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The technology of turbulent drag reduction by viscoelastic additives cannot be widely applied in practical engineering due to the difficulty in judging the effect of drag reduction. To solve this problem, the experiment of drag-reducing channel flow of polymer solution was carried out based on the comprehensive analysis of the factors affecting the drag reduction rate. Abundant drag reduction rate data were obtained. A three-layer BP neural network prediction model was established with polymer solution concentration, Reynolds number and injection flow rate as input parameters. Based on the test results, the prediction accuracy on drag reduction rate of the model was analysed. The prediction and model validation of drag reduction rate are carried out further according to the historical data in literature. The results show that the predicted drag reduction rate of BP neural network is close to the real drag reduction rate in the drag-reducing flow of polymer solution. The prediction is with high accuracy and with good generalization ability. It is expected to be applied to practical projects and to promote the development of turbulent drag reduction technology by additives.

Prediction of Drag Reduction Rate in Turbulent Channel Flow Based on BP Neural Network