Accurate Density Prediction of Nanofluids using an Optimized Artificial Neural Network for Enhanced Heat Transfer and Energy Systems

This research presents an Artificial Neural Network (ANN) model for accurate density prediction of nanofluids, showcasing its potential applications in heat transfer and energy systems. The ANN model's performance is rigorously evaluated, and the impact of hyperparameter tuning is thoroughly analysed. The results demonstrate the model's remarkable accuracy, achieving 99.60% for the testing dataset and 99.51% for the training dataset. The absolute percentage error (APE) values are remarkably low, validating the model's precision in predicting density. Comparative analysis between the ANN's predictions and experimental data further confirms its reliability. Hyperparameter tuning significantly enhances the model's accuracy and generalization capabilities. The developed ANN model opens new possibilities for advancing nanofluid research and optimizing heat transfer technologies. Its versatility and potential for broader applications in nanofluid behaviours make it a valuable tool for sustainable engineering practices.