Investigating the effect of using aluminum oxide nanofluid and twisted tapes with different twisted pitches on the heat transfer efficiency and using perceptron artificial neural networks in data prediction

This study investigates the effect of aluminum oxide nanofluid with 1% and 2% volume fractions (VFs) in four different tubes with identical bending and the uniform, low-high-low, low-high, and high-low-high longitudinal states with twist ratios of 0, 2 and 4. These tubes are affected by a constant thermal flux of 10000 W/m2 and Reynolds numbers of 2000-10000. For the numerical solution of these equations, the finite difference method on the basis of the control volume and the SIMPLEC algorithm and the K-ε-RNG method were applied for the turbulent flow modeling. The determined Nusselt number from the numerical methods were evaluated applying the perceptron neural network. The most optimal increase in the Nusselt number was in the twisted tube with twist ratio of 4 in the Reynolds number of 10,000 and VF of 1% and 2% nanofluid with increasing trend of length from low to high. The most optimal increase in Nusselt number compared to base fluid was about 25%. The sum of squared error and the relative error data obtained from the hyperbolic tangent function for the training and test data were 0.080-1.331 and 0.113-0.675, and from the sigmoid function for the training and test data were 0.093-0.392 and 0.097-0.147.