Abstract
This study was dedicated to introducing a new method for predicting the Sauter mean diameter (SMD) buildup in the swirl cup airblast fuel injector. There have been considerable difficulties with predicting SMD mainly because of complicated flow characteristics in a spray. Therefore, the backpropagation (BP) neural network-based machine learning was applied for the prediction of SMD as a function of geometry, condition parameters, and axial distance such as primary swirl number, secondary swirl number, venturi angle, mass flow rate of fuel, and relative air pressure. SMD was measured by a phase Doppler particle analyzer (PDPA). The results show that the prediction accuracy of the trained BP neural network was excellent with a coefficient of determination (R(2)) score of 0.9599, root mean square error (RMSE) score of 1.4613, and overall relative error within 20%. Through sensitivity analysis, the relative air pressure drop and primary swirl number were the largest and smallest factors affecting the value of SMD, respectively. Finally, the prediction accuracy of the BP neural network model is far greater than the current prediction correlations. Moreover, for the predicting target in the present study, the BP neural network shows the advantages of a simple structure and short running time compared with PSO-BP and GRNN. All these prove that the BP neural network is a novel and effective way to predict the SMD of droplets generated by a swirl cup airblast fuel injector.