| Citation: | BO Lixin, LI Ruichao, LIU Yuan, et al. Research on dynamic real-time modeling of aero engine based on ODENet[J]. Journal of Aerospace Power, 2025, 40（4）：20240513 doi: 10.13224/j.cnki.jasp.20240513
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A new dynamic real-time modeling technique for aircraft engines based on Neural Ordinary Differential Equations (ODENet) was proposed. First, the structure of the ODENet model was determined based on the prior knowledge of engine dynamics. Then, an amplitude-modulated pseudo-random binary sequence signal was designed to collect input-output data from the component-level model of the engine, which was used to train the ODENet model, ensuring to cover the dynamic characteristics across the entire operating envelope. Finally, the model was applied to a mixed-flow turbofan engine with a bypass duct injector to validate its real-time performance, accuracy, and robustness through open-loop and closed-loop simulations. The results show that: in the open-loop simulation, the ODENet model’s running speed was approximately 15 times faster than the component-level model, and the fitting degree of each parameter on both the training and testing datasets was no less than 90%; in the closed-loop simulation, the average steady-state and dynamic errors of the output variables between the ODENet and component-level models did not exceed 4%.
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