The internal flow of the serpentine nozzle is complex, with numerous parameters affecting its aerodynamic performance. Traditional component-based zero-dimensional engine models cannot accurately assess the aerodynamic performance impact of the serpentine nozzle on the overall engine. In this paper, a high-fidelity performance prediction model for serpentine nozzles is established using the back propagation (BP) neural network and coupled with a zero-dimensional turbofan engine model. This integrated approach is employed to investigate the influence of the baseline nozzle on engine speed, altitude characteristics, and component behaviors, as well as the differences in nozzle and engine performance under various geometric parameters. The results indicate that, compared to axisymmetric nozzles, engine equipped with the serpentine nozzle experienced a decline in performance. Specifically, at sea-level static conditions, the engine's thrust decreases by 4.50%, while the specific fuel consumption increases by 4.75%. The fan bypass ratio decreased by a maximum of 0.33% at sea level, accompanied by a reduction in surge margin. Conversely, at an altitude of 12 km, the fan bypass ratio increased by a maximum of 0.28%, and the surge margin increased. These varying trends in fan operating characteristics were attributed to the differences in throttling effects of the serpentine nozzle on the core and bypass of mixing chamber at different altitudes. Additionally, increasing the length-to-diameter ratio of the serpentine nozzle from 2.2 to 3 enhanced its performance, resulting in an 8.0% increase in thrust coefficient and a 4.8% increase in discharge coefficient.The multi-dimensional coupling model between serpentine nozzle and engine established in this study can effectively evaluate the changes in engine performance and component characteristics following the installation of serpentine nozzles of varying geometric parameters.
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