Selective assembly for aero-engine fuel nozzle driven by atomization performance prediction

Abstract:

In order to reduce the repeated disassembly and reassembly in aero-engine fuel nozzle assembly and improve the success rate of one assembly, a key part selective assembly method based on atomization performance prediction was proposed. First, based on the historical assembly data of nozzle, the nozzle geometric precision-atomization performance case library was constructed. Next, considering the impact of large fluctuations in sample space size and nozzle geometric accuracy, as well as poor consistency, the sample space was expanded by adaptive comprehensive oversampling method, and simultaneously the continuous attribute was discretized by improved K-means clustering algorithm. Finally, the correlation between geometric accuracy and atomization performance was established by association rule mining algorithm, and the accuracy of each rule was quantified by rule fitness evaluation method. Based on these association rule sets, the nozzle atomization performance prediction model was constructed to guide nozzle assembly. The research results show that the nozzle atomization performance prediction model proposed in this paper has the best prediction effect, with a prediction accuracy of up to 98.33%, compared with methods such as decision tree, support vector machine, and artificial neural network, which can effectively predict the atomization performance of different parts combination, thus reducing invalid assembly and improving the assembly efficiency of nozzle.