeva Zubair Ahmad Soheil Salahshour Mohammadreza Baghoolizadeh Mostafa Pirmoradian |
| author_sort | Huaguang Li |
| collection | DOAJ |
| description | Background: Because of their enhanced thermophysical characteristics, namely greater thermal conductivity, viscosity control, and long-term stability than traditional nanofluids, hybrid nanofluids drew interest. Such properties make them suitable candidates for many industrial applications such as solar systems and thermal management. However, knowing the thermophysical properties of these materials accurately is difficult because of the complexities of nanoparticles and the interaction with the base fluid. This paper utilizes machine learning methods to predict the thermophysical properties of water/ethylene glycol mixture-based hybrid nanofluids containing reduced silver-graphene oxide. Method: ology: This study aimed to predict Viscosity (DV), Thermal Conductivity (TC) and Density (D) by three machine learning algorithms including multiple linear regression (MLR), Multiple Polynomial Regression (MPR) and Gaussian Process Regression (GPR). A 5 × 28 dataset was used for training and testing the network, with 80 % of the data used for training the network and 20 % for testing the network. Evaluating the performance of algorithms is based on the evaluation indices of Correlation coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Standard Deviation (STD). In addition, optimization is done by the Non-dominated Sorting Genetic Algorithm-II (NSGA-Ⅱ) algorithm and the impact results of different mutation and combination rates are examined. Results: The MPR algorithm yielded the lowest MoD values (0.07 % and −0.06 %) and the highest prediction accuracy among the models tested (R = 0.9999, RMSD = 2.726 × 10−4, STD = 0.0219). Furthermore, NSGA-II optimization results revealed that the temperature and concentration of nanoparticles could effectively increase the thermal conductivity, while too high concentration could also increase viscosity. Finally, through the TOPSIS method, the best point was chosen giving a blend of ideal thermophysical properties. This signifies that machine learning methods can be successfully employed for the prediction and optimization of hybrid nanofluid characteristics. |
| format | Article |
| id | doaj-art-e1d16e276b6c48a0b8083ad1cdddfac1 |
| institution | OA Journals |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
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| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-e1d16e276b6c48a0b8083ad1cdddfac12025-08-20T02:17:39ZengElsevierCase Studies in Thermal Engineering2214-157X2025-05-016910603810.1016/j.csite.2025.106038Prediction of the thermophysical properties of Ag-reduced graphene oxide-water/ethylene-glycol hybrid nanofluids using different machine learning methods Huaguang Li0Ali B.M. Ali1Rasha Abed Hussein2Narinderjit Singh Sawaran Singh3Barno Abdullaeva4Zubair Ahmad5Soheil Salahshour6Mohammadreza Baghoolizadeh7Mostafa Pirmoradian8Intelligent Manufacturing College, Qingdao Huanghai University, Qingdao, Shandong, 266427, China; Corresponding author.Air Conditioning Engineering Department, College of Engineering, University of Warith Al-Anbiyaa, Karbala, IraqDepartment of Dentistry, Al-Manara College for Medical Sciences, Amarah, Maysan, IraqFaculty of Data Science and Information Technology, INTI International University, Persiaran Perdana BBN, Putra Nilai, Nilai, 71800, MalaysiaDepartment of Mathematics and Information Technologies, Vice-Rector for Scientific Affairs, Tashkent State Pedagogical University, Tashkent, UzbekistanCentre of Bee Research and its Products, King Khalid University, P.O. Box 9004, Abha, 61413, Saudi Arabia; Applied College, Mahala Campus, King Khalid University, P.O. Box 9004, Abha, 61413, Saudi ArabiaFaculty of Engineering and Natural Sciences, Istanbul Okan University, Istanbul, Turkey; Faculty of Engineering and Natural Sciences, Bahcesehir University, Istanbul, Turkey; Research Center of Applied Mathematics, Khazar University, Baku, AzerbaijanDepartment of Mechanical Engineering, Shahrekord University, Shahrekord, 88186-34141, IranDepartment of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, IranBackground: Because of their enhanced thermophysical characteristics, namely greater thermal conductivity, viscosity control, and long-term stability than traditional nanofluids, hybrid nanofluids drew interest. Such properties make them suitable candidates for many industrial applications such as solar systems and thermal management. However, knowing the thermophysical properties of these materials accurately is difficult because of the complexities of nanoparticles and the interaction with the base fluid. This paper utilizes machine learning methods to predict the thermophysical properties of water/ethylene glycol mixture-based hybrid nanofluids containing reduced silver-graphene oxide. Method: ology: This study aimed to predict Viscosity (DV), Thermal Conductivity (TC) and Density (D) by three machine learning algorithms including multiple linear regression (MLR), Multiple Polynomial Regression (MPR) and Gaussian Process Regression (GPR). A 5 × 28 dataset was used for training and testing the network, with 80 % of the data used for training the network and 20 % for testing the network. Evaluating the performance of algorithms is based on the evaluation indices of Correlation coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Standard Deviation (STD). In addition, optimization is done by the Non-dominated Sorting Genetic Algorithm-II (NSGA-Ⅱ) algorithm and the impact results of different mutation and combination rates are examined. Results: The MPR algorithm yielded the lowest MoD values (0.07 % and −0.06 %) and the highest prediction accuracy among the models tested (R = 0.9999, RMSD = 2.726 × 10−4, STD = 0.0219). Furthermore, NSGA-II optimization results revealed that the temperature and concentration of nanoparticles could effectively increase the thermal conductivity, while too high concentration could also increase viscosity. Finally, through the TOPSIS method, the best point was chosen giving a blend of ideal thermophysical properties. This signifies that machine learning methods can be successfully employed for the prediction and optimization of hybrid nanofluid characteristics.http://www.sciencedirect.com/science/article/pii/S2214157X25002989Thermophysical propertiesHybrid nanofluidsMachine learning methods |
| spellingShingle | Huaguang Li Ali B.M. Ali Rasha Abed Hussein Narinderjit Singh Sawaran Singh Barno Abdullaeva Zubair Ahmad Soheil Salahshour Mohammadreza Baghoolizadeh Mostafa Pirmoradian Prediction of the thermophysical properties of Ag-reduced graphene oxide-water/ethylene-glycol hybrid nanofluids using different machine learning methods Case Studies in Thermal Engineering Thermophysical properties Hybrid nanofluids Machine learning methods |
| title | Prediction of the thermophysical properties of Ag-reduced graphene oxide-water/ethylene-glycol hybrid nanofluids using different machine learning methods |
| title_full | Prediction of the thermophysical properties of Ag-reduced graphene oxide-water/ethylene-glycol hybrid nanofluids using different machine learning methods |
| title_fullStr | Prediction of the thermophysical properties of Ag-reduced graphene oxide-water/ethylene-glycol hybrid nanofluids using different machine learning methods |
| title_full_unstemmed | Prediction of the thermophysical properties of Ag-reduced graphene oxide-water/ethylene-glycol hybrid nanofluids using different machine learning methods |
| title_short | Prediction of the thermophysical properties of Ag-reduced graphene oxide-water/ethylene-glycol hybrid nanofluids using different machine learning methods |
| title_sort | prediction of the thermophysical properties of ag reduced graphene oxide water ethylene glycol hybrid nanofluids using different machine learning methods |
| topic | Thermophysical properties Hybrid nanofluids Machine learning methods |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X25002989 |
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