Optimization of Nozzle Structure Based on Backpropagation Neural Network

A large degree of freedom analysis method based on BP neural network model is proposed to address the impact of nozzle structural parameters on atomization performance in the design of handheld air spray guns. Based on the analysis of the internal flow field of the air atomization nozzle, the structural parameters such as the diameter and angle of the fan control hole, the diameter of the auxiliary atomization hole, and the diameter of the central atomization hole are used to obtain the three-dimensional fluid analysis response of the nozzle under various parameter combinations through orthogonal experiments. These parameters are used as training samples for the backpropagation neural network to achieve the neural network expression of the nozzle structure combination; By using evolutionary neural networks to optimize the initial weights and thresholds of the training process, the optimized neural network can accurately predict the three-dimensional fluid analysis response of nozzles in non training sample sets. The research results indicate that this model has practical guidance significance for improving the atomization quality of air atomizing nozzles and optimizing nozzle structure design.