Computational fluid dynamics (CFD) and artificial neural network (ANN) are used to examine the cooling performance of two electronic components in an enclosure filled with a Cu-water nanofluid. The heat transfer within the enclosure is due to laminar natural convection between the heated electronic components mounted on the left and right vertical walls with a relatively lower temperature. The results of a CFD simulation are used to train and validate a series of ANN architectures, which are developed to quickly and accurately carry out this analysis. A comparison study between the results from the CFD simulation and the ANN analysis indicates that the ANN accurately predicts the cooling performance of electronic components within the given range of data.
An Artificial Neural Network Approach to Cooling Analysis of Electronic Components in Enclosures Filled With Nanofluids
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Kargar, A., Ghasemi, B., and Aminossadati, S. M. (March 10, 2011). "An Artificial Neural Network Approach to Cooling Analysis of Electronic Components in Enclosures Filled With Nanofluids." ASME. J. Electron. Packag. March 2011; 133(1): 011010. https://doi.org/10.1115/1.4003215
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