Study of flow field, heat transfer, and entropy generation of nanofluid turbulent natural convection in an enclosure utilizing the computational fluid dynamics‐artificial neural network hybrid method. Issue 4 (28th January 2019)

• Record Type: Journal Article
• Authors: Sepehrnia, Mojtaba; Sheikhzadeh, Ghanbarali; Abaei, Golnoush; Motamedian, Mahdi
• Is Part Of: Heat transfer - Asian research. Volume 48:Issue 4(2019)
• Page Start: 1151
• Page End: 1179
• Publication Date: 2019-01-28
• Subjects: entropy generation -- nanofluid -- natural convection -- radial basis function artificial neural networks (RBF‐ANN) -- tall enclosure -- turbulent flow; Heat -- Transmission -- Periodicals; Electronic journals; 621.4022
• DOI: 10.1002/htj.21425
• Languages: English
• ISSNs: 1099-2871

Abstract

Abstract: In this study, the turbulent natural convection of Ag‐water nanofluid in a tall, inclined enclosure has been investigated. The main objective of this study is finding the optimized angle of the enclosure with operational boundary condition in cooling from ceiling utilizing the computational fluid dynamics‐artificial neural network (CFD‐ANN) hybrid method, which has not been noticed in previous studies. To achieve this, we proposed two approaches. First, the simulations have been done with a deviation angle of 0 to 90° by using water and Ag‐water nanofluid. And second, a new prediction approach is proposed based on radial basis function artificial neural networks (RBF‐ANN) to predict the mean Nusselt number and entropy generation with the variation of Rayleigh numbers, deviation angles, and volume fractions as inputs. The results from the first approach indicate that the Rayleigh number has a considerable function in the determination of optimized angle. The results from the second approach, which used the first approach simulation results as training data set, could predict the mean Nusselt number and entropy generation with 1.4577e −022 and 1.552e −015 mean square error, respectively. Moreover, a new set of data for Rayleigh numbers, deviation angles, and volume fractions were used to test the performance of the prediction model, which shows promising and superior prospects for RBF‐ANN.