Effect of different pitches on the 3D helically coiled shell and tube heat exchanger filled with a hybrid nanofluid: Numerical study and artificial neural network modeling

Abstract

The effects of using hybrid nanofluids and of helical coil pitch (lambda) in a 3D shell and tube heat exchanger (STHE) are investigated. The algorithm used in this study is Phase Coupled SIMPLE and the method used is Eulerian. Nanofluid flow with Reynolds (Re) numbers of 10,000, 15,000, and 20,000, nanoparticles with volume fractions (phi) of 2 and 4%, and lambda = 20, 25, 40, and 50 mm are investigated. The highest numbers related to the thermal index (Nu) and effectiveness occurred in the lambda = 20 mm and the maximum phi and Re. In the case of lambda = 20 mm, the maximum Nusselt number is 15.8%, 26%, and 45.3% more than that of 25, 40, and 50 mm, respectively. However, in the same case, in comparison between the phi = 4% and phi = 0, the Nu increases by 45.7%, 61.7%, and 76%. The present study shows that combining using hybrid nanofluids and changing the geometry of STHE, as an innovative approach can positively increase efficiency. Finally, the results are used for training an artificial neural network (ANN). In this regard, for finding the optimum neuron numbers in the hidden layer, the optimum feed-forward network is obtained to predict the efficiency of the material.