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Artificial Neural Network Approaches for Predicting the Heat Transfer in a Mini-Channel Heatsink with Alumina/Water Nanofluid | ||
| Journal of Heat and Mass Transfer Research | ||
| دوره 11، شماره 1 - شماره پیاپی 21، مرداد 2024، صفحه 75-88 اصل مقاله (780.31 K) | ||
| نوع مقاله: Full Length Research Article | ||
| شناسه دیجیتال (DOI): 10.22075/jhmtr.2024.32947.1520 | ||
| نویسندگان | ||
| Mohammad Mahdi Tafarroj* 1؛ Seyed Soheil Mousavi Ajarostaghi2؛ C.J. Ho3؛ Wei-Mon Yan* 4، 5 | ||
| 1Mechanical Engineering Department, Faculty of Engineering, Lorestan University, P.O. Box 68151-44316, Khorramabad, Iran | ||
| 2Mechanical Engineering Department, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada | ||
| 3Department of Mechanical Engineering, National Cheng-Kung University, Tainan 70101, Taiwan | ||
| 4Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 10608, Taiwan | ||
| 5Research Center of Energy Conservation for New Generation of Residential, Commercial, and Industrial Sectors, National Taipei, University of Technology, Taipei 10608, Taiwan | ||
| تاریخ دریافت: 18 بهمن 1402، تاریخ بازنگری: 25 اسفند 1402، تاریخ پذیرش: 26 اسفند 1402 | ||
| چکیده | ||
| This work uses artificial neural networks to evaluate heat transfer in a mini-channel heatsink using an alumina/water nanofluid. The multi-layer perceptron (MLP) and radial basis function (RBF) neural networks are employed for the modeling. To apply the artificial neural network analysis, 60 data of experimental works are utilized. The outcomes depicted that the simulated annealing (SA) technique significantly increased the performance of the RBF network, although the optimal MLP structure was discovered by trial and error. The optimized RBF network carried over more data with less than 2% errors as compared to the MLP. While the results of the MLP network showed that the average relative error for the test data set was 2.0496%, this value was 1.417% for the RBF network. The modeling time is a significant determining element when choosing the optimal technique. The RBF network optimization took longer than 60 minutes, even though all MLP structures were run 100 times in less than 15 minutes. In summary, artificial neural networks are effective instruments for simulating these kinds of processes, and their application can save a lot of time-consuming experimentation. Additionally, the RBF network outperforms the MLP in terms of precision while requiring less processing time. | ||
| کلیدواژه‌ها | ||
| Artificial Neural Network (ANN)؛ Mini-Channel heatsink؛ Multilayer perceptron؛ Radial basis function؛ Simulated annealing | ||
| مراجع | ||
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