Development of ANN prediction model for estimation of heat transfer utilizing rectangular-toothed v-cut twisted tape

This work explores the heat transfer performance and friction characteristics of toothed v-cut twisted tapes, while employing an artificial neural network (ANN) as a predictive model. The novelty of this study lies in the innovative use of toothed v-cut twisted tapes to enhance heat transfer performance, coupled with the application of ANN for precise prediction and optimization. Focusing on a specific geometric range by adjusting the depth ratio of rectangular teeth and the width-to-depth ratio of the v-cut, the study investigates turbulent flows with Reynolds numbers spanning from 6000 to 13,000, mirroring real-world applications. The investigations unveil that the introduction of teeth to the v-cut generates a secondary vortex flow, contributing significantly to improved heat transfer by enhancing the Nusselt number ( Nu) and mitigating the reduction in heat transfer rate with increasing depth of cut at higher Reynolds numbers ( Re). The nuanced behavior of the friction factor is revealed, showcasing its inverse proportionality to Re and e/ c, and direct proportionality to b/ c, offering valuable practical insights. Remarkably, the analysis of heat transfer rate variations underscores the ANN model's predictive accuracy. Key findings include the most substantial increase in heat transfer rate for b/ c = 0.67 and e/ c = 0.14, with the ANN model predictions closely aligning with these results. The ANN model, trained on extensive datasets derived from experiments, emerges as a robust predictive tool, demonstrating mean relative errors constrained to less than 3.3% for Nusselt numbers and 0.08% for friction factors. Validation against previously unseen datasets further substantiates its efficacy, with an average percentage error of 3.32% for friction and 0.96% for Nusselt numbers. These results, along with the 97% and 99% accuracy for friction and Nusselt numbers, respectively, position the ANN model as a reliable tool for precision in predicting and optimizing heat transfer dynamics across varied engineering scenarios.