The prediction of maximum temperature for single chips' cooling using artificial neural networks
Yükleniyor...
Tarih
2009
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
A CFD simulation usually requires extensive computer storage and lengthy computational time. The application of artificial neural network models to thermal management of chips is still limited. In this study, the main objective is to find a neural network solution for obtaining suitable thickness levels and material for a chip subjected to a constant heat power. To achieve this aim a neural network is trained and tested using the results of the CFD program package Fluent. The back-propagation learning algorithm with three different variants, single layer and logistic sigmoid transfer function is employed in the network. By using the weights of the network, various formulations are designed for the output. The network has resulted in R (2) values of 0.999, and the mean% errors smaller than 0.8 and 0.7 for the training and test data, respectively. The analysis is extended for different thickness and input power values. Comparison of some randomly selected results obtained by the neural network model and the CFD program has yielded a maximum error of 1.8%, mean absolute percentage error of 0.55% and R (2) of 0.99994.
Açıklama
ARCAKLIOGLU, Erol/0000-0001-8073-5207
Anahtar Kelimeler
Kaynak
Heat And Mass Transfer
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
45
Sayı
4
Künye
closedAccess
