The thermal conductivity of nanofluids depends on several factors such as temperature, concentration and temperature. These parameters have the most significant effect on thermal conductivity compared with other factors. In the present study, the accuracy of trained Perceptron neural network with 10 neurons and three input variables including size of nanoparticles, temperature and concentration is evaluated. The sum of squared errors and the correlation coefficient of the trained neural network are equal to 0.99293 and 0.00031, respectively.
Modeling thermal conductivity ratio of CuO/ethylene glycol nanofluid by using artificial neural network / Nazari, M. A.; Ahmadi, M. H.; Lorenzini, G.; Maddah, H.; Alavi, M. F.; Ghasempour, R.. - In: DIFFUSION AND DEFECT DATA, SOLID STATE DATA. PART A, DEFECT AND DIFFUSION FORUM. - ISSN 1012-0386. - 388:(2018), pp. 39-43. [10.4028/www.scientific.net/DDF.388.39]
Modeling thermal conductivity ratio of CuO/ethylene glycol nanofluid by using artificial neural network
Lorenzini G.
;
2018-01-01
Abstract
The thermal conductivity of nanofluids depends on several factors such as temperature, concentration and temperature. These parameters have the most significant effect on thermal conductivity compared with other factors. In the present study, the accuracy of trained Perceptron neural network with 10 neurons and three input variables including size of nanoparticles, temperature and concentration is evaluated. The sum of squared errors and the correlation coefficient of the trained neural network are equal to 0.99293 and 0.00031, respectively.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
