Prediction of Thermal Conductivity of EG-Al2O3 Nanofluids Using Six Supervised Machine Learning Models
文献类型：期刊论文
| 作者 | Zhu, Tongwei1; Mei, Xiancheng2; Zhang, Jiamin3; Li, Chuanqi4,5 |
| 刊名 | APPLIED SCIENCES-BASEL
 |
| 出版日期 | 2024-07-01 |
| 卷号 | 14期号:14页码:22 |
| 关键词 | EG-Al2O3 nanofluids thermal conductivity prediction machine learning SHAP |
| DOI | 10.3390/app14146264 |
| 英文摘要 | Accurate prediction of the thermal conductivity of ethylene glycol (EG) and aluminum oxide (Al2O3) nanofluids is crucial for improving the utilization rate of energy in industries such as electronics cooling, automotive, and renewable energy systems. However, current theoretical models and simulations face challenges in accurately predicting the thermal conductivity of EG-Al2O3 nanofluids due to their complex and dynamic nature. To that end, this study develops several supervised ML models, including artificial neural network (ANN), decision tree (DT), gradient boosting decision tree (GBDT), k-nearest neighbor (KNN), multi-layer perceptron (MLP), and extreme gradient boosting (XGBoost) models, to predict the thermal conductivity of EG-Al2O3 nanofluids. Three key parameters, particle size (D), temperature (T), and volume fraction (VF) of EG-Al2O3 nanoparticles, are considered as input features for modeling. Furthermore, five indices combining with regression graphs and Taylor diagrams are used to evaluate model performance. The evaluation results indicate that the GBDT model achieved the highest performance among all models, with mean squared errors (MSE) of 6.7735 x 10(-6) and 1.0859 x 10(-5), root mean squared errors (RMSE) of 0.0026 and 0.0033, mean absolute errors (MAE) of 0.0009 and 0.0028, correlation coefficients (R-2) of 0.9974 and 0.9958, and mean absolute percent errors (MAPE) of 0.2764% and 0.9695% in the training and testing phases, respectively. Furthermore, the results of sensitivity analysis conducted using Shapley additive explanations (SHAP) demonstrate that T is the most important feature for predicting the thermal conductivity of EG-Al2O3 nanofluids. This study provides a novel calculation model based on artificial intelligence to realize an innovation beyond the traditional measurement of the thermal conductivity of EG-Al2O3 nanofluids. |
| 资助项目 | China Scholarship Council[202106370038] |
| WOS研究方向 | Chemistry ; Engineering ; Materials Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001278187900001 |
| 出版者 | MDPI |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/42104]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Li, Chuanqi |
| 作者单位 | 1.Univ Grenoble Alpes, Polytech Grenoble, F-38000 Grenoble, France 2.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 3.SINOPEC Res Inst Petr Engn Co Ltd, Beijing 100101, Peoples R China 4.Grenoble Alpes Univ, Lab 3SR, CNRS, UMR 5521, F-38000 Grenoble, France 5.Cent South Univ, Sch Resources & Safety Engn, Changsha 410083, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Tongwei,Mei, Xiancheng,Zhang, Jiamin,et al. Prediction of Thermal Conductivity of EG-Al2O3 Nanofluids Using Six Supervised Machine Learning Models[J]. APPLIED SCIENCES-BASEL,2024,14(14):22. |
| APA | Zhu, Tongwei,Mei, Xiancheng,Zhang, Jiamin,&Li, Chuanqi.(2024).Prediction of Thermal Conductivity of EG-Al2O3 Nanofluids Using Six Supervised Machine Learning Models.APPLIED SCIENCES-BASEL,14(14),22. |
| MLA | Zhu, Tongwei,et al."Prediction of Thermal Conductivity of EG-Al2O3 Nanofluids Using Six Supervised Machine Learning Models".APPLIED SCIENCES-BASEL 14.14(2024):22. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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