Prediction of heat transfer characteristics in a microchannel with vortex generators by machine learning
Küçük Resim Yok
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Walter De Gruyter Gmbh
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Because of the prompt improvements in Micro-Electro-Mechanical Systems, thermal management necessities have altered paying attention to the compactness and high energy consumption of actual electronic devices in industry. In this study, 625 data sets obtained numerically according to the change of five different geometric parameters and Reynolds numbers for delta winglet type vortex generator pairs placed in a microchannel were utilized. Four dissimilar artificial neural network models were established to predict the heat transfer characteristics in a microchannel with innovatively oriented vortex generators in the literature. Friction factor, Nusselt number, and performance evaluation criteria were considered to explore the heat transfer characteristics. Different neuron numbers were determined in the hidden layer of each of the models in which the Levethenberg-Marquardt training algorithm was benefited as the training algorithm. The predicted values were checked against the target data and empirical correlations. The coefficient of determination values calculated for each machine learning model were found to be above 0.99. According to obtained results, the designed artificial neural networks can provide high prediction performance for each data set and have higher prediction accuracy compared to empirical correlations. All data predicted by machine learning models were collected within the range of +/- 3% deviation bands, whereas the majority of the estimated data by empirical correlations dispersed within & PLUSMN;20% ones. For that reason, a full evaluation of the estimation performance of artificial neural networks versus empirical correlations data is enabled to fill a gap in the literature as one of the uncommon works.
Açıklama
Anahtar Kelimeler
artificial neural network, heat transfer enhancement, Levenberg-Marquardt, machine learning, microchannel, vortex generator, Artificial Neural-Network, Thermal-Conductivity, Single-Phase, Rectangular Microchannel, Forced-Convection, Liquid Flow, Nanofluid, Channel, Model, Sink
Kaynak
Kerntechnik
WoS Q Değeri
Q4
Scopus Q Değeri
Cilt
88
Sayı
1
