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• W3127949610 endingPage "106863" @default.
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• W3127949610 abstract "Since experimental studies in the field of nanofluid pool boiling requires costly and time-consuming tests, numerical methods such as artificial neural networks with higher predictability and nonlinear features are suitable for prediction and modeling of problem parameters. In this paper, 180 pool boiling laboratory data of Fe 3 O 4 /water nanofluid are employed as datasets used for network training to determine the effect of different parameters of nanofluid pool boiling on Boiling Heat Transfer Coefficient (BHTC) and wall superheat. The concerned input parameters for the neural network include concentration, roughness, and heat flux, while the network outputs are the BHTC and wall superheat. Finally, it becomes clear that the trainbr training algorithm with the optimal quantity of 41 neurons within the hidden layer shows the best performance. In addition, the present model can accurately predict the BHTC and wall superheat with correlation coefficients (R) of 0.99936 and 0.9986 and the mean square error (mse) of 0.103 and 0.013, respectively. Also, given the optimization objectives considered in this research, including maximizing the heat transfer coefficient and minimizing the wall superheat in the nanofluid pool boiling process, the multi-objective genetic algorithm has been used to optimize the two objective functions concerned. •ANN and GA were utilized in order to optimize the BHTC. •The GA was employed to create an interaction between the target functions. •There is an accordance between the target data and network outputs. •The optimal values occurred at 0.1 vol%, a roughness of 0.786μm. •The BHTC on the precipitated surface having low heat flux decreased." @default.
• W3127949610 created "2021-02-15" @default.
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• W3127949610 date "2021-05-01" @default.
• W3127949610 modified "2023-10-17" @default.
• W3127949610 title "Analysis of the effect of roughness and concentration of Fe3O4/water nanofluid on the boiling heat transfer using the artificial neural network: An experimental and numerical study" @default.
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• W3127949610 doi "https://doi.org/10.1016/j.ijthermalsci.2021.106863" @default.
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