ANFIS-Based Modeling and Optimization of Heat Transfer in Ultrasonically Assisted Finned-Tube Heat Exchangers with MWCNT Nanofluids

Document Type : Research Paper

Author
Department of Mechanical Engineering, University of Bojnord, Bojnord, Iran
Abstract
This study examines the thermal performance of a finned-tube heat exchanger enhanced with multi-walled carbon nanotube (MWCNT) nanofluids under ultrasonic excitation. An experimental setup is designed to evaluate the effects of inlet temperature, air velocity, nanoparticle concentration, and ultrasonic power on outlet temperature, heat transfer rate, and Nusselt number. An adaptive neuro-fuzzy inference system (ANFIS) is developed to represent the nonlinear behavior of the system. Model parameters are optimized using a genetic algorithm (GA) and particle swarm optimization (PSO). A dataset of 108 experimental samples is used, with 81 samples allocated for training and 27 samples allocated for testing. The predictions show strong agreement with experimental data, with coefficients of determination up to 0.98. The ANFIS–GA model achieves lower prediction errors and more consistent performance than ANFIS–PSO. Sensitivity analysis identifies the inlet temperature, nanoparticle concentration, and ultrasonic power as the dominant parameters, whereas the air velocity has a weaker effect. The results confirm that ultrasonic excitation combined with nanofluids improves heat transfer performance. The proposed ANFIS–GA framework provides an accurate and efficient alternative to extensive experimental testing.
Keywords
Subjects

Publisher’s Note Shahid Chamran University of Ahvaz remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Available Online from 11 June 2026