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Showing 2 results for Nanofluid
Maryam Karami, Shahram Delfani, Mohammad Ali Akhavan Bahabadi,
Volume 21, Issue 2 (7-2018)
Abstract
In this study, the efficiency of direct absorption solar collector (DASC) using carbon nanotube nanofluid in the mixture of water and ethylene glycol as the base fluid was experimentally investigated and compared with the efficiency of the flat plate solar collector (FPSC). The results show that the maximum efficiency of the DASC using the base fluid and absorptive bottom surface is about 4.7% and using carbon nanotube nanofluid is about 22% more than that of FPSC. According to the results, the performance of DASC with the application in the domestic solar water heaters, is better than FPSC at the same operating condition.
Daneial Khazaei, Dariush Jafari, Morteza Esfandyari,
Volume 22, Issue 1 (4-2019)
Abstract
In recent years, the use of modeling methods that directly utilize empirical data is increasing due to the high accuracy in predicting the results of the process, rather than statistical methods. In this paper, the ability of Artificial Neural Network (ANN) and Adaptive Fuzzy-Neural Inference System (ANFIS) models in the prediction of the thermal performance of Al2O3 nanofluid that is measured by thermal resistance is investigated. The laboratory data was extracted from a valid paper that examined the thermal performance of Al2O3 nanoparticles in an oscillating tube. For modeling by ANN, a multi-layered perceptron network was used and for ANFIS a sugeno fuzzy model was used that are both the most accurate and most commonly used modeling methods. Comparison of target values with predicted values by both models was highly satisfactory, and the correlation coefficient for both of them was more than 0.99, which indicates that the accuracy of these two models is high. Finally, the performance of both models was compared with each other, which was very good and close, but in general ANN showed better performance than ANFIS.
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