Nowadays, there has been an increasing focus on integrated flight propulsion control and the inlet-exhaust design for the aero-propulsion system. Traditional component-level models are inadequate due to installed performance deviations and mismatches between the real engine and the model, failing to meet the accuracy requirements of supersonic conditions. This paper establishes a quasi-one-dimensional model for the inlet-exhaust system and conducts experimental calibration. Additionally, a mechanism-data fusion adaptive modeling scheme using an Extreme Learning Machine based on the Salp Swarm Algorithm (SSA-ELM) is proposed. The study reveals the inlet model's efficacy in reflecting installed performance, flow matching, and mitigating pressure distortion, while the nozzle model accurately predicts flow coefficients and thrust coefficients, and identifies various operational states. The model's output closely aligns with typical experimental parameters. By combining offline optimization and online adaptive correction, the mechanism-data fusion adaptive model substantially reduces output errors during regular flights and varying levels of degradation, and effectively handles gradual degradation within a single flight cycle. Notably, the mechanism-data fusion adaptive model holistically addresses total pressure errors within the inlet-exhaust system and normal shock location correction. This approach significantly curbs performance deviations in supersonic conditions. For example, at Ma = 2.0, the system error impressively drops from 34.17% to merely 6.54%, while errors for other flight conditions consistently stay below the 2.95% threshold. These findings underscore the clear superiority of the proposed method.

An accurate and reliable turbofan engine model which can describe its dynamic behavior within the full flight envelop and lifecycle plays a critical role in performance optimization, controller design and fault diagnosis. However, due to the performance differences caused by the tolerance of engine manufacturing and assembly, and performance degradation during continuously stringent environmental regulations, the model accuracy is severely reduced. In this paper, an adaptive modification method of turbofan engine nonlinear Component-Llevel Model (CLM) based on Long Short-Term Memory (LSTM) Neural Network (NN) and hybrid optimization algorithm is pro-posed. First, a dynamic compensator with a combined LSTM NN architecture is constructed to compensate for the initial error between the experimental data and CLM of a turbofan engine under health condition. Then, a sensitivity analysis approach based on the entropy coefficient and technique for order preference by similarity to an ideal solution integrated evaluation is developed to choose the unmeasurable health parameters to be adjusted. Finally, a parallel hybrid optimization algorithm is developed to complete the adaptive model modification when the performance degrades. The proposed method is verified on a military low-bypass twin-spool turbofan engine, and the experimental results show the effectiveness of the proposed method.