Predicting turbulent flow friction coefficient using ANFIS technique. Issue 2 (February 2017)

• Record Type: Journal Article
• Authors: Bardestani, Sara; Givehchi, Mohammad; Younesi, Emran; Sajjadi, Shahin; Shamshirband, Shahaboddin; Petkovic, Dalibor
• Is Part Of: Signal, image and video processing. Volume 11:Issue 2(2017)
• Page Start: 341
• Page End: 347
• Publication Date: 2017-02
• Subjects: Friction coefficient -- Adaptive neuro-fuzzy inference system -- Colebrook's equation -- Pipe relative roughness -- Reynolds number; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-016-0948-8
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract The friction coefficient is widely used for technical and economical design of pipes in irrigation, land drainage, urban sewage systems and intake structures. In the present study, the friction factor in pipes is estimated by using adaptive neuro-fuzzy inference system (ANFIS) and grid partition method. The data derived from the Colebrook's equation were considered for ascertaining the neuro-fuzzy model. Present approach developed an ANFIS technique to predict the friction coefficient as output variable based on pipe relative roughness and Reynold's number as input variables. The performance of the ANFIS model was evaluated against conventional procedures. Correlation coefficient (R2), root mean squared error and mean absolute error were used as comparing statistical indicators for the assessment of the proposed approach's performance. It was found that the adaptive neuro-fuzzy inference system model is more accurate than other empirical equations in modeling friction factor.