This study presents a neuro-fuzzy inference system for predicting pressure loss in coiled flow inverter (CFI) tubes. Computational fluid dynamics (CFD) simulations were conducted to obtain pressure loss values across nine distinct CFI configurations. The neuro-fuzzy model utilized three key input parameters such as Reynolds number (Re), number of 90° bends (N), and the tube-to-coil diameter ratio (L/D). Following CFD validation, the dataset was partitioned into training (two-thirds) and testing (one-third) subsets. The model achieved an outstanding mean relative error (MRE) of 0.549%, demonstrating its high predictive accuracy and reliability for pressure loss estimation in coiled flow inverter systems. These results highlight the neuro-fuzzy approach as a suitable tool for optimizing CFI designs in industrial applications. This study ultimately demonstrates how the strategic combination of numerical simulation and machine learning can accelerate development cycles while maintaining rigorous accuracy standards, providing engineers with a powerful tool for system design and optimization.
