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Proposing empirical correlations and optimization of Nu and S<inf>gen</inf> of nanofluids in channels and pbkp_redicting them using artificial neural network

Abstract:

Getting the best performance from a thermal system requires two fundamental analyses, energy and entropy generation. An ideal mechanism has the highest Nu and the lowest entropy Sgen. As part of this research, a large dataset of fluid flow via tubes has been collected experimentally. As well as the inclusion of nanoparticles, analyses are included as well. By using deep learning algorithms, the Nusselt number and total entropy generation are pbkp_redicted. In both models, the mean absolute error was lower than 5%. To determine the most accurate model, hyperparameter tuning is performed. That is adjusting all the settings in the neural network to attain the best results. The results of the pbkp_redictive models are compared against experimental and benchmark results. The study incorporates a massive optimization strategy to fine-tune the pbkp_redictive capabilities of the models. Furthermore, the model's pbkp_redictive abilities are evaluated through the use of the coefficient of determination R2. For water and nanofluids flowing through circular, square, and rectangular cross-sections, the proposed models can pbkp_redict Nu and Sgen. The results showed remarkable agreement with the experimental results. The models showed an MAE of not higher than 1.33%, which is a great achievement. Also, empirical correlations are proposed for both parameters, and double factorial optimization is implemented. The results showed that to achieve the best results, the Re should be higher than 1600, and the nanoparticle concentration should be 3%. A thorough justification of selected cases is presented as well.