Comparison of Hydrodynamic Performance of Three Types of Static Mixers Using Computational Fluid Dynamics and Artificial Neural Network

Static mixers are applied for increasing the mixing in chemical reactors as well as for increasing the heat transfer coefficient in heat exchangers. In the study, fluid flow characteristics in tubes equipped with modified static mixers with different geometric parameters were investigated by computational fluid dynamics. The classic twisted tape, perforated twisted tape, and V-Cut twisted tape were evaluated for Reynolds numbers between of 3000 to 19000. The fluid flow and pressure drop for the mixers were investigated. The validated simulation results were employed to train the artificial neural network model. Reynolds number and geometric parameters of the mixers were used as input variables of the neural network for predicting the friction factor. The model accuracy for estimating the friction factor was investigated and a relative error of less than 1% was obtained. The main relative errors for all data in classical, V-Cut, and perforated twisted tape were 0.75%, 0.57%, and 0.52%, respectively, and for validation data were 1.1%, 0.92%, and 0.62%. 30% of the data were randomly selected for the neural network to prove the model validity.