Abstract
The main aim of this research is to demonstrate effectiveness of soft computing techniques in thermo-hydraulic behavior modeling of passive heat transfer enhancement (HTE) techniques. An artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), two effective modeling methods, have been used to model Nusselt numbers and friction factors of wire coil and twisted tape inserts in various flow regimes. The experimental data sets were utilized for training and validation of these models, and their results were compared with the corresponding correlations. The mean relative error (MRE) between the predicted results and experimental data of ANN and ANFIS models were found to be less than 3% and 1.5% for thermo-hydraulic behavior modeling of wire coil and twisted tape inserts, respectively. Depending on model complexity, performance of both ANN and ANFIS models was found to be superior to that of the corresponding power-law regressions. Hence, application of the soft computing approach to predict the performance of thermal systems in engineering applications is recommended.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
